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214 Best Big Data Research Topics for Your Thesis Paper

Finding an ideal big data research topic can take you a long time. Big data, IoT, and robotics have evolved. The future generations will be immersed in major technologies that will make work easier. Work that was done by 10 people will now be done by one person or a machine. This is amazing because, in as much as there will be job loss, more jobs will be created. It is a win-win for everyone.

Big data is a major topic that is being embraced globally. Data science and analytics are helping institutions, governments, and the private sector. We will share with you the best big data research topics.

On top of that, we can offer you the best writing tips to ensure you prosper well in your academics. As students in the university, you need to do proper research to get top grades. Hence, you can consult us if in need of research paper writing services.

Big Data Analytics Research Topics for your Research Project

Are you looking for an ideal big data analytics research topic? Once you choose a topic, consult your professor to evaluate whether it is a great topic. This will help you to get good grades.

Which are the best tools and software for big data processing?
Evaluate the security issues that face big data.
An analysis of large-scale data for social networks globally.
The influence of big data storage systems.
The best platforms for big data computing.
The relation between business intelligence and big data analytics.
The importance of semantics and visualization of big data.
Analysis of big data technologies for businesses.
The common methods used for machine learning in big data.
The difference between self-turning and symmetrical spectral clustering.
The importance of information-based clustering.
Evaluate the hierarchical clustering and density-based clustering application.
How is data mining used to analyze transaction data?
The major importance of dependency modeling.
The influence of probabilistic classification in data mining.

Interesting Big Data Analytics Topics

Who said big data had to be boring? Here are some interesting big data analytics topics that you can try. They are based on how some phenomena are done to make the world a better place.

Discuss the privacy issues in big data.
Evaluate the storage systems of scalable in big data.
The best big data processing software and tools.
Data mining tools and techniques are popularly used.
Evaluate the scalable architectures for parallel data processing.
The major natural language processing methods.
Which are the best big data tools and deployment platforms?
The best algorithms for data visualization.
Analyze the anomaly detection in cloud servers
The scrutiny normally done for the recruitment of big data job profiles.
The malicious user detection in big data collection.
Learning long-term dependencies via the Fourier recurrent units.
Nomadic computing for big data analytics.
The elementary estimators for graphical models.
The memory-efficient kernel approximation.

Big Data Latest Research Topics

Do you know the latest research topics at the moment? These 15 topics will help you to dive into interesting research. You may even build on research done by other scholars.

Evaluate the data mining process.
The influence of the various dimension reduction methods and techniques.
The best data classification methods.
The simple linear regression modeling methods.
Evaluate the logistic regression modeling.
What are the commonly used theorems?
The influence of cluster analysis methods in big data.
The importance of smoothing methods analysis in big data.
How is fraud detection done through AI?
Analyze the use of GIS and spatial data.
How important is artificial intelligence in the modern world?
What is agile data science?
Analyze the behavioral analytics process.
Semantic analytics distribution.
How is domain knowledge important in data analysis?

Big Data Debate Topics

If you want to prosper in the field of big data, you need to try even hard topics. These big data debate topics are interesting and will help you to get a better understanding.

The difference between big data analytics and traditional data analytics methods.
Why do you think the organization should think beyond the Hadoop hype?
Does the size of the data matter more than how recent the data is?
Is it true that bigger data are not always better?
The debate of privacy and personalization in maintaining ethics in big data.
The relation between data science and privacy.
Do you think data science is a rebranding of statistics?
Who delivers better results between data scientists and domain experts?
According to your view, is data science dead?
Do you think analytics teams need to be centralized or decentralized?
The best methods to resource an analytics team.
The best business case for investing in analytics.
The societal implications of the use of predictive analytics within Education.
Is there a need for greater control to prevent experimentation on social media users without their consent?
How is the government using big data; for the improvement of public statistics or to control the population?

University Dissertation Topics on Big Data

Are you doing your Masters or Ph.D. and wondering the best dissertation topic or thesis to do? Why not try any of these? They are interesting and based on various phenomena. While doing the research ensure you relate the phenomenon with the current modern society.

The machine learning algorithms are used for fall recognition.
The divergence and convergence of the internet of things.
The reliable data movements using bandwidth provision strategies.
How is big data analytics using artificial neural networks in cloud gaming?
How is Twitter accounts classification done using network-based features?
How is online anomaly detection done in the cloud collaborative environment?
Evaluate the public transportation insights provided by big data.
Evaluate the paradigm for cancer patients using the nursing EHR to predict the outcome.
Discuss the current data lossless compression in the smart grid.
How does online advertising traffic prediction helps in boosting businesses?
How is the hyperspectral classification done using the multiple kernel learning paradigm?
The analysis of large data sets downloaded from websites.
How does social media data help advertising companies globally?
Which are the systems recognizing and enforcing ownership of data records?
The alternate possibilities emerging for edge computing.

The Best Big Data Analysis Research Topics and Essays

There are a lot of issues that are associated with big data. Here are some of the research topics that you can use in your essays. These topics are ideal whether in high school or college.

The various errors and uncertainty in making data decisions.
The application of big data on tourism.
The automation innovation with big data or related technology
The business models of big data ecosystems.
Privacy awareness in the era of big data and machine learning.
The data privacy for big automotive data.
How is traffic managed in defined data center networks?
Big data analytics for fault detection.
The need for machine learning with big data.
The innovative big data processing used in health care institutions.
The money normalization and extraction from texts.
How is text categorization done in AI?
The opportunistic development of data-driven interactive applications.
The use of data science and big data towards personalized medicine.
The programming and optimization of big data applications.

The Latest Big Data Research Topics for your Research Proposal

Doing a research proposal can be hard at first unless you choose an ideal topic. If you are just diving into the big data field, you can use any of these topics to get a deeper understanding.

The data-centric network of things.
Big data management using artificial intelligence supply chain.
The big data analytics for maintenance.
The high confidence network predictions for big biological data.
The performance optimization techniques and tools for data-intensive computation platforms.
The predictive modeling in the legal context.
Analysis of large data sets in life sciences.
How to understand the mobility and transport modal disparities sing emerging data sources?
How do you think data analytics can support asset management decisions?
An analysis of travel patterns for cellular network data.
The data-driven strategic planning for citywide building retrofitting.
How is money normalization done in data analytics?
Major techniques used in data mining.
The big data adaptation and analytics of cloud computing.
The predictive data maintenance for fault diagnosis.

Interesting Research Topics on A/B Testing In Big Data

A/B testing topics are different from the normal big data topics. However, you use an almost similar methodology to find the reasons behind the issues. These topics are interesting and will help you to get a deeper understanding.

How is ultra-targeted marketing done?
The transition of A/B testing from digital to offline.
How can big data and A/B testing be done to win an election?
Evaluate the use of A/B testing on big data
Evaluate A/B testing as a randomized control experiment.
How does A/B testing work?
The mistakes to avoid while conducting the A/B testing.
The most ideal time to use A/B testing.
The best way to interpret results for an A/B test.
The major principles of A/B tests.
Evaluate the cluster randomization in big data
The best way to analyze A/B test results and the statistical significance.
How is A/B testing used in boosting businesses?
The importance of data analysis in conversion research
The importance of A/B testing in data science.

Amazing Research Topics on Big Data and Local Governments

Governments are now using big data to make the lives of the citizens better. This is in the government and the various institutions. They are based on real-life experiences and making the world better.

Assess the benefits and barriers of big data in the public sector.
The best approach to smart city data ecosystems.
The big analytics used for policymaking.
Evaluate the smart technology and emergence algorithm bureaucracy.
Evaluate the use of citizen scoring in public services.
An analysis of the government administrative data globally.
The public values are found in the era of big data.
Public engagement on local government data use.
Data analytics use in policymaking.
How are algorithms used in public sector decision-making?
The democratic governance in the big data era.
The best business model innovation to be used in sustainable organizations.
How does the government use the collected data from various sources?
The role of big data for smart cities.
How does big data play a role in policymaking?

Easy Research Topics on Big Data

Who said big data topics had to be hard? Here are some of the easiest research topics. They are based on data management, research, and data retention. Pick one and try it!

Who uses big data analytics?
Evaluate structure machine learning.
Explain the whole deep learning process.
Which are the best ways to manage platforms for enterprise analytics?
Which are the new technologies used in data management?
What is the importance of data retention?
The best way to work with images is when doing research.
The best way to promote research outreach is through data management.
The best way to source and manage external data.
Does machine learning improve the quality of data?
Describe the security technologies that can be used in data protection.
Evaluate token-based authentication and its importance.
How can poor data security lead to the loss of information?
How to determine secure data.
What is the importance of centralized key management?

Unique IoT and Big Data Research Topics

Internet of Things has evolved and many devices are now using it. There are smart devices, smart cities, smart locks, and much more. Things can now be controlled by the touch of a button.

Evaluate the 5G networks and IoT.
Analyze the use of Artificial intelligence in the modern world.
How do ultra-power IoT technologies work?
Evaluate the adaptive systems and models at runtime.
How have smart cities and smart environments improved the living space?
The importance of the IoT-based supply chains.
How does smart agriculture influence water management?
The internet applications naming and identifiers.
How does the smart grid influence energy management?
Which are the best design principles for IoT application development?
The best human-device interactions for the Internet of Things.
The relation between urban dynamics and crowdsourcing services.
The best wireless sensor network for IoT security.
The best intrusion detection in IoT.
The importance of big data on the Internet of Things.

Big Data Database Research Topics You Should Try

Big data is broad and interesting. These big data database research topics will put you in a better place in your research. You also get to evaluate the roles of various phenomena.

The best cloud computing platforms for big data analytics.
The parallel programming techniques for big data processing.
The importance of big data models and algorithms in research.
Evaluate the role of big data analytics for smart healthcare.
How is big data analytics used in business intelligence?
The best machine learning methods for big data.
Evaluate the Hadoop programming in big data analytics.
What is privacy-preserving to big data analytics?
The best tools for massive big data processing
IoT deployment in Governments and Internet service providers.
How will IoT be used for future internet architectures?
How does big data close the gap between research and implementation?
What are the cross-layer attacks in IoT?
The influence of big data and smart city planning in society.
Why do you think user access control is important?

Big Data Scala Research Topics

Scala is a programming language that is used in data management. It is closely related to other data programming languages. Here are some of the best scala questions that you can research.

Which are the most used languages in big data?
How is scala used in big data research?
Is scala better than Java in big data?
How is scala a concise programming language?
How does the scala language stream process in real-time?
Which are the various libraries for data science and data analysis?
How does scala allow imperative programming in data collection?
Evaluate how scala includes a useful REPL for interaction.
Evaluate scala’s IDE support.
The data catalog reference model.
Evaluate the basics of data management and its influence on research.
Discuss the behavioral analytics process.
What can you term as the experience economy?
The difference between agile data science and scala language.
Explain the graph analytics process.

Independent Research Topics for Big Data

These independent research topics for big data are based on the various technologies and how they are related. Big data will greatly be important for modern society.

The biggest investment is in big data analysis.
How are multi-cloud and hybrid settings deep roots?
Why do you think machine learning will be in focus for a long while?
Discuss in-memory computing.
What is the difference between edge computing and in-memory computing?
The relation between the Internet of things and big data.
How will digital transformation make the world a better place?
How does data analysis help in social network optimization?
How will complex big data be essential for future enterprises?
Compare the various big data frameworks.
The best way to gather and monitor traffic information using the CCTV images
Evaluate the hierarchical structure of groups and clusters in the decision tree.
Which are the 3D mapping techniques for live streaming data.
How does machine learning help to improve data analysis?
Evaluate DataStream management in task allocation.
How is big data provisioned through edge computing?
The model-based clustering of texts.
The best ways to manage big data.
The use of machine learning in big data.

Is Your Big Data Thesis Giving You Problems?

These are some of the best topics that you can use to prosper in your studies. Not only are they easy to research but also reflect on real-time issues. Whether in University or college, you need to put enough effort into your studies to prosper. However, if you have time constraints, we can provide professional writing help. Are you looking for online expert writers? Look no further, we will provide quality work at a cheap price.

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Trendy Big Data Research Topics

Students that want to focus on emerging issues when writing academic papers and essays should choose trendy data science topics. Big data covers the initiatives and technologies that tackle massive and diverse data when it comes to addressing traditional skills, technologies, and infrastructure efficiently. Here are some of the latest data topics to consider when writing a research paper or essay.

Tools and software for processing big data
Privacy and security issues that face big data
Scalable architectures for processing massively parallel data
Analyzing large scale data for social networks
Scalable big data storage systems
Platforms for big data computing- Big data analytics and adoption
How to analyze big data
How to effectively manage big data
Parallel big data programming and processing techniques
Semantics in big data
Visualization of big data
Business intelligence and big data analytics
Map-reduce architecture and Hadoop programming
Methods for machine learning in big data
Big data analytics and privacy preservation
How to process stream data in big data
Uncertainty in big data management
Anomaly detection in large scale data systems
Analytics for big data in the Smart Healthcare systems
The importance of big data technologies for modern businesses

These are great data research topics that learners at different study levels should consider when asked to write academic papers or essays. However, extensive research is required to come up with great write-ups on these topics.

Data Mining Research Topics for Students

Data mining refers to the extraction of useful information from raw data. It’s a technique that companies apply to accomplish tasks like prediction analysis, generation of the association rule, and clustering. Data mining topics can explain this technique or address issues that are associated with it. Here are some of the best data mining project topics that learners can consider.

Big data mining techniques and tools
Model-based clustering of texts
Describe the concept of data spectroscopic clustering
Parallel spectral clustering within a distributed system
Describe asymmetrical spectral clustering
What is information-based clustering?
Self-turning spectral clustering
Symmetrical spectral clustering
Discuss the K-Means algorithms in data clustering
Discuss the package of MATLAB spectral clustering
Discuss the K-Means clustering from an online spherical perspective
Discuss the hierarchical clustering application
Explain the importance of probabilistic classification in data mining
How can the effectiveness of nonlinear and linear regression analysis be improved?
Explain the Association Rule Learning regarding data mining
Explain the performance of dependency modeling
Discuss the performance of representative-based clustering
Explain the need for density-based clustering
Discuss the importance of subject-based data mining when it comes to reducing terrorism
How can data mining be used to analyze transaction data in a supermarket?

Most data mining current research topics focus on finding or establishing patterns. Students can even find some of the best data mining case study topics in this category. Nevertheless, every idea requires detailed and extensive research to come up with facts that make a great paper or essay.

Big Data Analysis Topics

The moderns IT industry depends on data analytics as its lifeline. Big data is one of the techniques and technologies that are used to analyze vast data volumes. The industry is using data analytics as a strategy for gaining insights into system performance and customer behavior. Here are some of the best data analytics research topics that students can consider when writing academic papers.

Internet of Things
Describe the importance of augmented reality
How important is artificial intelligence?
Explain the graph analytics process
What is agile data science?
Why is machine intelligence for modern businesses?
What is hyper-personalization?
Explain the behavioral analytics process
What is the experience economy?
Discuss journey sciences
Discuss knowledge validation and extraction
What is semantic data management?
Explain the deep learning process
Explain software engineering for big data science
What is structured machine learning?
Explain semantic question answering
What is distributed semantic analytics?
Why is domain knowledge important in data analysis?
Why is data exploration important in data analysis?
Who uses big data analytics?

Writing about data analytics topics requires background knowledge of the issues being discussed. That’s because the analysis entails harnessing data and extracting its value.

Data Management Project Topics

This category has some of the best data science research topics. The enormous amount of data that modern organizations have to deal with every day is not easy to handle. As such, its effective management is required to ensure its effective use. Here are some of the best topics that students can write about in this aspect.

Describe some of the most innovative bid data management concepts
Data catalogs: Describe approaches and their implementation, as well as, adoption
How to manage platforms for enterprise analytics
Discuss the impact of data quality on a business
Explain the best data management strategies for modern enterprises
New technologies and AI in data management
What is data retention and why is it important?
Describe the basics of data management
Explain the application of data management basics
Data publishing and access by modern companies
Explain the process of analyzing and managing data for reproducible research
Explain how to work with images during research
How can an organization ensure secure and confidential handling and management of data?
How to promote research and scientific outreach through data management
How to source and manage external data
How to ensure effective data protection through proper management
Data catalog reference model and market study
What is data valuation and why does it matter in data management?
How can machine learning improve the data quality?
How can a company implement data governance?

This category also has some of the best big data seminar topics. That’s because some of the ideas featured in this section are about issues that affect almost every organization.

Resent Data Security Topics for Research

Big data that comes from different computers and devices require security. That’s because such data is vulnerable to different cyber threats. Some of the best research topics in this category include the following.

How changing data from Terabytes to Petabytes affects its security
What are the major vulnerabilities for big data?
Why big data owners should update security measures regularly
How can poor data security lead to loss of important information
Describe security technologies that can be used to protect big data
Explain how Hadoop integrates with modern security tools
Which are the best encryption tools for protecting transit data?
Explain how data encryption tools work
What is token-based authentication?
Explain how intrusion prevention and detection systems work
What are the most effective physical systems for securing data?
Which is the best intrusion detection system?
Describe the most suitable key management system when it comes to processing massive data
Which tool or algorithm can be used for data owner and user’s authentication?
Explain how you can determine the amount of secure data
How to identify a legit data user
How to prevent illegitimate data access
How to implement attribute-access or role-based access control
Explain the importance of centralized key management
Why is user-access control important?

Any topic in this category can be used to write a brilliant paper or essay that will earn the learner the top grade. However, time and efforts are required to work on these ideas.

Whether students opt to write about data visualization topics or data structure research topics, the most important thing is to choose ideas they like and find interesting. What’s more, learners should pick topics they can find adequate information for online. That way, they will find the research and writing process enjoyable. They can also buy dissertations or any other academic papers that will impress educators to award them the top grades.

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170 Excellent Big Data Research Topics

Table of Contents

If you are a computer science student, then for your final year research assignment, you can very well consider a trending topic related to big data. In case, you are unsure what topic to choose and how to compose a detailed big data research paper, continue reading this blog. For your better comprehension, here we have suggested some key tips for big data research paper topic selection and writing. In addition to that, we have also presented a list of the best 150+ big data research topics and ideas for you to focus on.

Let’s get started to know about big data research paper preparation.

Big Data Research Paper Topic Selection

Perhaps, the students might focus on emerging problems while writing their research papers and thereby select trendy big data topics. Moreover, big data envelop the technologies and efforts that manage extensive data to manage technologies or infrastructures effectively.

Firstly, ensure selecting topics on which you have practical knowledge because theoretical knowledge alone might not meet your goals.
Secondly, carefully listen to your professor’s guidelines and seek their approval before finalizing your topic. In this way, you might obtain clarity before beginning to write your dissertations or essays.
Thirdly, avoid choosing research topics that don’t interest you, because you might need to manage them till the end.

