certificate course in biostatistics epidemiology and research methodology

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certificate course in biostatistics epidemiology and