Big Data Research Paper Writing

Perhaps, you might create an exemplary outline, mostly if you don’t have an idea of starting your research paper.
Moreover, after selecting topics of your interest, you ought to establish the writing objective of your paper.
Subsequently, make a list of key ideas, mostly if you have selected complicated big data research topics.
Furthermore, you might consider organizing and optimizing your ideas, hence you need to strictly focus on your content preparation.
Finally, explain the main points and consider reviewing and revising your content to ensure a logical transition.

List of the Best Big Data Research Topics

The following are some outstanding big data research questions that will help you in composing a top-score-fetching academic paper. When you run short of topic ideas for your big data thesis, feel free to explore the list presented below and spot a perfect topic that is convenient for you to research and write about.

Basic Big Data Research Topics

Big Data- Mining tools and techniques.
What is information-driven clustering?
Explain asymmetrical spectral clustering.
Internet of Things.
Describe the relevance of Artificial Intelligence.
What is the graph analytics process?
Significance of machine learning for modern business.
Agile data science and its relevance.
Explain the significance of augmented reality.
Big data analytics and its users.
Define distributed semantic analytics.
Explain semantic question answering.
Define structured machine learning.
The deep learning process and its relevance.
What is journey sciences?
Describe some futuristic applications of Big Data in GPS navigation
Discuss the methods, challenges, and future directions of the synergies of machine learning and data management
Access Control Models and Technologies for Big Data Processing and Management
Discuss the Foundation, Frontiers, and Applications of GNNs (Graph Neural Networks)
Big data and AI for therapeutics and genomics – Proceedings of the 19th Annual Meeting of the MidSouth Computational Biology and Bioinformatics Society

Easy Big Data Research Paper Topics

Relevance of data exploration in data analysis.
Data management and its basics.
Managing and sourcing external data.
Market study and the data catalog reference model.
Access to data publications in modern companies.
Managing images during research.
Explain the relevance of data encryption tools.
Token-based authentication and its significance.
Current trends in big data technologies
Use of big data analytics to power AI (artificial intelligence) and ML (machine learning) automation
Why the concepts of big data analytics are important in the field of business?
What are the key technologies used in big data analytics?
How to turn big data into great success?
How to handle real-time video analytics in the distributed cloud?
How to handle uncertainty in big data processing?

Read more: 150 Technology Research Topics for Writing Top-Notch Assignments

High-Quality Big Data Research Topics

Lightweight Big Data Analytics in terms of the service
How to handle data as well as model drift for real-world applications?
Scalable architectures for processing massively parallel data
Tools and software for processing big data
Privacy and security issues that face big data
Platforms for big data computing- Big data analytics and adoption
Parallel big data programming and processing techniques
Big data and its major vulnerabilities.
Discuss the ways to identify a legitimate data user.
Explain the significance of user-access control.
Describe the significance of centralized key management.
Identify strategies to avoid illegal data access.
Intrusion-detection system- Which is the best?
How does machine learning enhance data quality?
List the security technologies used for big data protection.
Modern security tools and Hadoop integration.
Data governance and its implementation by companies.
Analyze the need for big data owners to regularly update security measures.
Poor data security and the loss of important information.
Explain the most innovative big data management concepts.

Unique Big Data Research Questions

What is data retention and explain its relevance?
Artificial Intelligence and new technologies in data management.
Enterprise analytics- How to manage platforms?
Promotion of research and scientific outreach- Data management
Development of Data Fabric technology
Vector Similarity Search and its evolution
A functional view of big data ecosystem
How can big data develop organizational operations and enhance its competitive advantage in the current competitive market?
Describe scalability for scalable architectures that can be used for parallel data processing.
How to process stream data in big data?
Map-reduce architecture and Hadoop programming
Business intelligence and big data analytics
Uncertainty in big data management
How to source and manage external data?
Reproducible research- Data management and analysis.

Good-Scoring Big Data Research Topics

Experience economy and its relevance.
What is the behavioral analytic process?
Importance of data exploration in data analysis.
Relevance of knowledge validation and extraction.
Explain hyper-personalization.
Applying data management basics.
Big data science and software engineering.
Analyzing transaction data in supermarkets- Role of data mining.
Define agile data science.
Explain the influence of data quality on a business.
The impact of data transformation from Terabytes to Petabytes on security.
Massive data processing and the most appropriate key management system.
Transit data protection- Identify the best encryption tools.
Explore the relevance of data valuation in data management.

Outstanding Big Data Research Ideas

Describe data catalog approaches, implementations, and adoption.
Analyze the relevance of density-based clustering.
Explain the effectiveness of representative-based clustering.
Explore the efficiency of dependency modeling.
What is a NoSQL Database?
Comparative study between NoSQL Database, Apache Hadoop, and NewSQL
Difference between Hadoop and MongoDB
Discuss the types of data governance
Semantic approach to big data governance
Which tool or algorithm can be used for data owner and user authentication?
How to promote research and scientific outreach through data management?
Analyze scalable big data storage systems.
Parallel big data- Programming and processing techniques.
Explain big data visualization.
Smart Healthcare systems- Big data analytics.

Captivating Big Data Research Topics

Big data computing platforms- Big data insights and adoption.
Large-scale data system and anomaly detection.
Data streaming and big data- What is the process?
Analyze the uncertainties in big data management.
Elaborate on the hierarchical clustering application.
Self-turning spectral clustering.
MATLAB spectral clustering- Discuss the package.
Association Rule Learning and data mining.
K-Means clustering and an online spherical approach.
Probabilistic classification in data mining- Explain its relevance.
Attribute-access control and role-based access control- Explain its implementation process.
Identify a tool or algorithm used for data owners and user authentication.
Analyze the relevance of subject-oriented data mining in minimizing terrorism.
Relevance of efficient physical systems for data security.
What is the Hadoop Ecosystem?

Trending Big Data Research Ideas

Lightweight big data analytics- Explain its relevance as a service.
Big data analytics and its impacts on marketing strategy
Application of big data analytics to device efficient business policies and marketing strategies
Impacts of Big Data Analytics on business decision-making
Application of big data to recognize employee’s perceptions of the organization
Application of big data to improve HR practices
Use of big data to understand consumers’ buying behavior
Application of big data to prepare an efficient training plan
Application of big data to recognize future demand and forecasting
Spark clusters and their automated deployment.
Application of big data to recognize supply conditions in the market
Application of big data to improve supply chain management of organization
MapReduce architect for big data
Discuss the most useful big data tools
5Vs of Big Data

Latest Research Topics on Big Data

What role does probabilistic categorization play in data mining?
How can the linear and nonlinear regression analyses’ efficacy be increased?
The Association Rule, please. Finding out about data mining
Describe some of the most cutting-edge ideas in bid data management.
Data registries Describe the approaches, their application, and acceptance.
Describe how dependency modeling performs.
Talk about how well representative-based clustering performs.
How can a company make sure that data is handled and managed safely and privately?
How to use data management to advance scientific outreach and research
Describe the best key management system for processing large amounts of data.
Discuss the advantages and disadvantages of using Hybrid Clouds
Develop a comparative analysis of Dark data and Data fabric including their application
Describe the new trends in data science innovation including their usefulness for business organizations
Analysis of the value big data provides to innovation management
Briefly describe how big data analytics can help foster new services and new product development

Wrapping Up

Out of the 150+ ideas recommended here, choose any big data research paper topic that matches your interest and then begin writing your assignment . In case, you need any other trending big data topics for your project or if you need an expert to offer you help with big data research paper topic selection, writing, and editing, call us immediately. As per your needs, the skilled professionals on our platform will offer cheap and the best big data assignment help online. Even from our academic writers, you can also receive database management assignment help at a nominal price. Note that, the solutions that our specialists prepare and dispatch will be plagiarism-free, accurate, and error-free.

Without any dilemma, quickly avail of our big data research paper writing help service online to get your work done on time and boost your overall grades.

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37 Research Topics In Data Science To Stay On Top Of

February 22, 2024

As a data scientist, staying on top of the latest research in your field is essential.

The data science landscape changes rapidly, and new techniques and tools are constantly being developed.

To keep up with the competition, you need to be aware of the latest trends and topics in data science research.

In this article, we will provide an overview of 37 hot research topics in data science.

We will discuss each topic in detail, including its significance and potential applications.

These topics could be an idea for a thesis or simply topics you can research independently.

Stay tuned – this is one blog post you don’t want to miss!

37 Research Topics in Data Science

1.) predictive modeling.

Predictive modeling is a significant portion of data science and a topic you must be aware of.

Simply put, it is the process of using historical data to build models that can predict future outcomes.

Predictive modeling has many applications, from marketing and sales to financial forecasting and risk management.

As businesses increasingly rely on data to make decisions, predictive modeling is becoming more and more important.

While it can be complex, predictive modeling is a powerful tool that gives businesses a competitive advantage.

2.) Big Data Analytics

These days, it seems like everyone is talking about big data.

And with good reason – organizations of all sizes are sitting on mountains of data, and they’re increasingly turning to data scientists to help them make sense of it all.

But what exactly is big data? And what does it mean for data science?

Simply put, big data is a term used to describe datasets that are too large and complex for traditional data processing techniques.

Big data typically refers to datasets of a few terabytes or more.

But size isn’t the only defining characteristic – big data is also characterized by its high Velocity (the speed at which data is generated), Variety (the different types of data), and Volume (the amount of the information).

Given the enormity of big data, it’s not surprising that organizations are struggling to make sense of it all.

That’s where data science comes in.

Data scientists use various methods to wrangle big data, including distributed computing and other decentralized technologies.

With the help of data science, organizations are beginning to unlock the hidden value in their big data.

By harnessing the power of big data analytics, they can improve their decision-making, better understand their customers, and develop new products and services.

3.) Auto Machine Learning

Auto machine learning is a research topic in data science concerned with developing algorithms that can automatically learn from data without intervention.

This area of research is vital because it allows data scientists to automate the process of writing code for every dataset.

This allows us to focus on other tasks, such as model selection and validation.

Auto machine learning algorithms can learn from data in a hands-off way for the data scientist – while still providing incredible insights.

This makes them a valuable tool for data scientists who either don’t have the skills to do their own analysis or are struggling.

4.) Text Mining

Text mining is a research topic in data science that deals with text data extraction.

This area of research is important because it allows us to get as much information as possible from the vast amount of text data available today.

Text mining techniques can extract information from text data, such as keywords, sentiments, and relationships.

This information can be used for various purposes, such as model building and predictive analytics.

5.) Natural Language Processing

Natural language processing is a data science research topic that analyzes human language data.

This area of research is important because it allows us to understand and make sense of the vast amount of text data available today.

Natural language processing techniques can build predictive and interactive models from any language data.

Natural Language processing is pretty broad, and recent advances like GPT-3 have pushed this topic to the forefront.

6.) Recommender Systems

Recommender systems are an exciting topic in data science because they allow us to make better products, services, and content recommendations.

Businesses can better understand their customers and their needs by using recommender systems.

This, in turn, allows them to develop better products and services that meet the needs of their customers.

Recommender systems are also used to recommend content to users.

This can be done on an individual level or at a group level.

Think about Netflix, for example, always knowing what you want to watch!

Recommender systems are a valuable tool for businesses and users alike.

7.) Deep Learning

Deep learning is a research topic in data science that deals with artificial neural networks.

These networks are composed of multiple layers, and each layer is formed from various nodes.

Deep learning networks can learn from data similarly to how humans learn, irrespective of the data distribution.

This makes them a valuable tool for data scientists looking to build models that can learn from data independently.

The deep learning network has become very popular in recent years because of its ability to achieve state-of-the-art results on various tasks.

There seems to be a new SOTA deep learning algorithm research paper on https://arxiv.org/ every single day!

8.) Reinforcement Learning

Reinforcement learning is a research topic in data science that deals with algorithms that can learn on multiple levels from interactions with their environment.

This area of research is essential because it allows us to develop algorithms that can learn non-greedy approaches to decision-making, allowing businesses and companies to win in the long term compared to the short.

9.) Data Visualization

Data visualization is an excellent research topic in data science because it allows us to see our data in a way that is easy to understand.

Data visualization techniques can be used to create charts, graphs, and other visual representations of data.

This allows us to see the patterns and trends hidden in our data.

Data visualization is also used to communicate results to others.

This allows us to share our findings with others in a way that is easy to understand.

There are many ways to contribute to and learn about data visualization.

Some ways include attending conferences, reading papers, and contributing to open-source projects.

10.) Predictive Maintenance

Predictive maintenance is a hot topic in data science because it allows us to prevent failures before they happen.

This is done using data analytics to predict when a failure will occur.

This allows us to take corrective action before the failure actually happens.

While this sounds simple, avoiding false positives while keeping recall is challenging and an area wide open for advancement.

11.) Financial Analysis

Financial analysis is an older topic that has been around for a while but is still a great field where contributions can be felt.

Current researchers are focused on analyzing macroeconomic data to make better financial decisions.

This is done by analyzing the data to identify trends and patterns.

Financial analysts can use this information to make informed decisions about where to invest their money.

Financial analysis is also used to predict future economic trends.

This allows businesses and individuals to prepare for potential financial hardships and enable companies to be cash-heavy during good economic conditions.

Overall, financial analysis is a valuable tool for anyone looking to make better financial decisions.

12.) Image Recognition

Image recognition is one of the hottest topics in data science because it allows us to identify objects in images.

This is done using artificial intelligence algorithms that can learn from data and understand what objects you’re looking for.

This allows us to build models that can accurately recognize objects in images and video.

This is a valuable tool for businesses and individuals who want to be able to identify objects in images.

Think about security, identification, routing, traffic, etc.

Image Recognition has gained a ton of momentum recently – for a good reason.

13.) Fraud Detection

Fraud detection is a great topic in data science because it allows us to identify fraudulent activity before it happens.

This is done by analyzing data to look for patterns and trends that may be associated with the fraud.

Once our machine learning model recognizes some of these patterns in real time, it immediately detects fraud.

This allows us to take corrective action before the fraud actually happens.

Fraud detection is a valuable tool for anyone who wants to protect themselves from potential fraudulent activity.

14.) Web Scraping

Web scraping is a controversial topic in data science because it allows us to collect data from the web, which is usually data you do not own.

This is done by extracting data from websites using scraping tools that are usually custom-programmed.

This allows us to collect data that would otherwise be inaccessible.

For obvious reasons, web scraping is a unique tool – giving you data your competitors would have no chance of getting.

I think there is an excellent opportunity to create new and innovative ways to make scraping accessible for everyone, not just those who understand Selenium and Beautiful Soup.

15.) Social Media Analysis

Social media analysis is not new; many people have already created exciting and innovative algorithms to study this.

However, it is still a great data science research topic because it allows us to understand how people interact on social media.

This is done by analyzing data from social media platforms to look for insights, bots, and recent societal trends.

Once we understand these practices, we can use this information to improve our marketing efforts.

For example, if we know that a particular demographic prefers a specific type of content, we can create more content that appeals to them.

Social media analysis is also used to understand how people interact with brands on social media.

This allows businesses to understand better what their customers want and need.

Overall, social media analysis is valuable for anyone who wants to improve their marketing efforts or understand how customers interact with brands.

16.) GPU Computing

GPU computing is a fun new research topic in data science because it allows us to process data much faster than traditional CPUs .

Due to how GPUs are made, they’re incredibly proficient at intense matrix operations, outperforming traditional CPUs by very high margins.

While the computation is fast, the coding is still tricky.

There is an excellent research opportunity to bring these innovations to non-traditional modules, allowing data science to take advantage of GPU computing outside of deep learning.

17.) Quantum Computing

Quantum computing is a new research topic in data science and physics because it allows us to process data much faster than traditional computers.

It also opens the door to new types of data.

There are just some problems that can’t be solved utilizing outside of the classical computer.

For example, if you wanted to understand how a single atom moved around, a classical computer couldn’t handle this problem.

You’ll need to utilize a quantum computer to handle quantum mechanics problems.

This may be the “hottest” research topic on the planet right now, with some of the top researchers in computer science and physics worldwide working on it.

You could be too.

18.) Genomics

Genomics may be the only research topic that can compete with quantum computing regarding the “number of top researchers working on it.”

Genomics is a fantastic intersection of data science because it allows us to understand how genes work.

This is done by sequencing the DNA of different organisms to look for insights into our and other species.

Once we understand these patterns, we can use this information to improve our understanding of diseases and create new and innovative treatments for them.

Genomics is also used to study the evolution of different species.

Genomics is the future and a field begging for new and exciting research professionals to take it to the next step.

19.) Location-based services

Location-based services are an old and time-tested research topic in data science.

Since GPS and 4g cell phone reception became a thing, we’ve been trying to stay informed about how humans interact with their environment.

This is done by analyzing data from GPS tracking devices, cell phone towers, and Wi-Fi routers to look for insights into how humans interact.

Once we understand these practices, we can use this information to improve our geotargeting efforts, improve maps, find faster routes, and improve cohesion throughout a community.

Location-based services are used to understand the user, something every business could always use a little bit more of.

While a seemingly “stale” field, location-based services have seen a revival period with self-driving cars.

20.) Smart City Applications

Smart city applications are all the rage in data science research right now.

By harnessing the power of data, cities can become more efficient and sustainable.

But what exactly are smart city applications?

In short, they are systems that use data to improve city infrastructure and services.

This can include anything from traffic management and energy use to waste management and public safety.

Data is collected from various sources, including sensors, cameras, and social media.

It is then analyzed to identify tendencies and habits.

This information can make predictions about future needs and optimize city resources.

As more and more cities strive to become “smart,” the demand for data scientists with expertise in smart city applications is only growing.

21.) Internet Of Things (IoT)

The Internet of Things, or IoT, is exciting and new data science and sustainability research topic.

IoT is a network of physical objects embedded with sensors and connected to the internet.

These objects can include everything from alarm clocks to refrigerators; they’re all connected to the internet.

That means that they can share data with computers.

And that’s where data science comes in.

Data scientists are using IoT data to learn everything from how people use energy to how traffic flows through a city.

They’re also using IoT data to predict when an appliance will break down or when a road will be congested.

Really, the possibilities are endless.

With such a wide-open field, it’s easy to see why IoT is being researched by some of the top professionals in the world.

22.) Cybersecurity

Cybersecurity is a relatively new research topic in data science and in general, but it’s already garnering a lot of attention from businesses and organizations.

After all, with the increasing number of cyber attacks in recent years, it’s clear that we need to find better ways to protect our data.

While most of cybersecurity focuses on infrastructure, data scientists can leverage historical events to find potential exploits to protect their companies.

Sometimes, looking at a problem from a different angle helps, and that’s what data science brings to cybersecurity.

Also, data science can help to develop new security technologies and protocols.

As a result, cybersecurity is a crucial data science research area and one that will only become more important in the years to come.

23.) Blockchain

Blockchain is an incredible new research topic in data science for several reasons.

First, it is a distributed database technology that enables secure, transparent, and tamper-proof transactions.

Did someone say transmitting data?

This makes it an ideal platform for tracking data and transactions in various industries.

Second, blockchain is powered by cryptography, which not only makes it highly secure – but is a familiar foe for data scientists.

Finally, blockchain is still in its early stages of development, so there is much room for research and innovation.

As a result, blockchain is a great new research topic in data science that vows to revolutionize how we store, transmit and manage data.

24.) Sustainability

Sustainability is a relatively new research topic in data science, but it is gaining traction quickly.

To keep up with this demand, The Wharton School of the University of Pennsylvania has started to offer an MBA in Sustainability .

This demand isn’t shocking, and some of the reasons include the following:

Sustainability is an important issue that is relevant to everyone.

Datasets on sustainability are constantly growing and changing, making it an exciting challenge for data scientists.

There hasn’t been a “set way” to approach sustainability from a data perspective, making it an excellent opportunity for interdisciplinary research.

As data science grows, sustainability will likely become an increasingly important research topic.

25.) Educational Data

Education has always been a great topic for research, and with the advent of big data, educational data has become an even richer source of information.

By studying educational data, researchers can gain insights into how students learn, what motivates them, and what barriers these students may face.

Besides, data science can be used to develop educational interventions tailored to individual students’ needs.

Imagine being the researcher that helps that high schooler pass mathematics; what an incredible feeling.

With the increasing availability of educational data, data science has enormous potential to improve the quality of education.

26.) Politics

As data science continues to evolve, so does the scope of its applications.

Originally used primarily for business intelligence and marketing, data science is now applied to various fields, including politics.

By analyzing large data sets, political scientists (data scientists with a cooler name) can gain valuable insights into voting patterns, campaign strategies, and more.

Further, data science can be used to forecast election results and understand the effects of political events on public opinion.

With the wealth of data available, there is no shortage of research opportunities in this field.

As data science evolves, so does our understanding of politics and its role in our world.

27.) Cloud Technologies

Cloud technologies are a great research topic.

It allows for the outsourcing and sharing of computer resources and applications all over the internet.

This lets organizations save money on hardware and maintenance costs while providing employees access to the latest and greatest software and applications.

I believe there is an argument that AWS could be the greatest and most technologically advanced business ever built (Yes, I know it’s only part of the company).

Besides, cloud technologies can help improve team members’ collaboration by allowing them to share files and work on projects together in real-time.

As more businesses adopt cloud technologies, data scientists must stay up-to-date on the latest trends in this area.

By researching cloud technologies, data scientists can help organizations to make the most of this new and exciting technology.

28.) Robotics

Robotics has recently become a household name, and it’s for a good reason.

First, robotics deals with controlling and planning physical systems, an inherently complex problem.

Second, robotics requires various sensors and actuators to interact with the world, making it an ideal application for machine learning techniques.

Finally, robotics is an interdisciplinary field that draws on various disciplines, such as computer science, mechanical engineering, and electrical engineering.

As a result, robotics is a rich source of research problems for data scientists.

29.) HealthCare

Healthcare is an industry that is ripe for data-driven innovation.

Hospitals, clinics, and health insurance companies generate a tremendous amount of data daily.

This data can be used to improve the quality of care and outcomes for patients.

This is perfect timing, as the healthcare industry is undergoing a significant shift towards value-based care, which means there is a greater need than ever for data-driven decision-making.

As a result, healthcare is an exciting new research topic for data scientists.

There are many different ways in which data can be used to improve healthcare, and there is a ton of room for newcomers to make discoveries.

30.) Remote Work

There’s no doubt that remote work is on the rise.

In today’s global economy, more and more businesses are allowing their employees to work from home or anywhere else they can get a stable internet connection.

But what does this mean for data science? Well, for one thing, it opens up a whole new field of research.

For example, how does remote work impact employee productivity?

What are the best ways to manage and collaborate on data science projects when team members are spread across the globe?

And what are the cybersecurity risks associated with working remotely?

These are just a few of the questions that data scientists will be able to answer with further research.

So if you’re looking for a new topic to sink your teeth into, remote work in data science is a great option.

31.) Data-Driven Journalism

Data-driven journalism is an exciting new field of research that combines the best of both worlds: the rigor of data science with the creativity of journalism.

By applying data analytics to large datasets, journalists can uncover stories that would otherwise be hidden.

And telling these stories compellingly can help people better understand the world around them.

Data-driven journalism is still in its infancy, but it has already had a major impact on how news is reported.

In the future, it will only become more important as data becomes increasingly fluid among journalists.

It is an exciting new topic and research field for data scientists to explore.

32.) Data Engineering

Data engineering is a staple in data science, focusing on efficiently managing data.

Data engineers are responsible for developing and maintaining the systems that collect, process, and store data.

In recent years, there has been an increasing demand for data engineers as the volume of data generated by businesses and organizations has grown exponentially.

Data engineers must be able to design and implement efficient data-processing pipelines and have the skills to optimize and troubleshoot existing systems.

If you are looking for a challenging research topic that would immediately impact you worldwide, then improving or innovating a new approach in data engineering would be a good start.

33.) Data Curation

Data curation has been a hot topic in the data science community for some time now.

Curating data involves organizing, managing, and preserving data so researchers can use it.

Data curation can help to ensure that data is accurate, reliable, and accessible.

It can also help to prevent research duplication and to facilitate the sharing of data between researchers.

Data curation is a vital part of data science. In recent years, there has been an increasing focus on data curation, as it has become clear that it is essential for ensuring data quality.

As a result, data curation is now a major research topic in data science.

There are numerous books and articles on the subject, and many universities offer courses on data curation.

Data curation is an integral part of data science and will only become more important in the future.

34.) Meta-Learning

Meta-learning is gaining a ton of steam in data science. It’s learning how to learn.

So, if you can learn how to learn, you can learn anything much faster.

Meta-learning is mainly used in deep learning, as applications outside of this are generally pretty hard.

In deep learning, many parameters need to be tuned for a good model, and there’s usually a lot of data.

You can save time and effort if you can automatically and quickly do this tuning.

In machine learning, meta-learning can improve models’ performance by sharing knowledge between different models.

For example, if you have a bunch of different models that all solve the same problem, then you can use meta-learning to share the knowledge between them to improve the cluster (groups) overall performance.

I don’t know how anyone looking for a research topic could stay away from this field; it’s what the Terminator warned us about!

35.) Data Warehousing

A data warehouse is a system used for data analysis and reporting.

It is a central data repository created by combining data from multiple sources.

Data warehouses are often used to store historical data, such as sales data, financial data, and customer data.

This data type can be used to create reports and perform statistical analysis.

Data warehouses also store data that the organization is not currently using.

This type of data can be used for future research projects.

Data warehousing is an incredible research topic in data science because it offers a variety of benefits.

Data warehouses help organizations to save time and money by reducing the need for manual data entry.

They also help to improve the accuracy of reports and provide a complete picture of the organization’s performance.

Data warehousing feels like one of the weakest parts of the Data Science Technology Stack; if you want a research topic that could have a monumental impact – data warehousing is an excellent place to look.

36.) Business Intelligence

Business intelligence aims to collect, process, and analyze data to help businesses make better decisions.

Business intelligence can improve marketing, sales, customer service, and operations.

It can also be used to identify new business opportunities and track competition.

BI is business and another tool in your company’s toolbox to continue dominating your area.

Data science is the perfect tool for business intelligence because it combines statistics, computer science, and machine learning.

Data scientists can use business intelligence to answer questions like, “What are our customers buying?” or “What are our competitors doing?” or “How can we increase sales?”

Business intelligence is a great way to improve your business’s bottom line and an excellent opportunity to dive deep into a well-respected research topic.

37.) Crowdsourcing

One of the newest areas of research in data science is crowdsourcing.

Crowdsourcing is a process of sourcing tasks or projects to a large group of people, typically via the internet.

This can be done for various purposes, such as gathering data, developing new algorithms, or even just for fun (think: online quizzes and surveys).

But what makes crowdsourcing so powerful is that it allows businesses and organizations to tap into a vast pool of talent and resources they wouldn’t otherwise have access to.

And with the rise of social media, it’s easier than ever to connect with potential crowdsource workers worldwide.

Imagine if you could effect that, finding innovative ways to improve how people work together.

That would have a huge effect.

Final Thoughts, Are These Research Topics In Data Science For You?

Thirty-seven different research topics in data science are a lot to take in, but we hope you found a research topic that interests you.

If not, don’t worry – there are plenty of other great topics to explore.

The important thing is to get started with your research and find ways to apply what you learn to real-world problems.

We wish you the best of luck as you begin your data science journey!

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Multivariate Polynomial Regression Python

140 Excellent Big Data Research Topics to Consider

Table of Contents

Are you a computer science student searching for recent big data research topics for your final year project? Do you want to write a top-quality big data research paper but are confused about what topic to choose? If yes, then this blog post is for you.

Big Data is one of the recently emerging technologies that have gained a lot of attraction among professionals, especially computer science engineers and information technologists. In the latest internet world, we are surrounded by data and information. Particularly, after the advent of digital systems, data is considered to be precious. In order to process, store, and analyze a large volume of data, the concept of Big Data came into existence.

To write an excellent computer science thesis on big data, you must have a valid research topic. As big data is a broad subject, choosing a new trending research topic is a challenging task. So, to help you, here, in this blog post, we have listed the top interesting big data topics for you to consider for research or academic writing.

List of Outstanding Big Data Research Topics

When it comes to writing research papers and essays, it is necessary to choose trendy research topics to get an A+ grade. As far as big data is concerned, you can conduct research on any interesting data science topics, data mining topics, data analysis topics, or data security topics.

Listed below are a few top-notch big data research topic ideas. You can go through the complete list and identify the best big data research topic of your desire.

Simple Big Data Research Ideas

Maturity model of big data.
How far is data science relevant as a master’s thesis and research in today’s date?
How can big data develop organizational operations and enhance its competitive advantage in the current competitive market?
Briefly describe the Hadoop Ecosystem
Describe the use of NoSQL Database and R Programming
Evaluation of SQL-based Technologies
Describe the application of Predictive Analytics
Comparative analysis of the application of Apache Spark and Elasticsearch
Describe the difference between Tensor Flow, Beam, and Apache Airflow
Compare and contrast Docker and Kubernetes
How does the use of data analytics bring positive social impact?
Discuss the use of Big Data in therapies and genomics
Describe the three major components of big data
What are the major challenges of big data?
Discuss the impact of Big Data on bioinformatics

Big Data Analysis Research Topics

Who uses big data analytics?
Why is domain knowledge important in data analysis?
What is distributed semantic analytics?
Why is data exploration important in data analysis?
Define semantic questions answering
What is structured machine learning?
What is semantic data management ?
The Internet of Things
How important is artificial intelligence?
Describe the importance of augmented reality.
What is agile data science?
Explain the knowledge validation and extraction.
Explain the deep learning process.
Significance of machine learning for modern business.
What is hyper-personalization?
Experience economy and its relevance.
Analyzing large-scale data for social networks
Discuss the behavioral analytics process.
Explain journey sciences.
Discuss the graph analytics process.
Explore the problems associated with big data.
Analyze the use of GIS and spatial data.
How far is big data for storage and transfer
How can big data be used for efficiently modeling uncertainty?
Explore the use of Quantum computing for big data Analytics
Describe the five latest Big Data trends in 2022
Discuss DataOps and data stewardship
What are the essential practices related to big data analytics for manufacturing businesses?
Discuss the best way to preserve and Assess Big Data, Video Integrity, and Images using AI
Evaluate the Use of Big Data in Healthcare
Evaluation of the effectiveness of healthcare diagnoses and using deep learning
Synergies of machine learning and data management: methods, problems, and future directions
Describe the usefulness of Big Data analysis

Data Mining Research Topics

Big data mining techniques and tools
The role of data mining in analyzing transaction data in a supermarket.
Parallel spectral clustering within a distributed system
Explain the Association Rule Learning regarding data mining
Describe the concept of data spectroscopic clustering
Describe asymmetrical spectral clustering
What is information-based clustering?
Self-turning spectral clustering
Discuss the K-Means clustering from an online spherical perspective.
Discuss the K-Means algorithms in data clustering.
Symmetrical spectral clustering
Discuss the performance of representative-based clustering.
Discuss the package of MATLAB spectral clustering.
How can the effectiveness of nonlinear and linear regression analysis be improved?
Discuss the hierarchical clustering application.
Explain the performance of dependency modeling.
Explain the importance of probabilistic classification in data mining.
Model-based clustering of texts
Explain the need for density-based clustering.
Discuss the importance of subject-based data mining in minimizing terrorism.
Explore how data mining can be used in automatic content generation.
The use of data mining in evaluating employee performance.
Discuss about Parallel Spectral Clustering in Distributed System
What are K-Means Algorithms for Data Clustering and how it gets applied in Data Mining?
Why Data mining is called an iterative process?
How does Data mining go through the phases laid down by the Cross Industry Standard Process for Data Mining (CRISP-DM) process model?
Compare and contrast Data Mining and Web Mining
Discuss the differences between Oracle Data Mining and Test Mining
Analyze Data Mining as a Service(DMaaS)
What is called Domain Driven Data Mining and Opinion Mining?
How Predictive Analytics is Used in Data Mining?
Discuss the benefits and drawbacks of using Web mining for businesses that depend on the web

Read more: Innovative Technology Research Topics To Explore and Write About

Data Security Research Topics

Why should big data owners update security measures regularly?
How does changing the data from Terabytes to Petabytes affect its security?
What are the major vulnerabilities of big data?
The security technologies that can be used to protect big data
How does Hadoop integrate with modern security tools?
Token-based authentication
How do data encryption tools work?
How can poor data security lead to the loss of important information?
Why is user access control important?
How to prevent illegitimate data access?
How to identify a legit data user?
The importance of centralized key management
How to implement attribute-access or role-based access control?
How do intrusion prevention and detection systems work?
The best intrusion detection system
Which tool or algorithm can be used for data owner and user authentication?
What are the most effective physical systems for securing data?
The implementation of attribute-access or role-based access control.
Explain how you can determine the amount of secure data.
The best encryption tools for protecting transit data.

Recent Trending Big Data Research Topics

Data retention and its importance.
Describe data catalog approaches, implementations, and adoption.
Describe some of the most innovative bid data management concepts.
Analytics for Big Data in the Smart Healthcare Systems
New technologies and AI in data management
Explain the best data management strategies for modern enterprises.
How to manage platforms for enterprise analytics
The impact of data quality on business
How can a company implement data governance?
How can machine learning improve the data quality?
Anomaly detection in large-scale data systems
The process of analyzing and managing data for reproducible research.
Data catalog reference model and market study
The role of data valuation in data management.
Explain software engineering for big data science.
How to ensure effective data protection through proper management
Big data analytics and privacy preservation
Data publishing and access by modern companies
How to work with images during research?
How to promote research and scientific outreach through data management?

Read more: Interesting Cybercrime Research Topics To Deal With

Unique Big Data Research Topics

Evaluate the logistic regression modeling.
Explain the malicious user detection in big data collection.
Evaluate data stream management in task allocation.
Explain how to gather and monitor traffic information using CCTV images
What is the difference between edge computing and in-memory computing?
Explain the difference between agile data science and Scala language.
Evaluate how Scala includes a useful REPL for interaction.
Discuss the influence of big data and smart city planning in society.
Evaluate the adaptive systems and models at runtime.
Explain the relation between urban dynamics and crowdsourcing services.

The Bottom Line

From the list of 100+ ideas suggested above, choose any topic that matches your university requirements and compose a brilliant big data research paper . In case, you are not satisfied with the topics recommended here, contact us immediately. We have plenty of subject professionals on our platform to offer premium-quality Big data assignment help . Especially, starting from research paper topic selection to writing and editing, our assignment helpers who are experts in big data would provide the best assistance as per your needs at an affordable cost. Moreover, by availing of our big data research paper writing service, you can also submit plagiarism-free academic papers on time and secure the grades you desire to score.

Without any dilemma, take our academic writing service and enjoy all the scholastic benefits it offers.

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100+ Controversial Research Topics and Ideas to Focus On

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166 Latest Big Data Research Topics And Fascinating Ideas

Big data refers to a huge volume of data, whether organized or unorganized, whose analysis shapes technologies and methodologies. Big data is so massive and complicated that it cannot be handled using ordinary application software. For instance, some frameworks, such as Hadoop, are built to process large amounts of data. Big data has gained much attention, hence it’s a trendy topic and essay for students and researchers who want to write thesis, projects, and dissertations. Based on this, there are several searchable and interesting topics to explore for undergraduate and master’s theses in big data, same as doctoral degrees. In this article, we have provided every topic you need on big data. Our topics stretch from big data analytics, big data research questions, to IoT and database essays. If you’ve been looking for the latest big data research topics, your search stops here. Read on to see some of the most interesting topics for your thesis.

Interesting Big Data Analytics Research Topics

Data analytics is the lifeblood of the modern IT sector. Big data is one of the strategies and technologies for analyzing large amounts of data. Data analytics is being used by the industry to acquire knowledge of system performance and customer behavior. Here are some of the best big data analytics topics and ideas for academic papers.

The surge of Internet of Things (IoT)
Explain the significance of augmented reality.
What is the significance of artificial intelligence?
Describe the graph analytics procedure.
What is agile data science, and how does it differ from traditional data science?
What role does machine intelligence play in today’s businesses?
What is hyper-personalization, and how does it work?
Describe how behavioral analytics works.
What is the experience economy, and how does it work?
Talk about the science of travel.
Discuss the validation and extraction of knowledge.
What is semantic data management, and how does it work?
Describe the process of deep learning.
Describe software engineering in the context of big data science.
What is structured machine learning, and how does it work?
Describe how to answer a semantic question
What is distributed semantic analytics, and how does it work?
What role does domain knowledge play in data analysis?
Why is data exploration important in data analysis?
Who uses big data analytics?

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Most Debatable Big Data Research Topics and Essays

The rapid rise of big data in our current time is not without controversy. There is a myriad of ongoing debates in the discipline that have gone unresolved for quite some time. The list below contains the most common big data debate topics.

Big data and its major vulnerabilities.
What measures are in place to recognize a legit user of big data?
Explain the significance of user-access control.
Investigate the importance of centralized key management.
Identify ways to prevent illegal access of data.
Intrusion-detection system: Which is the best?
Does machine learning enhance data quality?
Which security technology has proven to be the best for big data protection?
What strategies should be used for data governance and who should implement data policies?
Should tech giants regularly update security measures and be transparent about them?
How has poor data security contributed to the loss of historical evidence?
What are the most important big data management tools and strategies?
What is data retention and explain its relevance?
Artificial intelligence will lead to the loss of employment and human interaction.
Enterprise analytics: How to manage platforms?
Can data management foster the promotion of peace and freedom?
Who should be in control of data security: Tech giants or the government?
What are the functions of the government in big data management and security?
Discuss how big data is leading to the end of morals and ethics.
How is big data contributing to the rise of global climate and why tech should pay carbon taxes.

Interesting Dissertation Topics on Big Data

Many research theses and big data topics can be found online for undergraduates, Masters, and Ph.D. students. The list below comprises some dissertation topics on big data.

Privacy and security issues in big data and how to curtail them.
Impacts of storage systems of scalable big data.
The significance of big data processing and data management to industrial development.
Techniques and data mining tools for big data.
The benefits of data analytics and cloud computing to the future of work.
Parallel data processing: effective data architecture and how to go about it.
Impacts of machine learning algorithms on the fashion industry.
Using bandwidth provision, how the world of streaming is changing.
What are the benefits and threats of dedicated networks to governance?
Cloud gaming and impacts on Millennials and Generation Z.
Ways to enhance and maximize spread efficiency using flow authority model.
How divergent and convergent is the Internet of Things (IoT) on manufacturing?
Data mining and environmental impact: The way forward.
Geopolitics and the surge of demographic mapping in big data.
Impacts of travel patterns on big data analytics and data management.
The rise of deep learning in the automotive industry.
The sophistication of big data and its implications on cybersecurity.
Discuss how the big data manufacturing process indicates positive globalization.
Evaluate the future of data mining and the adaptation of humans to big data.
Human and material wastes in big data management.

Interesting Research Topics on A/B Testing in Big Data

The A/B testing is also known as controlled experiments and is used widely by companies and firms to make decisions in product launches. Tech companies use the test to know the acceptability of a certain product by the users. However, below are some key research topics on A/B testing in Big Data

Evaluate the common A/B pitfalls in the automotive industry.
Discuss the benefits of improving library user experience with A/B Testing.
How to design A/B tests in a collaboration network.
Analyze how the future of social network advertising can be improved by A/B testing.
Effectiveness of A/B experiments in MOOCs for better instructional methods.
Strategies of Bayesian A/B testing for business decisions.
A/B testing challenges in large-scale social networks and online controlled experiments.
Illustrate how consumer behaviors and trends are shaped by A/B testing.

List of Research Topics on Big Data and Local Governments

Big data offers tremendous value to grassroots governments with the ability to optimize cost through data-induced decisions that reduce the crime rate, traffic congestion and improve the environment. Below are interesting topics on big data and local governments.

How local governments can measure crime using big data testing.
Big data and algorithmic policy in local government policies.
Application of data science technologies to civil service in the local government.
Combating grassroots crime and corruption through algorithmic government.
Big data in the public sector: how local governments can benefit from the algorithmic policy.

Innovative Research Topics on Big Data and IoT

Big data has a lot in common with the Internet of Things (IoT). Indeed, IoT is an integral part of big data. Below are researchable IoT and big data research topics.

The impacts of big data and the Internet of Things (IoT) on the fourth industrial revolution.
The importance of big data and the Internet of Things (IoT) on public health systems.
Explain how big data and the Internet of Things (IoT) dictate the flow of information in the media sector.
Challenges of big data and the Internet of Things (IoT) on governance and sustainability.
The disruption of big data and its attendant effects on the Internet of Things (IoT).
Illustrate the surge in household smart devices and the role of big data analytics.
An analysis of the disruption of the supply chain of traditional goods through the Internet of Things (IoT).
A comprehensive evaluation of machine and deep learning for IoT-enabled healthcare systems.
The future evaluation of the internet of things and big data analytics in the public infrastructure systems.
Discuss how AI-induced security can guarantee effective data protection.
IoT privacy: what data protection means to households and the impacts of security infringement.
Discuss the role of big data and the integrity of the Internet of Things (IoT).
How do dedicated networks work through the Internet of Things (IoT)?
The threats and benefits of the Internet of Things (IoT) forensic science.
Big data distributed storage and impacts on IoT-enabled industries.

Most Engaging Database Big Data Research Topics

The database category of big data has some interesting data science research topics. Due to the large data, modern companies have to analyze every day, which are difficult to handle, strict managing is essential to make sure of the effective use of data. Check out some intriguing big data database research topics students and researchers can write about.

Explain the most inventive big data information concepts and strategies.
Clarify the most ideal data management strategies and techniques for present-day businesses.
New advancements and AI in information management.
What is information maintenance and for what reason is it significant?
Depict the essentials of information management.
Clarify the use of information management in e-learning.
Information distribution and access by present-day organizations.
Clarify the most common way of investigating and overseeing information for biomedical exploration.
Disclose how to function with 3D pictures during research.
How could an association guarantee secure and classified information management and security?
Information indexes: Describe approaches and their execution as well as their reception.
Talk about the effect of information quality on a business.
Instructions on how to advance medical examination and reach logical effort through information management.
The most effective method to source and oversee external data.
Evaluate the procedures available to organizations in ensuring information security through appropriate administration.
Information catalog reference model and global market study.
What is information valuation and what difference does it make in information management?
How could AI further develop database security?
How might an organization carry out effective data administration?
Database management and the cost of disruptive cybersecurity.

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Compelling Big Data Scala Research Topics

Big Data Scala is the product of algorithmic frameworks in deep and machine learning. Below are listed topics on big data Scala for students and young researchers.

Large information versatility dependent on Scala and Spark Machine Learning Libraries
Analyze versatile large information stockpiling frameworks in deep learning.
Dealing with Data and Model drift for practical applications.
Building generative systems based on conversational frameworks (Chatbot systems).
Adaptable designs for parallel data building.
Dealing with continuous video analytics in cloud computing.
Proficient graph processing at a machine learning scale.
Dimensional reduction approaches for information management.
Compelling anonymization of sensitive fields in computer vision.
Versatile security safeguarding on big data.

List of Independent Research Topics for Big Data

Independent researches are pieces of research that may be considered unorthodox in big data testing and management. These are research studies generated by individual researchers. Here is a list of the most fascinating independent research topics on big data.

Significance of effective data mining tools and procedures.
What is data-driven clustering in deep and machine learning?
How impactful is the graph analytics process to the Internet of Things?
Explain the significance of AI for present-day businesses.
Significance of information investigation in information examination on deep learning.
Evaluate the usefulness of coding in Artificial Intelligence.
Clarify the AI strategies in big data management.
Data security: what it means to computer vision.
Impact of open-source deep learning libraries on developers.
The significance of token-based authentication to data security.
Using big data to identify disinformation and misinformation.
Data management and the fundamental principles of Artificial Intelligence.
Big data analytics and why it should be more user-friendly.
Why business intelligence should focus more on privacy preservation.
Social networks and impact on privacy infringement.

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Although we have provided you with a list of big data essays to choose from, we dare say university research topics go beyond mere writing tips. As a student, you may need quality college paper writing services and professional assistance to writing an A-graded and top-notch thesis or dissertations. Here is where we come in. You can consult our reliable and professional writing experts to ease your degree courses at a pocket-friendly price. Aside, you can also refer your colleagues online to enjoy our discounted services that will make your research experience less tacky and frustrating.

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Top 15+ Big Data Dissertation Topics

The term big data refers to the technology which processes a huge amount of data in various formats within a fraction of seconds . Big data handles the research domains by means of managing their data loads. Big data dissertation helps to convey the perceptions on the proposed research problems. It is also known as the new generation technology which could compatible with high-speed data acquisitions, storage, and analytics . From this article, you will come to know the big data dissertation topics with their relevant justifications”

In general, dissertation writing is one of the irreplaceable parts of the research . A well-drafted dissertation helps you to point out the issues and solutions of the researched area to the other opponents . Our technical team has framed this article with the introduction of big data fundamentals to make you understand. At the end of this article, you are going to become a master in the areas of dissertation topics without any doubts. Shall we move on to the upcoming areas? Let’s move to get into the article.

Top 5 Interesting Big Data Dissertation Topics

Fundamentals of Big Data

Pattern Analytics
Sentiment Analysis
Block Modeling
Association Rule Mining
Partitioning Nodes
Cassandra & Oozie
Hbase & JAQL
Mahout & Hadoop
Hive & Middleware
Pig & MapReduce
Demographical Data
Social Media Data
Multimedia Data
Crime Incidents
Financial Reports
Telephone Histories
Network Location Data
Observation Logs

The above listed are the aspects that are getting comprised in the fundamentals of big data . Big data is the technology to progress a huge amount of data with homogeneity by numerous concepts. Big data applications can be deployed in any of the fields to achieve extreme results in the determined areas of research/projects . In the subsequent areas, we mentioned to you the pipeline architecture of the big data for the ease of your understanding.

Big data progresses the unstructured data and normalizes the same in the human-readable formats. Our technical crew is very much sure about every concept of big data technology . Now let us move on to the next phase. Are you interested in stepping into the next section? Come we will learn together.

Pipeline Architecture for Big Data

Data Warranty
Data Cleaning
Meta Data Managing
Raw & Normalized Logs Storage
Prescriptive & Descriptive
Pattern Recognition
Machine Learning & AI
Statistical Data Mining
Decision Support Methods
Visualized Dashboards
Alerting & Reporting Systems

This is how the big data architecture is built in real-time. Generally, manual working with a massive amount of data leads to too much time ingestions. Besides, you need to get familiar with the big data technical concepts to exclude these limitations . Usually, it needs experts’ pieces of advice to learn the eminent and crucial edges of those overlays.

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Familiar with Hadoop & Cloud era etc.
Google & AWS cloud deployment practices
Virtuous inherent writing skillsets
Experts in handling the bottlenecks with various tools
Masters in big data concepts
Experts in IoT, deep learning, machine learning & data mining
Conversant with software, hardware, myriad & Matlab tools
Experts in multivariable calculus, matrix & linear algebra
Highly aware of Hadoop , SQL, R, Hive & Scala
Proficient in Python, Java, C++ & R

The aforementioned are the various skillsets of our technical team. We are delivering the big data and other projects/researches by interpreting with these techniques and abilities. So far, we have discussed the basic concepts of big data analytics . We thought that it would be the right time to reveal the major features that overlap in big data analytics for the ease of your understanding. Shall we guys get into that phase? Here we go!!!

Major Features of Big Data Analytics

Optimization of data storage
Processing large volume of data
Relevant search option
Feedbacks update and work precisely

The listed above passage conveyed to you the features that manipulate the workflow of big data . As the matter of fact, our technical team with experts is frequently updating them according to the trends in the technology industry and solves the problems that arise in it. As this article is concentrated on the big data dissertation topics, our experts want to highlight the major problems that get up in big data management to improve your skill sets in that areas too. Let us have the next section!!!

Major Problems in Big Data

Difficult to work with the different data formats
Massive unstructured data ranges from videos, data & image
Region-wise privacy control variations make much complex
Trains the decentralized data models
Accommodates with the regulatory in which data cannot be shared
Requires improved local models in each boundary
Hardware or software level security is big a challenge
It fails to preserve the sensitive fields in the healthcare systems
For instance, it reveals the personal health records visibly
It fails to recognize the abnormalities (anomalies) of the big data
In addition, it is the major issue in telecom domains
Effective graph processing is needed in social media analysis
It fails to handle the large scale graph processing
Spark & Hadoop processes the online & offline data formats
It requires improved scalability to process the parallel big data
Videos are the public data transmission medium
For instance CCTV footages, YouTube, and other social media video clips
Data storage in cloud systems are a challenging issue here
Inaccurate / Partial & Low Reliability is the biggest issue here
Unlabeled data vagueness makes it much complex
It results in data omission & ineffective data propagation
Leads to understand the meaning in different ways
Visualization of the massive amount of data dimensions are not possible
Results in spreading rumors unconditionally
Fake data sources are Whatsapp, Twitters & forged URLs

The listed above are the major problems that are being faced in big data technologies. However, these issues can be eradicated by the deployment of several tools along with improving the techniques of the same. In fact, this phase needs experts guidance. We do have world-class certified engineers to perform in emerging technologies.

If you are facing any issues in these areas while experimenting you can approach our researchers at any time. We are always welcoming the students to get benefits from us.

In a matter of fact, our technical crew is very much intelligent in handling the thesis/dissertation as well as familiar in the areas of big data projects and researches. Yes, we are going to cover the next section by highlighting the recent big data dissertation topics for your better understanding. As we reserved the unique places in the industries, we are being trusted blindly in the event of providing the unimaginable innovations in the determined dissertation and other works.

Recent Big Data Dissertation Topics

Huge Scale Key-Value Storing & Data Distribution by Kinetic Drives
Blocking Falls / HOL Deadlock Freedom & Minimal Path Routing by Smart-queuing
Digital 5D Network Applications by Lessor Dimensionality Elements
Effective Biological Network Analytics by Graph Theory Sampling Methods
Advanced Big Data Segmentation (unfair) by Boosted Sampling Methods
Collaborative Filtering & Huge Scale Bipartite Rating Graphs by Spark
DDoS Attack Mitigation by IoT & SDN
Termination of Tasks by Drive Diagnostic Data Center Attribution System
Container Resource Integrations by Hadoop Transcoding Cluster Split Samples
Retail Supply Chain Decision Making & Alerting System by Cloud Computing
Sensitive Processes by Collaborative Filtering Algorithm & Quality Variance Methods
Keyword Searches in Proxy Servers & Cloud Computing by Cryptography
Non-Collaborative (Game) Cloud Computing by Task Scheduling Algorithm
Multi-core Parallelizing & Overlapping by Speaker Listener Label Propagation
Bipartite Graphs for Vacation Spots by Inventive Recommendation Frameworks

The above listed are some of the big data dissertation topics . In this section we have used some acronyms; we thought that you might need their explanations to understand the same.

SDN- Software Defined Networking
DDoS- Distributed Denial of Service
IoT- Internet of Things

Let’s begin your dissertation works by envisaging these as your references. We hope that you are getting the points as of now listed. As the matter of fact, we are offering the dissertation services at the lowest cost compared to others. In addition to that, we have delivered more than 10,000 big data dissertations till now.

To be honest, each big data dissertation has a unique quality and we never imitate the contents as represented in the other dissertations. This makes us irreplaceable from others. If you are interested, let’s join your hands with us to experience the inexperienced technical fields. In addition to these sections, we have also wanted to encompass the big data analytics tools for the ease of your understanding. Let’s have that section!

Big Data Analytics Tools

Imports data from RDBMS and sends to the Hadoop systems for queries
Runs the aggregated queries & generates the columnar based database
Sums up the incidences and words in the given inputs
Stores the massive unstructured data & acts as a data streaming mode
Computational open source big data tool with real-time occurrences
Analyses & processes the immense amount of data robustly
Handles the data portions effectively (chunks) & distributed DB
Manages and integrates the big data acquisitions
Deals with the dynamic datasets
Analyses & warehouses the huge amount of data

The aforementioned are the top big data analytical tools . In those tools, Spark & Kafka writes simple window sliding queries to identify the necessary data. Open source datasets & log data parsing can be practiced if you become familiar with the functionalities and concepts of the big data analytical tools. So far, we have learned in the areas of big data dissertation topics. We hope that you would have enjoyed this article as this is conveyed to you the very essential aspects with crystal clear points. We are hoping for your explorations.

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List of Best Research and Thesis Topic Ideas for Data Science in 2022

In an era driven by digital and technological transformation, businesses actively seek skilled and talented data science potentials capable of leveraging data insights to enhance business productivity and achieve organizational objectives. In keeping with an increasing demand for data science professionals, universities offer various data science and big data courses to prepare students for the tech industry. Research projects are a crucial part of these programs and a well- executed data science project can make your CV appear more robust and compelling. A broad range of data science topics exist that offer exciting possibilities for research but choosing data science research topics can be a real challenge for students . After all, a good research project relies first and foremost on data analytics research topics that draw upon both mono-disciplinary and multi-disciplinary research to explore endless possibilities for real –world applications.

As one of the top-most masters and PhD online dissertation writing services , we are geared to assist students in the entire research process right from the initial conception to the final execution to ensure that you have a truly fulfilling and enriching research experience. These resources are also helpful for those students who are taking online classes .

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Data science thesis topics

We have compiled a list of data science research topics for students studying data science that can be utilized in data science projects in 2022. our team of professional data experts have brought together master or MBA thesis topics in data science that cater to core areas driving the field of data science and big data that will relieve all your research anxieties and provide a solid grounding for an interesting research projects . The article will feature data science thesis ideas that can be immensely beneficial for students as they cover a broad research agenda for future data science . These ideas have been drawn from the 8 v’s of big data namely Volume, Value, Veracity, Visualization, Variety, Velocity, Viscosity, and Virility that provide interesting and challenging research areas for prospective researches in their masters or PhD thesis . Overall, the general big data research topics can be divided into distinct categories to facilitate the research topic selection process.

Security and privacy issues
Cloud Computing Platforms for Big Data Adoption and Analytics
Real-time data analytics for processing of image , video and text
Modeling uncertainty

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DATA SCIENCE PHD RESEARCH TOPICS

The article will also guide students engaged in doctoral research by introducing them to an outstanding list of data science thesis topics that can lead to major real-time applications of big data analytics in your research projects.

Intelligent traffic control ; Gathering and monitoring traffic information using CCTV images.
Asymmetric protected storage methodology over multi-cloud service providers in Big data.
Leveraging disseminated data over big data analytics environment.
Internet of Things.
Large-scale data system and anomaly detection.

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Publication; We don’t just suggest PhD data science research topics but our PhD consultancy services take your research to the next level by ensuring its publication in well-reputed journals. A PhD thesis is indispensable for a PhD degree and with our premier best PhD thesis services that tackle all aspects of research writing and cater to essential requirements of journals , we will bring you closer to your dream of being a PhD in the field of data analytics.
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Research experience: We take pride in our world –class team of computing industry professionals equipped with the expertise and experience to assist in choosing data science research topics and subsequent phases in research including findings solutions, code development and final manuscript writing.
Business ethics: We are driven by a business philosophy that‘s wholly committed to achieving total customer satisfaction by providing constant online and offline support and timely submissions so that you can keep track of the progress of your research.

Now, we’ll proceed to cover specific research problems encompassing both data analytics research topics and big data thesis topics that have applications across multiple domains.

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Multi-modal Transfer Learning for Cross-Modal Information Retrieval

Aim and objectives.

The research aims to examine and explore the use of CMR approach in bringing about a flexible retrieval experience by combining data across different modalities to ensure abundant multimedia data.

Develop methods to enable learning across different modalities in shared cross modal spaces comprising texts and images as well as consider the limitations of existing cross –modal retrieval algorithms.
Investigate the presence and effects of bias in cross modal transfer learning and suggesting strategies for bias detection and mitigation.
Develop a tool with query expansion and relevance feedback capabilities to facilitate search and retrieval of multi-modal data.
Investigate the methods of multi modal learning and elaborate on the importance of multi-modal deep learning to provide a comprehensive learning experience.

The Role of Machine Learning in Facilitating the Implication of the Scientific Computing and Software Engineering

Evaluate how machine learning leads to improvements in computational APA reference generator tools and thus aids in the implementation of scientific computing
Evaluating the effectiveness of machine learning in solving complex problems and improving the efficiency of scientific computing and software engineering processes.
Assessing the potential benefits and challenges of using machine learning in these fields, including factors such as cost, accuracy, and scalability.
Examining the ethical and social implications of using machine learning in scientific computing and software engineering, such as issues related to bias, transparency, and accountability.

Trustworthy AI

The research aims to explore the crucial role of data science in advancing scientific goals and solving problems as well as the implications involved in use of AI systems especially with respect to ethical concerns.

Investigate the value of digital infrastructures available through open data in aiding sharing and inter linking of data for enhanced global collaborative research efforts
Provide explanations of the outcomes of a machine learning model for a meaningful interpretation to build trust among users about the reliability and authenticity of data
Investigate how formal models can be used to verify and establish the efficacy of the results derived from probabilistic model.
Review the concept of Trustworthy computing as a relevant framework for addressing the ethical concerns associated with AI systems.

The Implementation of Data Science and their impact on the management environment and sustainability

The aim of the research is to demonstrate how data science and analytics can be leveraged in achieving sustainable development.

To examine the implementation of data science using data-driven decision-making tools
To evaluate the impact of modern information technology on management environment and sustainability.
To examine the use of data science in achieving more effective and efficient environment management
Explore how data science and analytics can be used to achieve sustainability goals across three dimensions of economic, social and environmental.

Big data analytics in healthcare systems

The aim of the research is to examine the application of creating smart healthcare systems and how it can lead to more efficient, accessible and cost –effective health care.

Identify the potential Areas or opportunities in big data to transform the healthcare system such as for diagnosis, treatment planning, or drug development.
Assessing the potential benefits and challenges of using AI and deep learning in healthcare, including factors such as cost, efficiency, and accessibility
Evaluating the effectiveness of AI and deep learning in improving patient outcomes, such as reducing morbidity and mortality rates, improving accuracy and speed of diagnoses, or reducing medical errors
Examining the ethical and social implications of using AI and deep learning in healthcare, such as issues related to bias, privacy, and autonomy.

Large-Scale Data-Driven Financial Risk Assessment

The research aims to explore the possibility offered by big data in a consistent and real time assessment of financial risks.

Investigate how the use of big data can help to identify and forecast risks that can harm a business.
Categories the types of financial risks faced by companies.
Describe the importance of financial risk management for companies in business terms.
Train a machine learning model to classify transactions as fraudulent or genuine.

Scalable Architectures for Parallel Data Processing

Big data has exposed us to an ever –growing volume of data which cannot be handled through traditional data management and analysis systems. This has given rise to the use of scalable system architectures to efficiently process big data and exploit its true value. The research aims to analyses the current state of practice in scalable architectures and identify common patterns and techniques to design scalable architectures for parallel data processing.

To design and implement a prototype scalable architecture for parallel data processing
To evaluate the performance and scalability of the prototype architecture using benchmarks and real-world datasets
To compare the prototype architecture with existing solutions and identify its strengths and weaknesses
To evaluate the trade-offs and limitations of different scalable architectures for parallel data processing
To provide recommendations for the use of the prototype architecture in different scenarios, such as batch processing, stream processing, and interactive querying

Robotic manipulation modelling

The aim of this research is to develop and validate a model-based control approach for robotic manipulation of small, precise objects.

Develop a mathematical model of the robotic system that captures the dynamics of the manipulator and the grasped object.
Design a control algorithm that uses the developed model to achieve stable and accurate grasping of the object.
Test the proposed approach in simulation and validate the results through experiments with a physical robotic system.
Evaluate the performance of the proposed approach in terms of stability, accuracy, and robustness to uncertainties and perturbations.
Identify potential applications and areas for future work in the field of robotic manipulation for precision tasks.

Big data analytics and its impacts on marketing strategy

The aim of this research is to investigate the impact of big data analytics on marketing strategy and to identify best practices for leveraging this technology to inform decision-making.

Review the literature on big data analytics and marketing strategy to identify key trends and challenges
Conduct a case study analysis of companies that have successfully integrated big data analytics into their marketing strategies
Identify the key factors that contribute to the effectiveness of big data analytics in marketing decision-making
Develop a framework for integrating big data analytics into marketing strategy.
Investigate the ethical implications of big data analytics in marketing and suggest best practices for responsible use of this technology.

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Take a review of different varieties of thesis topics and samples from our website TheResearchGuardian.com on multiple subjects for every educational level.

Platforms for large scale data computing: big data analysis and acceptance

To investigate the performance and scalability of different large-scale data computing platforms.

To compare the features and capabilities of different platforms and determine which is most suitable for a given use case.
To identify best practices for using these platforms, including considerations for data management, security, and cost.
To explore the potential for integrating these platforms with other technologies and tools for data analysis and visualization.
To develop case studies or practical examples of how these platforms have been used to solve real-world data analysis challenges.

Distributed data clustering

Distributed data clustering can be a useful approach for analyzing and understanding complex datasets, as it allows for the identification of patterns and relationships that may not be immediately apparent.

To develop and evaluate new algorithms for distributed data clustering that is efficient and scalable.

To compare the performance and accuracy of different distributed data clustering algorithms on a variety of datasets.
To investigate the impact of different parameters and settings on the performance of distributed data clustering algorithms.
To explore the potential for integrating distributed data clustering with other machine learning and data analysis techniques.
To apply distributed data clustering to real-world problems and evaluate its effectiveness.

Analyzing and predicting urbanization patterns using GIS and data mining techniques".

The aim of this project is to use GIS and data mining techniques to analyze and predict urbanization patterns in a specific region.

To collect and process relevant data on urbanization patterns, including population density, land use, and infrastructure development, using GIS tools.
To apply data mining techniques, such as clustering and regression analysis, to identify trends and patterns in the data.
To use the results of the data analysis to develop a predictive model for urbanization patterns in the region.
To present the results of the analysis and the predictive model in a clear and visually appealing way, using GIS maps and other visualization techniques.

Use of big data and IOT in the media industry

Big data and the Internet of Things (IoT) are emerging technologies that are transforming the way that information is collected, analyzed, and disseminated in the media sector. The aim of the research is to understand how big data and IoT re used to dictate information flow in the media industry

Identifying the key ways in which big data and IoT are being used in the media sector, such as for content creation, audience engagement, or advertising.
Analyzing the benefits and challenges of using big data and IoT in the media industry, including factors such as cost, efficiency, and effectiveness.
Examining the ethical and social implications of using big data and IoT in the media sector, including issues such as privacy, security, and bias.
Determining the potential impact of big data and IoT on the media landscape and the role of traditional media in an increasingly digital world.

Exigency computer systems for meteorology and disaster prevention

The research aims to explore the role of exigency computer systems to detect weather and other hazards for disaster prevention and response

Identifying the key components and features of exigency computer systems for meteorology and disaster prevention, such as data sources, analytics tools, and communication channels.
Evaluating the effectiveness of exigency computer systems in providing accurate and timely information about weather and other hazards.
Assessing the impact of exigency computer systems on the ability of decision makers to prepare for and respond to disasters.
Examining the challenges and limitations of using exigency computer systems, such as the need for reliable data sources, the complexity of the systems, or the potential for human error.

Network security and cryptography

Overall, the goal of research is to improve our understanding of how to protect communication and information in the digital age, and to develop practical solutions for addressing the complex and evolving security challenges faced by individuals, organizations, and societies.

Developing new algorithms and protocols for securing communication over networks, such as for data confidentiality, data integrity, and authentication
Investigating the security of existing cryptographic primitives, such as encryption and hashing algorithms, and identifying vulnerabilities that could be exploited by attackers.
Evaluating the effectiveness of different network security technologies and protocols, such as firewalls, intrusion detection systems, and virtual private networks (VPNs), in protecting against different types of attacks.
Exploring the use of cryptography in emerging areas, such as cloud computing, the Internet of Things (IoT), and blockchain, and identifying the unique security challenges and opportunities presented by these domains.
Investigating the trade-offs between security and other factors, such as performance, usability, and cost, and developing strategies for balancing these conflicting priorities.

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10 Best Research and Thesis Topic Ideas for Data Science in 2022

These research and thesis topics for data science will ensure more knowledge and skills for both students and scholars

Handling practical video analytics in a distributed cloud: With increased dependency on the internet, sharing videos has become a mode of data and information exchange. The role of the implementation of the Internet of Things (IoT), telecom infrastructure, and operators is huge in generating insights from video analytics. In this perspective, several questions need to be answered, like the efficiency of the existing analytics systems, the changes about to take place if real-time analytics are integrated, and others.
Smart healthcare systems using big data analytics: Big data analytics plays a significant role in making healthcare more efficient, accessible, and cost-effective. Big data analytics enhances the operational efficiency of smart healthcare providers by providing real-time analytics. It enhances the capabilities of the intelligent systems by using short-span data-driven insights, but there are still distinct challenges that are yet to be addressed in this field.
Identifying fake news using real-time analytics: The circulation of fake news has become a pressing issue in the modern era. The data gathered from social media networks might seem legit, but sometimes they are not. The sources that provide the data are unauthenticated most of the time, which makes it a crucial issue to be addressed.
TOP 10 DATA SCIENCE JOB SKILLS THAT WILL BE ON HIGH DEMAND IN 2022
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TOP DATA SCIENCE PROJECTS TO DO DURING YOUR OMICRON QUARANTINE
Secure federated learning with real-world applications : Federated learning is a technique that trains an algorithm across multiple decentralized edge devices and servers. This technique can be adopted to build models locally, but if this technique can be deployed at scale or not, across multiple platforms with high-level security is still obscure.
Big data analytics and its impact on marketing strategy : The advent of data science and big data analytics has entirely redefined the marketing industry. It has helped enterprises by offering valuable insights into their existing and future customers. But several issues like the existence of surplus data, integrating complex data into customers’ journeys, and complete data privacy are some of the branches that are still untrodden and need immediate attention.
Impact of big data on business decision-making: Present studies signify that big data has transformed the way managers and business leaders make critical decisions concerning the growth and development of the business. It allows them to access objective data and analyse the market environments, enabling companies to adapt rapidly and make decisions faster. Working on this topic will help students understand the present market and business conditions and help them analyse new solutions.
Implementing big data to understand consumer behaviour : In understanding consumer behaviour, big data is used to analyse the data points depicting a consumer’s journey after buying a product. Data gives a clearer picture in understanding specific scenarios. This topic will help understand the problems that businesses face in utilizing the insights and develop new strategies in the future to generate more ROI.
Applications of big data to predict future demand and forecasting : Predictive analytics in data science has emerged as an integral part of decision-making and demand forecasting. Working on this topic will enable the students to determine the significance of the high-quality historical data analysis and the factors that drive higher demand in consumers.
The importance of data exploration over data analysis : Exploration enables a deeper understanding of the dataset, making it easier to navigate and use the data later. Intelligent analysts must understand and explore the differences between data exploration and analysis and use them according to specific needs to fulfill organizational requirements.
Data science and software engineering : Software engineering and development are a major part of data science. Skilled data professionals should learn and explore the possibilities of the various technical and software skills for performing critical AI and big data tasks.

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10 Compelling Machine Learning Ph.D. Dissertations for 2020

Machine Learning Modeling Research posted by Daniel Gutierrez, ODSC August 19, 2020 Daniel Gutierrez, ODSC

As a data scientist, an integral part of my work in the field revolves around keeping current with research coming out of academia. I frequently scour arXiv.org for late-breaking papers that show trends and reveal fertile areas of research. Other sources of valuable research developments are in the form of Ph.D. dissertations, the culmination of a doctoral candidate’s work to confer his/her degree. Ph.D. candidates are highly motivated to choose research topics that establish new and creative paths toward discovery in their field of study. Their dissertations are highly focused on a specific problem. If you can find a dissertation that aligns with your areas of interest, consuming the research is an excellent way to do a deep dive into the technology. After reviewing hundreds of recent theses from universities all over the country, I present 10 machine learning dissertations that I found compelling in terms of my own areas of interest.

[Related article: Introduction to Bayesian Deep Learning ]

I hope you’ll find several that match your own fields of inquiry. Each thesis may take a while to consume but will result in hours of satisfying summer reading. Enjoy!

1. Bayesian Modeling and Variable Selection for Complex Data

As we routinely encounter high-throughput data sets in complex biological and environmental research, developing novel models and methods for variable selection has received widespread attention. This dissertation addresses a few key challenges in Bayesian modeling and variable selection for high-dimensional data with complex spatial structures.

2. Topics in Statistical Learning with a Focus on Large Scale Data

Big data vary in shape and call for different approaches. One type of big data is the tall data, i.e., a very large number of samples but not too many features. This dissertation describes a general communication-efficient algorithm for distributed statistical learning on this type of big data. The algorithm distributes the samples uniformly to multiple machines, and uses a common reference data to improve the performance of local estimates. The algorithm enables potentially much faster analysis, at a small cost to statistical performance.

Another type of big data is the wide data, i.e., too many features but a limited number of samples. It is also called high-dimensional data, to which many classical statistical methods are not applicable.

This dissertation discusses a method of dimensionality reduction for high-dimensional classification. The method partitions features into independent communities and splits the original classification problem into separate smaller ones. It enables parallel computing and produces more interpretable results.

3. Sets as Measures: Optimization and Machine Learning

The purpose of this machine learning dissertation is to address the following simple question:

How do we design efficient algorithms to solve optimization or machine learning problems where the decision variable (or target label) is a set of unknown cardinality?

Optimization and machine learning have proved remarkably successful in applications requiring the choice of single vectors. Some tasks, in particular many inverse problems, call for the design, or estimation, of sets of objects. When the size of these sets is a priori unknown, directly applying optimization or machine learning techniques designed for single vectors appears difficult. The work in this dissertation shows that a very old idea for transforming sets into elements of a vector space (namely, a space of measures), a common trick in theoretical analysis, generates effective practical algorithms.

4. A Geometric Perspective on Some Topics in Statistical Learning

Modern science and engineering often generate data sets with a large sample size and a comparably large dimension which puts classic asymptotic theory into question in many ways. Therefore, the main focus of this dissertation is to develop a fundamental understanding of statistical procedures for estimation and hypothesis testing from a non-asymptotic point of view, where both the sample size and problem dimension grow hand in hand. A range of different problems are explored in this thesis, including work on the geometry of hypothesis testing, adaptivity to local structure in estimation, effective methods for shape-constrained problems, and early stopping with boosting algorithms. The treatment of these different problems shares the common theme of emphasizing the underlying geometric structure.

5. Essays on Random Forest Ensembles

A random forest is a popular machine learning ensemble method that has proven successful in solving a wide range of classification problems. While other successful classifiers, such as boosting algorithms or neural networks, admit natural interpretations as maximum likelihood, a suitable statistical interpretation is much more elusive for a random forest. The first part of this dissertation demonstrates that a random forest is a fruitful framework in which to study AdaBoost and deep neural networks. The work explores the concept and utility of interpolation, the ability of a classifier to perfectly fit its training data. The second part of this dissertation places a random forest on more sound statistical footing by framing it as kernel regression with the proximity kernel. The work then analyzes the parameters that control the bandwidth of this kernel and discuss useful generalizations.

6. Marginally Interpretable Generalized Linear Mixed Models

A popular approach for relating correlated measurements of a non-Gaussian response variable to a set of predictors is to introduce latent random variables and fit a generalized linear mixed model. The conventional strategy for specifying such a model leads to parameter estimates that must be interpreted conditional on the latent variables. In many cases, interest lies not in these conditional parameters, but rather in marginal parameters that summarize the average effect of the predictors across the entire population. Due to the structure of the generalized linear mixed model, the average effect across all individuals in a population is generally not the same as the effect for an average individual. Further complicating matters, obtaining marginal summaries from a generalized linear mixed model often requires evaluation of an analytically intractable integral or use of an approximation. Another popular approach in this setting is to fit a marginal model using generalized estimating equations. This strategy is effective for estimating marginal parameters, but leaves one without a formal model for the data with which to assess quality of fit or make predictions for future observations. Thus, there exists a need for a better approach.

This dissertation defines a class of marginally interpretable generalized linear mixed models that leads to parameter estimates with a marginal interpretation while maintaining the desirable statistical properties of a conditionally specified model. The distinguishing feature of these models is an additive adjustment that accounts for the curvature of the link function and thereby preserves a specific form for the marginal mean after integrating out the latent random variables.

7. On the Detection of Hate Speech, Hate Speakers and Polarized Groups in Online Social Media

The objective of this dissertation is to explore the use of machine learning algorithms in understanding and detecting hate speech, hate speakers and polarized groups in online social media. Beginning with a unique typology for detecting abusive language, the work outlines the distinctions and similarities of different abusive language subtasks (offensive language, hate speech, cyberbullying and trolling) and how we might benefit from the progress made in each area. Specifically, the work suggests that each subtask can be categorized based on whether or not the abusive language being studied 1) is directed at a specific individual, or targets a generalized “Other” and 2) the extent to which the language is explicit versus implicit. The work then uses knowledge gained from this typology to tackle the “problem of offensive language” in hate speech detection.

8. Lasso Guarantees for Dependent Data

Serially correlated high dimensional data are prevalent in the big data era. In order to predict and learn the complex relationship among the multiple time series, high dimensional modeling has gained importance in various fields such as control theory, statistics, economics, finance, genetics and neuroscience. This dissertation studies a number of high dimensional statistical problems involving different classes of mixing processes.

9. Random forest robustness, variable importance, and tree aggregation

Random forest methodology is a nonparametric, machine learning approach capable of strong performance in regression and classification problems involving complex data sets. In addition to making predictions, random forests can be used to assess the relative importance of feature variables. This dissertation explores three topics related to random forests: tree aggregation, variable importance, and robustness.

10. Climate Data Computing: Optimal Interpolation, Averaging, Visualization and Delivery

This dissertation solves two important problems in the modern analysis of big climate data. The first is the efficient visualization and fast delivery of big climate data, and the second is a computationally extensive principal component analysis (PCA) using spherical harmonics on the Earth’s surface. The second problem creates a way to supply the data for the technology developed in the first. These two problems are computationally difficult, such as the representation of higher order spherical harmonics Y400, which is critical for upscaling weather data to almost infinitely fine spatial resolution.

I hope you enjoyed learning about these compelling machine learning dissertations.

Editor’s note: Interested in more data science research? Check out the Research Frontiers track at ODSC Europe this September 17-19 or the ODSC West Research Frontiers track this October 27-30.

Daniel Gutierrez, ODSC

Daniel D. Gutierrez is a practicing data scientist who’s been working with data long before the field came in vogue. As a technology journalist, he enjoys keeping a pulse on this fast-paced industry. Daniel is also an educator having taught data science, machine learning and R classes at the university level. He has authored four computer industry books on database and data science technology, including his most recent title, “Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R.” Daniel holds a BS in Mathematics and Computer Science from UCLA.

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Dissertations / Theses on the topic 'Big data analytics and tools'

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Al-Shiakhli, Sarah. "Big Data Analytics: A Literature Review Perspective." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-74173.

Carrascosa, Baena María Carmen 1972. "Next generation of informatics tools for big data analytics in drug discovery." Doctoral thesis, Universitat Pompeu Fabra, 2018. http://hdl.handle.net/10803/586011.

Nováková, Martina. "Analýza Big Data v oblasti zdravotnictví." Master's thesis, Vysoká škola ekonomická v Praze, 2014. http://www.nusl.cz/ntk/nusl-201737.

Svenningsson, Philip, and Maximilian Drubba. "How to capture that business value everyone talks about? : An exploratory case study on business value in agile big data analytics organizations." Thesis, Internationella Handelshögskolan, Jönköping University, IHH, Företagsekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-48882.

Velthuis, Paul. "New authentication mechanism using certificates for big data analytic tools." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215694.

Shoop, Alexander. "Identifying and Evaluating Early Stage Fintech Companies: Working with Consumer Internet Data and Analytic Tools." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/133.

Dymov, Khasan. "Identifying and Evaluating Early Stage Fintech Companies: Working with Consumer Internet Data and Analytic Tools." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/132.

Doucet, Rachel A., Deyan M. Dontchev, Javon S. Burden, and Thomas L. Skoff. "Big data analytics test bed." Thesis, Monterey, California: Naval Postgraduate School, 2013. http://hdl.handle.net/10945/37615.

Miloš, Marek. "Nástroje pro Big Data Analytics." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-199274.

Erlandsson, Niklas. "Game Analytics och Big Data." Thesis, Mittuniversitetet, Avdelningen för arkiv- och datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-29185.

Sun, Mingyang. "Big data analytics in power systems." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/45061.

Katzenbach, Alfred, and Holger Frielingsdorf. "Big Data Analytics für die Produktentwicklung." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-214517.

Bitto, Nicholas. "Adding big data analytics to GCSS-MC." Thesis, Monterey, California: Naval Postgraduate School, 2014. http://hdl.handle.net/10945/43879.

TANNEEDI, NAREN NAGA PAVAN PRITHVI. "Customer Churn Prediction Using Big Data Analytics." Thesis, Blekinge Tekniska Högskola, Institutionen för kommunikationssystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13518.

Le, Quoc Do. "Approximate Data Analytics Systems." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-234219.

Leis, Machín Angela 1974. "Studying depression through big data analytics on Twitter." Doctoral thesis, TDX (Tesis Doctorals en Xarxa), 2021. http://hdl.handle.net/10803/671365.

Brydon, Humphrey Charles. "Missing imputation methods explored in big data analytics." University of the Western Cape, 2018. http://hdl.handle.net/11394/6605.

Oikonomidi, Sofia. "Impact of Big Data Analytics in Industry 4.0." Thesis, Linnéuniversitetet, Institutionen för informatik (IK), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-99443.

Rystadius, Gustaf, David Monell, and Linus Mautner. "The dynamic management revolution of Big Data : A case study of Åhlen’s Big Data Analytics operation." Thesis, Jönköping University, Internationella Handelshögskolan, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-48959.

Zhang, Liangwei. "Big Data Analytics for eMaintenance : Modeling of high-dimensional data streams." Licentiate thesis, Luleå tekniska universitet, Drift, underhåll och akustik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-17012.

Hellström, Elin, and My Hemlin. "Det binära guldet : en uppsats om big data och analytics." Thesis, Uppsala universitet, Informationssystem, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-205900.

Talevi, Iacopo. "Big Data Analytics and Application Deployment on Cloud Infrastructure." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14408/.

MA, YIXIAO. "Big Data Analytics of City Wide Building Energy Declarations." Thesis, KTH, Industriell ekologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-165080.

Moran, Andrew M. Eng Massachusetts Institute of Technology. "Improving big data visual analytics with interactive virtual reality." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105972.

Jun, Sang-Woo. "Scalable multi-access flash store for Big Data analytics." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/87947.

Palummo, Alexandra Lina. "Supporto SQL al sistema Hadoop per big data analytics." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2016.

Alaka, H. A. "'Big data analytics' for construction firms insolvency prediction models." Thesis, University of the West of England, Bristol, 2017. http://eprints.uwe.ac.uk/30714/.

Olsén, Cleas, and Gustav Lindskog. "Big Data Analytics : A potential way to Competitive Performance." Thesis, Linnéuniversitetet, Institutionen för informatik (IK), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-104372.

Mathias, Henry. "Analyzing Small Businesses' Adoption of Big Data Security Analytics." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/6614.

Vahedian, Khezerlou Amin. "Mining big mobility data for large urban event analytics." Diss., University of Iowa, 2019. https://ir.uiowa.edu/etd/7039.

Hahmann, Martin, Claudio Hartmann, Lars Kegel, Dirk Habich, and Wolfgang Lehner. "Big by blocks: Modular Analytics." De Gruyter, 2016. https://tud.qucosa.de/id/qucosa%3A72848.

Cao, Lei. "Outlier Detection In Big Data." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/82.

Plevoets, Christina, and Rodrigo Fernandes. "Exploring the role of Big Data and Analytics : Creating Data-Driven Innovation." Thesis, Blekinge Tekniska Högskola, Institutionen för industriell ekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13463.

Abounia, Omran Behzad. "Application of Data Mining and Big Data Analytics in the Construction Industry." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu148069742849934.

Singh, Shailendra. "Smart Meters Big Data : Behavioral Analytics via Incremental Data Mining and Visualization." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35244.

Jun, Sang-Woo. "Big data analytics made affordable using hardware-accelerated flash storage." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/118088.

Niland, Michael John. "Toward the influence of the organisation on big data analytics." Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/64902.

Stevens, Melissa Anine. "Creating value from big data and analytics : a leader's perspective." Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/64819.

Bin, Saip Mohamed A. "Big Social Data Analytics: A Model for the Public Sector." Thesis, University of Bradford, 2019. http://hdl.handle.net/10454/18352.

Khan, Mukhtaj. "Hadoop performance modeling and job optimization for big data analytics." Thesis, Brunel University, 2015. http://bura.brunel.ac.uk/handle/2438/11078.

Stouten, Floris. "Big data analytics attack detection for Critical Information Infrastructure Protection." Thesis, Luleå tekniska universitet, Datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-59562.

Zhang, Liangwei. "Big Data Analytics for Fault Detection and its Application in Maintenance." Doctoral thesis, Luleå tekniska universitet, Drift, underhåll och akustik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-60423.

Saenyi, Betty. "Opportunities and challenges of Big Data Analytics in healthcare : An exploratory study on the adoption of big data analytics in the Management of Sickle Cell Anaemia." Thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Informatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-42864.

Gardoni, Pietro. "Big Data Analytics: il valore delle informazioni nella strategia e nell'organizzazione aziendale." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

Shukla, Soumya. "Study of big data analytics landscape : considerations for market entry of an E-commerce analytics vendor." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104515.

Ajiboye, Soladoye Oyebowale. "Video big data : an agile architecture for systematic exploration and analytics." Thesis, University of Sussex, 2017. http://sro.sussex.ac.uk/id/eprint/71047/.

Collerà, Alessandro. "Classificazione e selezione di tecniche di visualizzazione per Big Data Analytics." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10968/.

Kamw, Farah Shleemon. "UTILIZING BIG TRAJECTORY DATA FOR URBAN VISUAL ANALYTICS AND ACCESSIBILITY STUDIES." Kent State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=kent1555254527369841.

Fors, Anton, and Emelie Ohlson. "Business analytics in traditional industries – tackling the new age of data and analytics." Thesis, Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-10450.

Santos, Rivera Juan De Dios. "Data Analysis on Hadoop - finding tools and applications for Big Data challenges." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-260557.

1000+ FREE Research Topics & Title Ideas

If you’re at the start of your research journey and are trying to figure out which research topic you want to focus on, you’ve come to the right place. Select your area of interest below to view a comprehensive collection of potential research ideas.

Research Topic FAQs

What (exactly) is a research topic.

A research topic is the subject of a research project or study – for example, a dissertation or thesis. A research topic typically takes the form of a problem to be solved, or a question to be answered.

A good research topic should be specific enough to allow for focused research and analysis. For example, if you are interested in studying the effects of climate change on agriculture, your research topic could focus on how rising temperatures have impacted crop yields in certain regions over time.

To learn more about the basics of developing a research topic, consider our free research topic ideation webinar.

What constitutes a good research topic?

A strong research topic comprises three important qualities : originality, value and feasibility.

Originality – a good topic explores an original area or takes a novel angle on an existing area of study.
Value – a strong research topic provides value and makes a contribution, either academically or practically.
Feasibility – a good research topic needs to be practical and manageable, given the resource constraints you face.

To learn more about what makes for a high-quality research topic, check out this post .

What's the difference between a research topic and research problem?

A research topic and a research problem are two distinct concepts that are often confused. A research topic is a broader label that indicates the focus of the study , while a research problem is an issue or gap in knowledge within the broader field that needs to be addressed.

To illustrate this distinction, consider a student who has chosen “teenage pregnancy in the United Kingdom” as their research topic. This research topic could encompass any number of issues related to teenage pregnancy such as causes, prevention strategies, health outcomes for mothers and babies, etc.

Within this broad category (the research topic) lies potential areas of inquiry that can be explored further – these become the research problems . For example:

What factors contribute to higher rates of teenage pregnancy in certain communities?
How do different types of parenting styles affect teen pregnancy rates?
What interventions have been successful in reducing teenage pregnancies?

Simply put, a key difference between a research topic and a research problem is scope ; the research topic provides an umbrella under which multiple questions can be asked, while the research problem focuses on one specific question or set of questions within that larger context.

How can I find potential research topics for my project?

There are many steps involved in the process of finding and choosing a high-quality research topic for a dissertation or thesis. We cover these steps in detail in this video (also accessible below).

How can I find quality sources for my research topic?

Finding quality sources is an essential step in the topic ideation process. To do this, you should start by researching scholarly journals, books, and other academic publications related to your topic. These sources can provide reliable information on a wide range of topics. Additionally, they may contain data or statistics that can help support your argument or conclusions.

Identifying Relevant Sources

When searching for relevant sources, it’s important to look beyond just published material; try using online databases such as Google Scholar or JSTOR to find articles from reputable journals that have been peer-reviewed by experts in the field.

You can also use search engines like Google or Bing to locate websites with useful information about your topic. However, be sure to evaluate any website before citing it as a source—look for evidence of authorship (such as an “About Us” page) and make sure the content is up-to-date and accurate before relying on it.

Evaluating Sources

Once you’ve identified potential sources for your research project, take some time to evaluate them thoroughly before deciding which ones will best serve your purpose. Consider factors such as author credibility (are they an expert in their field?), publication date (is the source current?), objectivity (does the author present both sides of an issue?) and relevance (how closely does this source relate to my specific topic?).

By researching the current literature on your topic, you can identify potential sources that will help to provide quality information. Once you’ve identified these sources, it’s time to look for a gap in the research and determine what new knowledge could be gained from further study.

How can I find a good research gap?

Finding a strong gap in the literature is an essential step when looking for potential research topics. We explain what research gaps are and how to find them in this post.

How should I evaluate potential research topics/ideas?

When evaluating potential research topics, it is important to consider the factors that make for a strong topic (we discussed these earlier). Specifically:

Originality
Feasibility

So, when you have a list of potential topics or ideas, assess each of them in terms of these three criteria. A good topic should take a unique angle, provide value (either to academia or practitioners), and be practical enough for you to pull off, given your limited resources.

Finally, you should also assess whether this project could lead to potential career opportunities such as internships or job offers down the line. Make sure that you are researching something that is relevant enough so that it can benefit your professional development in some way. Additionally, consider how each research topic aligns with your career goals and interests; researching something that you are passionate about can help keep motivation high throughout the process.

How can I assess the feasibility of a research topic?

When evaluating the feasibility and practicality of a research topic, it is important to consider several factors.

First, you should assess whether or not the research topic is within your area of competence. Of course, when you start out, you are not expected to be the world’s leading expert, but do should at least have some foundational knowledge.

Time commitment

When considering a research topic, you should think about how much time will be required for completion. Depending on your field of study, some topics may require more time than others due to their complexity or scope.

Additionally, if you plan on collaborating with other researchers or institutions in order to complete your project, additional considerations must be taken into account such as coordinating schedules and ensuring that all parties involved have adequate resources available.

Resources needed

It’s also critically important to consider what type of resources are necessary in order to conduct the research successfully. This includes physical materials such as lab equipment and chemicals but can also include intangible items like access to certain databases or software programs which may be necessary depending on the nature of your work. Additionally, if there are costs associated with obtaining these materials then this must also be factored into your evaluation process.

Potential risks

It’s important to consider the inherent potential risks for each potential research topic. These can include ethical risks (challenges getting ethical approval), data risks (not being able to access the data you’ll need), technical risks relating to the equipment you’ll use and funding risks (not securing the necessary financial back to undertake the research).

If you’re looking for more information about how to find, evaluate and select research topics for your dissertation or thesis, check out our free webinar here . Alternatively, if you’d like 1:1 help with the topic ideation process, consider our private coaching services .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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M.Tech/Ph.D Thesis Help in Chandigarh | Thesis Guidance in Chandigarh

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These days the internet is being widely used than it was used a few years back. It has become a core part of our life. Billions of people are using social media and social networking every day all across the globe. Such a huge number of people generate a flood of data which have become quite complex to manage. Considering this enormous data, a term has been coined to represent it. So, what is this term called? Yes, Big Data Big Data is the term coined to refer to this huge amount of data. The concept of big data is fast spreading its arms all over the world. It is a trending topic for thesis, project, research, and dissertation. There are various good topics for the master’s thesis and research in Big Data and Hadoop as well as for Ph.D. First of all know, what is big data and Hadoop?

Find the link at the end to download the latest thesis and research topics in Big Data

What is Big Data?

Big Data refers to the large volume of data which may be structured or unstructured and which make use of certain new technologies and techniques to handle it. An organized form of data is known as structured data while an unorganized form of data is known as unstructured data. The data sets in big data are so large and complex that we cannot handle them using traditional application software. There are certain frameworks like Hadoop designed for processing big data. These techniques are also used to extract useful insights from data using predictive analysis, user behavior, and analytics. You can explore more on big data introduction while working on the thesis in Big Data. Big Data is defined by three Vs:

Volume – It refers to the amount of data that is generated. The data can be low-density, high volume, structured/unstructured or data with unknown value. This unknown data is converted into useful one using technologies like Hadoop. The data can range from terabytes to petabytes. Velocity – It refers to the rate at which the data is generated. The data is received at an unprecedented speed and is acted upon in a timely manner. It also requires real-time evaluation and action in case of the Internet of Things(IoT) applications. Variety – Variety refers to different formats of data. It may be structured, unstructured or semi-structured. The data can be audio, video, text or email. In this additional processing is required to derive the meaning of data and also to support the metadata. In addition to these three Vs of data, following Vs are also defined in big data. Value – Each form of data has some value which needs to be discovered. There are certain qualitative and quantitative techniques to derive meaning from data. For deriving value from data, certain new discoveries and techniques are required. Variability – Another dimension for big data is the variability of data i.e the flow of data can be high or low. There are challenges in managing this flow of data.

Thesis Research Topics in Big Data

Privacy, Security Issues in Big Data .
Storage Systems of Scalable for Big Data .
Massive Big Data Processing of Software and Tools.
Techniques and Data Mining Tools for Big Data .
Big Data Adoptation and Analytics of Cloud Computing Platforms.
Scalable Architectures for Parallel Data Processing.

Can you imagine how big is big data? Of course, you can’t. The amount of big data that is generated and stored on a global scale is unbelievable and is growing day by day. But do you know, only a small portion of this data is actually analyzed mainly for getting useful insights and information?

Big Data Hadoop

Hadoop is an open-source framework provided to process and store big data. Hadoop makes use of simple programming models to process big data in a distributed environment across clusters of computers. Hadoop provides storage for a large volume of data along with advanced processing power. It also gives the ability to handle multiple tasks and jobs.

Big Data Hadoop Architecture

HDFS is the main component of Hadoop architecture. It stands for Hadoop Distributed File Systems. It is used to store a large amount of data and multiple machines are used for this storage. MapReduce Overview is another component of big data architecture. The data is processed here in a distributed manner across multiple machines. YARN component is used for data processing resources like CPU, RAM, and memory. Resource Manager and Node Manager are the elements of YARN. These two elements work as master and slave. Resource Manager is the master and assigns resources to the slave i.e. Node Manager. Node Manager sends the signal to the master when it is going to start the work. Big Data Hadoop for the thesis will be plus point for you.

Importance of Hadoop in big data

Hadoop is essential especially in terms of big data . The importance of Hadoop is highlighted in the following points: Processing of huge chunks of data – With Hadoop, we can process and store huge amount of data mainly the data from social media and IoT(Internet of Things) applications. Computation power – The computation power of Hadoop is high as it can process big data pretty fast. Hadoop makes use of distributed models for processing of data. Fault tolerance – Hadoop provide protection against any form of malware as well as from hardware failure. If a node in the distributed model goes down, then other nodes continue to function. Copies of data are also stored. Flexibility – As much data as you require can be stored using Hadoop. There is no requirement of preprocessing the data. Low Cost – Hadoop is an open-source framework and free to use. It provides additional hardware to store the large quantities of data. Scalability – The system can be grown easily just by adding nodes in the system according to the requirements. Minimal administration is required.

Challenges of Hadoop

No doubt Hadoop is a very good platform for big data solution, still, there are certain challenges in this.

These challenges are:

All problems cannot be solved – It is not suitable for iteration and interaction tasks. Instead, it is efficient for simple problems for which division into independent units can be made.
Talent Gap – There is a lack of talented and skilled programmers in the field of MapReduce in big data especially at entry level.
Security of data – Another challenge is the security of data. Kerberos authentication protocol has been developed to provide a solution to data security issues.
Lack of tools – There is a lack of tools for data cleaning, management, and governance. Tools for data quality and standardization are also lacking.

Fields under Big Data

Big Data is a vast field and there are a number of topics and fields under it on which you can work for your thesis, dissertation as well as for research. Big Data is just an umbrella term for these fields.

Search Engine Data – It refers to the data stored in the search engines like Google, Bing and is retrieved from different databases. Social Media Data – It is a collection of data from social media platforms like Facebook, Twitter. Stock Exchange Data – It is a data from companies indulged into shares business in the stock market. Black box Data – Black Box is a component of airplanes, helicopters for voice recording of fight crew and for other metrics.

Big Data Technologies

Big Data technologies are required for more detailed analysis, accuracy and concrete decision making. It will lead to more efficiency, less cost, and less risk. For this, a powerful infrastructure is required to manage and process huge volumes of data.

The data can be analyzed with techniques like A/B Testing, Machine Learning, and Natural Language Processing.

The big data technologies include business intelligence, cloud computing, and databases.

The visualization of data can be done through the medium of charts and graphs.

Multi-dimensional big data can be handled through tensor-based computation. Tensor-based computation makes use of linear relations in the form of scalars and vectors. Other technologies that can be applied to big data are:

Massively Parallel Processing Search based applications Data Mining Distributed databases Cloud Computing

These technologies are provided by vendors like Amazon, Microsoft, IBM etc to manage the big data.

MapReduce Algorithm for Big Data

A large amount of data cannot be processed using traditional data processing approaches. This problem has been solved by Google using an algorithm known as the MapReduce algorithm. Using this algorithm, the task can be divided into small parts and these parts are assigned to distributed computers connected on the network. The data is then collected from individual computers to form a final dataset.

The MapReduce algorithm is used by Hadoop to run applications in which parallel processing of data is done on different nodes. Hadoop framework can develop applications that can run on clusters of computers to perform statistical analysis of a large amount of data.

The MapReduce algorithm consist of two tasks: Map Reduce

A set is of data is taken by Map which is converted into another set of data in which individual elements are broken into pairs known as tuples. Reduce takes the output of Map task as input. It combines data tuples into smaller tuples set.

The MapReduce algorithm is executed in three stages: Map Shuffle Reduce

In the map stage, the input data is processed and stored in the Hadoop file system(HDFS). After this a mapper performs the processing of data to create small chunks of data. Shuffle stage and Reduce stage occur in combination. The Reducer takes the input from the mapper for processing to create a new set of output which will later be stored in the HDFS. The Map and Reduce tasks are assigned to appropriate servers in the cluster by the Hadoop. The Hadoop framework manages all the details like issuing of tasks, verification, and copying. After completion, the data is collected at the Hadoop server. You can get thesis and dissertation guidance for the thesis in Big Data Hadoop from data analyst.

Applications of Big Data

Big Data find its application in various areas including retail, finance, digital media, healthcare, customer services etc.

Big Data is used within governmental services with efficiency in cost, productivity, and innovation. The common example of this is the Indian Elections of 2014 in which BJP tried this to win the elections. The data analysis, in this case, can be done by the collaboration between the local and the central government. Big Data was the major factor behind Barack Obama’s win in the 2012 election campaign.

Big Data is used in finance for market prediction. It is used for compliance and regulatory reporting, risk analysis, fraud detection, high-speed trading and for analytics. The data which is used for market prediction is known as alternate data.

Big Data is used in health care services for clinical data analysis, disease pattern analysis, medical devices and medicines supply, drug discovery and various other such analytics. Big Data analytics have helped in a major way in improving the healthcare systems. Using these certain technologies have been developed in healthcare systems like eHealth, mHealth, and wearable health gadgets.

Media uses Big Data for various mechanisms like ad targeting, forecasting, clickstream analytics, campaign management and loyalty programs. It is mainly focused on following three points:

Targeting consumers Capturing of data Data journalism

Big Data is a core of IoT(Internet of Things) . They both work together. Data can be extracted from IoT devices for mapping which helps in interconnectivity. This mapping can be used to target customers and for media efficiency by the media industry.

Information Technology

Big Data has helped employees working in Information Technology to work efficiently and for widespread distribution of Information Technology. Certain issues in Information Technology can also be resolved using Big Data. Big Data principles can be applied to machine learning and artificial intelligence for providing better solutions to the problems.

Advantages of Big Data

Big Data has certain advantages and benefits, particularly for big organizations.

Time Management – Big data saves valuable time as rather than spending hours on managing the different amount of data, big data can be managed efficiently and at a faster pace.
Accessibility – Big Data is easily accessible through authorization and data access rights and privileges.
Trustworthy – Big Data is trustworthy in the sense that we can get valuable insights from the data.
Relevant – The data is relevant whereas irrelevant data require filtering which can lead to complexity.
Secure – The data is secured using data hosting and through various advanced technologies and techniques.

Challenges of Big Data

Although Big Data has come in a big way in improving the way we store data, there are certain challenges which need to be resolved.

Data Storage and quality of Data – The data is growing at a fast pace as the number of companies and organizations are growing. Proper storage of this data has become a challenge. This data can be stored in data warehouses but this data is inconsistent. There are issues of errors, duplicacy, conflicts while storing this data in their native format. Moreover, this changes the quality of data.
Lack of big data analysts – There is a huge demand for data scientists and analysts who can understand and analyze this data. But there are very few people who can work in this field considering the fact that huge amount of data is produced every day. Those who are there don’t have proper skills.
Quality Analysis – Big companies and organizations use big for getting useful insights to make proper decisions for future plans. The data should also be accurate as inaccurate data can lead to wrong decisions that will affect the company business. Therefore quality analysis of the data should be there. For this testing is required which is a time-consuming process and also make use of expensive tools.
Security and Privacy of Data – Security, and privacy are the biggest risks in big data. The tools that are used for analyzing, storing, managing use data from different sources. This makes data vulnerable to exposure. It increases security and privacy concerns.

Thus Big Data is providing a great help to companies and organizations to make better decisions. This will ultimately lead to more profit. The main thesis topics in Big Data and Hadoop include applications, architecture, Big Data in IoT, MapReduce, Big Data Maturity Model etc.

Latest Thesis and Research Topics in Big Data

There are a various thesis and research topics in big data for M.Tech and Ph.D. Following is the list of good topics for big data for masters thesis and research:

Big Data Virtualization

Internet of Things(IoT)

Big Data Maturity Model

Data Science

Data Federation

Big Data Analytics

SQL-on-Hadoop

Predictive Analytics

Big Data Virtualization is the process of creating virtual structures rather than actual for Big Data systems. It is very beneficial for big enterprises and organizations to use their data assets to achieve their goals and objectives. Virtualization tools are available to handle big data analytics.

Big Data and IoT work in coexistence with each other. IoT devices capture data which is extracted for connectivity of devices. IoT devices have sensors to sense data from its surroundings and can act according to its surrounding environment.

Big Data Maturity Models are used to measure the maturity of big data. These models help organizations to measure big data capabilities and also assist them to create a structure around that data. The main goal of these models is to guide organizations to set their development goals.

Data Science is more or less related to Data Mining in which valuable insights and information are extracted from data both structured and unstructured. Data Science employs techniques and methods from the fields of mathematics, statistics, and computer science for processing.

Data Federation is the process of collecting data from different databases without copying and without transferring the original data. Rather than whole information, data federation collects metadata which is the description of the structure of original data and keep them in a single database.

Sampling is a technique of statistics to find and locate patterns in Big Data. Sampling makes it possible for the data scientists to work efficiently with a manageable amount of data. Sampled data can be used for predictive analytics. Data can be represented accurately when a large sample of data is used.

It is the process of exploring large datasets for the sake of finding hidden patterns and underlying relations for valuable customer insights and other useful information. It finds its application in various areas like finance, customer services etc. It is a good choice for Ph.D. research in big data analytics.

Clustering is a technique to analyze big data. In clustering, a group of similar objects is grouped together according to their similarities and characteristics. In other words, this technique partitions the data into different sets. The partitioning can be hard partitioning and soft partitioning. There are various algorithms designed for big data and data mining. It is a good area for thesis and researh in big data.

SQL-on-Hadoop is a methodology for implementing SQL on Hadoop platform by combining together the SQL-style querying system to the new components of the Hadoop framework. There are various ways to execute SQL in Hadoop environment which include – connectors for translating the SQL into a MapReduce format, push down systems to execute SQL in Hadoop clusters, systems that distribute the SQL work between MapReduce – HDFS clusters and raw HDFS clusters. It is a very good topic for thesis and research in Big Data.

It is a technique of extracting information from the datasets that already exist in order to find out the patterns and estimate future trends. Predictive Analytics is the practical outcome of Big Data and Business Intelligence(BI). There are predictive analytics models which are used to get future insights. For this future insight, predictive analytics take into consideration both current and historical data. It is also an interesting topic for thesis and research in Big Data.

These were some of the good topics for big data for M.Tech and masters thesis and research work. For any help on thesis topics in Big Data, contact Techsparks . Call us on this number 91-9465330425 or email us at [email protected] for M.Tech and Ph.D. help in big data thesis topics.

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Latest Thesis and Research Topics in Big Data(pdf)

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Latest Data Science Dissertation Topics to Grab Reader's Attention

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What Is Data Science and Its Importance?

A list of latest data science dissertation topics, how to choose a data science dissertation topic, stuck with data science dissertation topics we can help.

Data science is one of the fastest growing fields in present times, which makes it one of the befitting subjects among students. But, getting a degree in the field is not a piece of cake, as you have to overcome several hurdles. One such problem is to draft an ideal dissertation. Although, creating a paper can be easier when you have a precise topic. Thus, this blog will help you to explore data science dissertation topics to ease your workload. So, to begin with, have an insight into what the data science field is and why it is necessary.

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Data science is a field that studies data to extract valuable insights for a business. In other words, it is a means to use scientific techniques to evaluate and extract meaningful information from the ocean of data. Moreover, as per professional data science dissertation writers, it is an interdisciplinary approach that combines practices and principles of several fields. These subjects are mathematics, statistics, computer engineering, and artificial intelligence. However, you can learn all these in the dissertation writing process, which is a crucial thing in academics. Furthermore, its importance and use is increasing day by day in the field to:

Explore Transformative Patterns

Data science enables a business to explore new relationships and patterns that have the ability to transform the organisation and take it to new heights. Moreover, it can evaluate the low cost of resource management to get higher profits.

Innovation of New Products

Data science has the capability to reveal the gaps and the problems present the existing information that might go unnoticed otherwise. You can do it by evaluating the purchase decisions, consumer preferences, business process and more.

After gaining insight into data science and perceiving its importance, it is time to move ahead. Constructing a dissertation while you are pursuing your academic journey is necessary. Although it is a challenging task, but referring to online dissertation help can guide you on the right track. To move forward, explore the topics you can use to frame your dissertation and impress your professor.

In this section of the blog, you will explore dissertation topics in data science that you can use to build your paper on. These are shortlisted by the experts that will help you leave an impression on your professor and grab your readers' attention. Thus, begin to perceive them all listed by the professional dissertation writers in UK :

Best Data Science Dissertation Topics

Here are the hand-picked dissertation topics for data science that can help you grab the reader's attention quickly and without too much effort.

1. Compare the implementation of data science in various investigations concerning wildfires.

2. Explain the K-means clustering from the perspective of online spherical.

3. Explore how linear and nonlinear regression analyses' efficacy can be increased.

4. Evaluate the platforms for big data computing: Big data analytics and the adoption.

5. Discuss the best data management strategies for modern enterprises to use.

Compelling Data Science Dissertation Topics

Attracting the readers and making them stick to the end of the document is the most challenging task. But, if you have chosen ideal MSC data science dissertation topics, you can ace it easily. Thus, here are some of them:

11. Explain the Hadoop programming and the map-reduce architecture.

12. What is hyper-personalisation and its importance in the field?

13. Explore the value big data provides to innovation management.

14. Perform a comparative study on the implementation of data science in the teaching profession.

15. Overview of data valuation and why it matters in data management.

Data Science Dissertation Topics to Score Well

The motive behind constructing a dissertation is to score well apart from studying the subject. Thus, to make the paper effective, you can either buy dissertation service or select a topic which has the potential to fetch you good grades. So, here are some of the appropriate data science dissertation ideas:

16. Have a discussion about the MATLAB code for decision trees along with semantic data governance.

17. What is the necessity of big data technologies for modern businesses?

18. State the societal implications of using predictive analytics within education.

19. Mention the association rule learning regarding data mining.

20. Give an overview of the relevance of Artificial Intelligence.

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Unique Data Science Dissertation Topics

You must know that uniqueness is the key towards an ideal dissertation. Thus, in this section, you will explore the unique data science dissertation topics that will help you achieve your goal.

21. What is the implementation of data science, and how does it impact the management environment and sustainability?

22. How to apply attribute-access or role-based access control in an organisation?

These are some of the dissertation topics for data science that will help you ease the process of selecting the topic. So, move ahead to know the technique that you can implement and find the perfect data science research topics for your paper.

This section of the blow will help you plan your dissertation topic selection process to smoothen the path. So, read further to perceive the procedure that you should follow while selecting data science dissertation topics:

Pick the Meaningful Topic

As you know, there is a never-ending list of data science dissertation topics you can choose from and build your paper on. But you are opting for the appropriate one within your interest and trending simultaneously. However, if it is challenging for you, check examples of dissertation that can rescue you.

Work with Consistent Data

Due to the variety of data science dissertation topics available, you must choose the one with consistent data. It means some topics do not have an accurate amount of information available to research. So, to ensure that you do not get stuck in the middle, you must ensure that the theme you are opting for has a consistent flow of information.

Ease the Complexity of the Model

While finalising the data science dissertation topics, you need to ensure that it does not have a complex model to work with. It is so because, sometimes, for the sake of uniqueness, students go for the topic with complicated theories. Thus, it makes them struggle and confuses them while creating the paper. So, to ensure a smooth process, you must work on something with lower complexity.

Acknowledge the Day to Day Problems

While selecting a theme, you must keep yourself updated with the daily problems faced by the targeted audience. You can refer to the data science dissertation examples available to understand this better. It is crucial as it will grab the attention of the audience faster, and they can connect with it easily. To do this, enhance your knowledge in the field you are working in.

These were some easy steps that you should adhere to while selecting ideal dissertation topics for data science. So, if you are still struggling with the topics, you can seek professional help.

The data science dissertation topics listed in the blog are more than enough, and you must have found the one that perfectly fits your interest area. However, if you are still stuck with dissertation topic and want to explore more, our team of experts is there. Moreover, we can guide you with other challenging areas that might become a hurdle in your way. So, when you seek our data science dissertation help, you will get the following:

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Dissertation Topics on Data Security and Privacy

Published by Carmen Troy at January 4th, 2023 , Revised On August 16, 2023

For any company and organisation, one of the most important yet sensitive assets is its information. Therefore, it is essential to keep the data secured from getting stolen and avoid getting it used for malicious activities. There are many ways to keep data secure, and in today’s digital, it has become easier to secure data yet too smooth to steal it. However, by employing robust, imperative measures, the data can be secured from harmful encounters.

Anyways, if you are aiming to do your dissertation on data security and privacy or a related topic and are ambiguous about where to start, here is the solution to your problem. Down below is the list of dissertation topics along with their research aim regarding data security and privacy to help you get an idea for your dissertation.

You may also want to start your dissertation by requesting a brief research proposal from our writers on any of these topics, which includes an introduction to the problem, research question , aim and objectives, literature review , along with the proposed methodology of research to be conducted. Let us know if you need any help in getting started.

Check our example dissertation to get an idea of how to structure your dissertation .

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2022 Dissertation Topics on Data Security and Privacy

Topic 1: an investigation of the effectiveness of gdpr in safeguarding the personal data and privacy of e-commerce customers..

Research Aim: The research aims to investigate the effectiveness of GDPR in safeguarding the personal data and privacy of e-commerce customers.

Objectives:

To analyse the extent of data protection offered by GDPR concerning AI and big data.
To evaluate the data security and privacy concerns for e-commerce users.
To investigate the effectiveness of GDPR in safeguarding the personal data and privacy of e-commerce customers.

Topic 2: The data security and privacy challenges associated with Bring Your Own Devices policy in organisations and appropriate mitigation measures.

Research Aim: The research aims to analyse the data security and privacy challenges associated with bringing your own devices policy in organisations and appropriate mitigation measures.

To analyse the concept of bringing your own devices policy in organisations.
To identify the data security and privacy challenges associated with the BYOD policy.
To provide recommendations for improving data security and privacy of organisations and employees in a BYOD context.

Topic 3: The impact of organisational leadership and governance on employee awareness and training about possible cybersecurity attacks.

Research Aim: The research aims to evaluate the impact of organisational leadership and governance on employee awareness and training about possible cybersecurity attacks.

To analyse the impact of leadership on cybersecurity awareness among employees.
To evaluate the influence of corporate governance on employee training policies about data integrity and cybersecurity challenges.
To investigate the impact of organisational leadership and governance on employee awareness and training about possible cybersecurity attacks.

Topic 4: Analysis of the cybersecurity attacks impacting cloud-based ERP systems and recommendations of measures to improve data security and privacy.

Research Aim: The research aims to analyse the cybersecurity attacks impacting cloud-based ERP systems and recommendations of measures to improve data security and privacy.

To analyse the susceptibility of the ERP systems to cybersecurity attacks.
To evaluate the data security mechanisms of cloud-based ERP systems.
To provide recommendations for improving the data security and privacy of cloud-based ERP systems against cybersecurity attacks.

Topic 5: An investigation into the present data security challenges associated with the integration of big data in healthcare with Electronic health systems and available countermeasures.

Research Aim: The research aims to investigate the current data security challenges concerning the integration of big data in healthcare with Electronic health systems and the available countermeasures.

To analyse the data security issues of big data.
To evaluate the susceptibility of Electronic health systems to data security and privacy issues.
To investigate the available countermeasures for the current data security challenges in big data-powered Electronic health systems.

Topic. 1: Blockchain and data security

Research Aim: Blockchain is a decentralised digital record that holds the information of thousands of computer worldwide transactions. The stored data is highly encrypted, and therefore, blockchain technology offers robust security and speeds up the exchange of information cost-effectively.

The aim of the research is to deeply analyse and evaluate blockchain technology for strong data security and how it can be used in corporate settings for boosting security measures.

Topic. 2: Significance of data security and privacy in health care

Research Aim: Keeping the health privacy of the patients is highly important to maintain the follow-up and keep their privacy intact unless they give consent for sharing it with another party. The aim of the research is to find the importance of data security in the health care system by identifying the outcomes of mismanagement of the patient’s health record on the patient and the health institutions.

Topic. 3: Data security and privacy in banks

Research Aim: The aim of the research is to identify the daily challenges that banks have to face in order to keep their highly sensitive data secured and to study what are the ways to keep the data highly secure. It will also study the data security programs that high-profile banks use.

Topic. 4: Why it is imperative for businesses to ensure the data security of their clients

Research Aim: Businesses keep the information of their clients and prospected customers to plan, strategise, and implement their marketing plans. In that regard, they have a bulk of their clients’ information. It is the responsibility of the businesses not to share the information with a third party without the clients’ consent. The aim of the research is to identify the significance of the client’s data security for businesses by analysing their ramifications.

Topic. 5: Challenges of cyberattacks for state organisations

Research Aim: The aim of the research is to identify the daily challenges of cyberattacks that pose to different state-level organisations. It will identify the risks that can threaten the state’s security as a result of the attacks and what are the ways to encounter them.

Topic. 6: Challenges and opportunities of technology for data security and privacy

Research Aim: While technology has geared with the tools to deal with security issues, it has also made it easy for cybercriminals to extract sensitive information. The aim of the research is to identify and explore both sides of technology, advantages and disadvantages, for data security and privacy.

Also Read: Technology Dissertation Topics

Topic. 7: Data protection vs data privacy

Research Aim: The aim of the research is to compare and contrast data protection and data privacy. While they may look synonymous, they are not. Hence, they have to be ensured in different ways. Moreover, it is aimed to understand the necessary measures required to be taken in order to ensure data protection as well as data privacy.

Topic. 8: The importance of updates for data security

Research Aim: Personal computers and devices are always at risk of data theft, especially ransomware attacks. The updates that people usually are highly significant to counter such threats. The aim of the research is to identify and evaluate the importance of updates to keep the data of a computer system safe and secure.

Also Read: Artificial Intelligence Topics for Dissertations

Topic. 9: Social media accounts and their susceptibility to data theft

Research Aim: Social media platforms are some of the most common avenues for cybercriminals to target their prey. The aim of the research is to identify whether or not social media accounts are susceptible to data theft and how.

Topic. 10: Spam emails; do email websites filter the mails rightly

Research Aim: We see that normally email portals put suspected emails into the spam category, which is a very helpful way of avoiding scams. However, it is significant to understand the effectiveness of the program to accurately trigger all kinds of overt and covert spam messages. The research will aim to find out to what extent the email websites are effective to filter the mails rightly.

How Can ResearchProspect Help?

ResearchProspect writers can send several custom topic ideas to your email address. Once you have chosen a topic that suits your needs and interests, you can order for our dissertation outline service which will include a brief introduction to the topic, research questions , literature review , methodology , expected results , and conclusion . The dissertation outline will enable you to review the quality of our work before placing the order for our full dissertation writing service !

Topic. 11: What do weak passwords look like:

Research Aim: The aim of the research is to find ways to create a good password by identifying the features of weak kinds of passwords. A good password is a prerequisite to keeping computer systems, devices, and digital profiles and all kinds of information safe and secure.

Topic. 12: probing different techniques of data extraction:

Research Aim: There are many ways cybercriminals can extract information from computer systems. The aim of the research is to analyse and evaluate the different kinds of data extraction techniques and provide tips to avoid and overcome them.

Topic. 13: Unlocking phone with password vs face recognition:

Research Aim: Biometric recognition technology has made it much easier to deny access to outsiders and unknown to the computer systems and premises locked with computer systems. The smartphones have that feature too, which means they can be unlocked with face recognition or thumb impression. The aim of the research is to compare the unlocking phone with passwords and face recognition and find out the stronger option between them.

Topic. 14: The new update in WhatsApp: Are messages really encrypted

Research Aim: The aim of the research is to analyse and evaluate the recent updates in WhatsApp and explore if the messages sent between two parties are really encrypted or are shared with a third party.

Topic. 15: Encryption technology of different social media sites:

Research Aim: Different social media pages have different encryption technology used to keep the chats of the users secured. The aim of the research is to explore and analyse different encryption technologies used for differnet social media platforms and identify which one of them offers the best solution.

Topic. 16: cookies and cache files; what do they do

Research Aim: Cookies that we usually accept on different sites without reading why they are used is the negligence we often show. Although cookies and cache files may seem meaningless, they are not. In fact, they can help cybercriminals invade your computers and phones. The aim of the research is to study the function of cookies and cache files in-depth and analyse what they can do to pose a security threat to the data.

Also Read: 5 Dissertation Topics on Cyber Crime

Topic. 17: Mechanics of password protection:

Research Aim: The aim of the research is to study the mechanics of password protection in order to understand what goes into creating a robust password that is difficult to hack.

Topic. 18: IoT and the vulnerability of data theft:

Research Aim: Today, almost everything runs on the internet. The internet of things, the technology embedded in normal things to convert therm into smart devices, has taken over all the arduous tasks such as watching over, cleaning, and washing etc. However, having these devices set into homes and officies also has negative repercussions if the data is stolen. The aim of the research is to study IoT technology and its vulnerability to data theft.

Topic. 19: Bots and cyber security:

Research Aim: The aim of the research is to study and evaluate the future prospects regarding bots taking over the responsibility of cyber security. It will explain and evaluate how artificial intelligence is capable of taking charge of cyber security.

Topic. 20: term and conditions in software:

Research Aim: There are certain terms and conditions that we have to accept to install the software on a computer. A software mentions in its policy that it might share information with a third party. The aim of the research is to explore what the terms and conditions in software mean and how significantly they can affect the user.

Conducting research can be one of the most exciting things, but when it comes to writing, students become dreadful. But do not worry, we have got your back. Whether you want a section of the dissertation to be written impeccably or the whole of it, we are here. Don’t wait; click here .

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How to find data security and privacy dissertation topics.

To find data security and privacy dissertation topics:

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Explore legal and ethical aspects.
Investigate emerging technologies.
Analyze privacy regulations.
Consider IoT and AI implications.
Select a niche area matching your expertise and concerns.

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Key findings about Americans and data privacy

Many Americans have endless digital tools at their fingertips. And each device, site or app collects, analyzes and uses personal data. What does this mean for Americans now that so much of their day-to-day life leaves a digital footprint?

Pew Research Center has a long record of studying Americans’ views of privacy and their personal data, as well as their online habits. This study sought to understand how people think about each of these things – and what, if anything, they do to manage their privacy online. ( Read the full report .)

This survey was conducted among 5,101 U.S. adults from May 15 to 21, 2023. Everyone who took part in the survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race and ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used for this analysis , along with responses, and its methodology .

Here are nine takeaways from a new Pew Research Center report exploring these issues.

Americans, especially Republicans, are growing more concerned about how the government uses the data it collects about them. About seven-in-ten U.S. adults (71%) say they are very or somewhat concerned about this, up from 64% in 2019. Concern has grown among Republicans and those who lean Republican but has held steady among Democrats and Democratic leaners.

Line charts showing that growing shares of Republicans say they’re worried about how the government uses their personal data.

Many Americans have little trust in companies to use AI responsibly. Among those who have heard of artificial intelligence (AI):

70% say they have little to no trust in companies to make responsible decisions about how they use AI in their products.
81% say the information companies collect will be used in ways that people are not comfortable with
80% say it will be used in ways that were not originally intended.

Still, 62% of those who have heard of AI say companies using it to analyze personal details could make life easier.

Many trust themselves to make the right decisions but are skeptical their actions matter. About eight-in-ten (78%) say they trust themselves to make the right decisions to protect their personal information.

But a majority (61%) are skeptical anything they do will make much difference. And only about one-in-five are confident that those with access to their personal information will treat it responsibly.

A bar chart showing that many trust themselves to make the right privacy decisions but are also skeptical their actions matter.

More than half of Americans (56%) say they always, almost always or often click “agree” without reading privacy policies. Another 22% say they do this sometimes and 18% rarely or never do this.

A pie chart showing that nearly 6 in 10 Americans frequently skip reading privacy policies.

People are also largely skeptical that privacy policies do what they’re intended to do. About six-in-ten Americans (61%) think they’re ineffective at explaining how companies use people’s data.

About seven-in-ten Americans are overwhelmed by the number of passwords they have to remember. And nearly half (45%) report feeling anxious about whether their passwords are strong and secure.

Despite these concerns, only half of adults say they typically choose passwords that are more secure, even if they are harder to remember. A slightly smaller share (46%) opts for passwords that are easier to remember, even if they are less secure.

A bar chart showing that many Americans are overwhelmed by keeping up with passwords – and nearly half forgo secure ones.

Some Americans have been targets of data breaches and hacking. In the past 12 months:

A dot plot showing that Black adults are more likely than other racial and ethnic groups to say they have dealt with an online hack in the last 12 months.

Roughly a quarter of Americans (26%) say someone put fraudulent charges on their debit or credit card.
A smaller share say they have had someone take over their email or social media account without their permission (11%).
And 7% have had someone attempt to open a line of credit or apply for a loan using their name.

In total, 34% of Americans have experienced at least one of these issues in the past year. However, Black Americans are more likely than members of other racial and ethnic groups to have faced this.

Americans have little faith that social media executives will protect user privacy. Some 77% of Americans have little or no trust in leaders of social media companies to publicly admit mistakes and take responsibility for data misuse.

They are no more optimistic about the government reining them in: 71% have little to no trust that tech leaders will be held accountable for their missteps.

A chart showing that most Americans don’t trust social media CEOs to handle users’ data responsibly.

There is bipartisan support for more regulation to protect personal information. Some 78% of Democrats and 68% of Republicans think there should be more government regulation of what companies can do with customers’ personal information.

These findings are largely similar to our 2019 survey , which also showed strong support for increased regulation across parties.

A bar chart showing broad partisan support for more regulation of how consumer data is used.

About nine-in-ten Americans (89%) are concerned about social media sites knowing personal information about children. Most Americans are also concerned about advertisers using data about children’s online activities to target ads to them (85%) and online games tracking children on the internet (84%).

A horizontal stacked bar chart showing that a majority of Americans say parents and technology companies should have a great deal of responsibility for protecting children’s online privacy.

When it comes to who should be responsible for protecting kids’ online privacy, a vast majority (85%) says parents should bear a great deal of the responsibility. Still, roughly six-in-ten say the same about technology companies, and just under half believe the government should have a great deal of responsibility.

Note: Here are the questions used for this analysis , along with responses, and its methodology .

Artificial Intelligence
Online Privacy & Security
Privacy Rights
Social Media

Many Americans think generative AI programs should credit the sources they rely on

Americans’ use of chatgpt is ticking up, but few trust its election information, q&a: how we used large language models to identify guests on popular podcasts, striking findings from 2023, what the data says about americans’ views of artificial intelligence, most popular.

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21 Best Big Data Research Topics
Big Data Dissertation Topics : [Topic Selection -> Paper Publication]
Top 15+ Big Data Dissertation Topics [Functionalities and Concepts]
Latest Big Data Dissertation Ideas [Big Data Analytics Research Topics ]
Big Data key topics in existing research.
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214 Big Data Research Topics: Interesting Ideas To Try
These 15 topics will help you to dive into interesting research. You may even build on research done by other scholars. Evaluate the data mining process. The influence of the various dimension reduction methods and techniques. The best data classification methods. The simple linear regression modeling methods.
100 Best Big Data Research Topics
Here are some of the best data mining project topics that learners can consider. Big data mining techniques and tools. Model-based clustering of texts. Describe the concept of data spectroscopic clustering. Parallel spectral clustering within a distributed system.
Research Topics & Ideas: Data Science
Data Science-Related Research Topics. Developing machine learning models for real-time fraud detection in online transactions. The use of big data analytics in predicting and managing urban traffic flow. Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.
170 Excellent Big Data Research Topics
Easy Big Data Research Paper Topics. Relevance of data exploration in data analysis. Data management and its basics. Managing and sourcing external data. Market study and the data catalog reference model. Access to data publications in modern companies. Managing images during research. Explain the relevance of data encryption tools.
37 Research Topics In Data Science To Stay On Top Of » EML
22.) Cybersecurity. Cybersecurity is a relatively new research topic in data science and in general, but it's already garnering a lot of attention from businesses and organizations. After all, with the increasing number of cyber attacks in recent years, it's clear that we need to find better ways to protect our data.
140 Excellent Big Data Research Topics to Consider
To write an excellent computer science thesis on big data, you must have a valid research topic. As big data is a broad subject, choosing a new trending research topic is a challenging task. So, to help you, here, in this blog post, we have listed the top interesting big data topics for you to consider for research or academic writing. List of ...
166 Big Data Research Topics To Ace Your Paper
166 Latest Big Data Research Topics And Fascinating Ideas. Big data refers to a huge volume of data, whether organized or unorganized, whose analysis shapes technologies and methodologies. Big data is so massive and complicated that it cannot be handled using ordinary application software. For instance, some frameworks, such as Hadoop, are ...
Top 15+ Big Data Dissertation Topics
The term big data refers to the technology which processes a huge amount of data in various formats within a fraction of seconds.Big data handles the research domains by means of managing their data loads. Big data dissertation helps to convey the perceptions on the proposed research problems. It is also known as the new generation technology which could compatible with high-speed data ...
PDF The Evolution of Big Data and Its Business Applications
The Evolution of Big Data and Its Business Applications. Doctor of Philosophy (Information Science), May 2018, 113 pp., 12 tables, 10 figures, references, 203 titles. The arrival of the Big Data era has become a major topic of discussion in many sectors because of the premises of big data utilizations and its impact on decisionmaking. It is an -
Frontiers in Big Data
Emerging Trends in Computational Neuroscience: Harnessing Machine Learning for Enhanced Signal Processing and Decoding. This innovative journal focuses on the power of big data - its role in machine learning, AI, and data mining, and its practical application from cybersecurity to climate science and public health.
Top 10 Essential Data Science Topics to Real-World Application From the
1. Introduction. Statistics and data science are more popular than ever in this era of data explosion and technological advances. Decades ago, John Tukey (Brillinger, 2014) said, "The best thing about being a statistician is that you get to play in everyone's backyard."More recently, Xiao-Li Meng (2009) said, "We no longer simply enjoy the privilege of playing in or cleaning up everyone ...
Best Big Data Science Research Topics for Masters and PhD
These ideas have been drawn from the 8 v's of big data namely Volume, Value, Veracity, Visualization, Variety, Velocity, Viscosity, and Virility that provide interesting and challenging research areas for prospective researches in their masters or PhD thesis . Overall, the general big data research topics can be divided into distinct ...
10 Best Research and Thesis Topic Ideas for Data Science in 2022
The best course of action to amplify the robustness of a resume is to participate or take up different data science projects. In this article, we have listed 10 such research and thesis topic ideas to take up as data science projects in 2022. Handling practical video analytics in a distributed cloud: With increased dependency on the internet ...
10 Compelling Machine Learning Ph.D. Dissertations for 2020
This dissertation explores three topics related to random forests: tree aggregation, variable importance, and robustness. 10. Climate Data Computing: Optimal Interpolation, Averaging, Visualization and Delivery. This dissertation solves two important problems in the modern analysis of big climate data.
Dissertations / Theses: 'Big data analytics and tools'
Big data is currently a buzzword in both academia and industry, with the term being used todescribe a broad domain of concepts, ranging from extracting data from outside sources, storingand managing it, to processing such data with analytical techniques and tools.This thesis work thus aims to provide a review of current big data analytics concepts in an attemptto highlight big data analytics ...
1000+ Research Topics For Your Dissertation Or Thesis
1000+ FREE Research Topics & Ideas. If you're at the start of your research journey and are trying to figure out which research topic you want to focus on, you've come to the right place. Select your area of interest below to view a comprehensive collection of potential research ideas. AI & Machine Learning. Blockchain & Cryptocurrency.
Recent Dissertation Topics
2015. 2014. 2013. 2012. 2011. 2010. 2009. 2008. This list of recent dissertation topics shows the range of research areas that our students are working on.
A List of 20 Data Science Dissertation Topics for 2023
The handout provides a curated list of 20 data science dissertation topics for students in 2023. It emphasizes the importance of selecting a research area aligned with personal interests and addressing current trends and challenges in the field. The topics cover machine learning, data mining, artificial intelligence, and big data analytics.
Latest Thesis and Research Topics in Big Data
The main thesis topics in Big Data and Hadoop include applications, architecture, Big Data in IoT, MapReduce, Big Data Maturity Model etc. Latest Thesis and Research Topics in Big Data. There are a various thesis and research topics in big data for M.Tech and Ph.D. Following is the list of good topics for big data for masters thesis and research:
Best Data Science Dissertation Topics: A List from Experts
Here are the hand-picked dissertation topics for data science that can help you grab the reader's attention quickly and without too much effort. 1. Compare the implementation of data science in various investigations concerning wildfires. 2. Explain the K-means clustering from the perspective of online spherical.
Business Intelligence Dissertation Topics
Here are a few interesting and appealing topics for a dissertation on business intelligence. Implementation of a secure grouping agent with model-based organizational control. Business Intelligence Several Big Data analytics and their implementations. Knowledge management facilitates organizational learning in higher education.
Sample Masters Big Data Full Dissertation
The time required to write a master's level full dissertation varies, but it typically takes 6-12 months, depending on research complexity and individual pace. This is a sample Masters' level full dissertation in the area of big data, developed by our experts and demonstrates the quality of our work.
Dissertation Topics on Data Security and Privacy
Topic. 1: Blockchain and data security. Topic. 2: Significance of data security and privacy in health care. Topic. 3: Data security and privacy in banks. Topic. 4: Why it is imperative for businesses to ensure the data security of their clients. Topic. 5: Challenges of cyberattacks for state organisations.
Key findings about Americans and data privacy
People are also largely skeptical that privacy policies do what they're intended to do. About six-in-ten Americans (61%) think they're ineffective at explaining how companies use people's data. About seven-in-ten Americans are overwhelmed by the number of passwords they have to remember. And nearly half (45%) report feeling anxious about ...
Homicides Are Plummeting in American Cities
Homicides in American cities are falling at the fastest pace in decades, bringing them close to levels they were at before a pandemic-era jump. Nationwide, homicides dropped around 20% in 133 ...

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37 Research Topics In Data Science To Stay On Top Of

37 Research Topics in Data Science

2.) Big Data Analytics

3.) Auto Machine Learning

4.) Text Mining

5.) Natural Language Processing

6.) Recommender Systems

7.) Deep Learning

8.) Reinforcement Learning

9.) Data Visualization

10.) Predictive Maintenance

11.) Financial Analysis

12.) Image Recognition

13.) Fraud Detection

14.) Web Scraping

15.) Social Media Analysis

16.) GPU Computing

17.) Quantum Computing

18.) Genomics

19.) Location-based services

20.) Smart City Applications

21.) Internet Of Things (IoT)

22.) Cybersecurity

23.) Blockchain

24.) Sustainability

25.) Educational Data

26.) Politics

27.) Cloud Technologies

28.) Robotics

29.) HealthCare

30.) Remote Work

31.) Data-Driven Journalism

32.) Data Engineering

33.) Data Curation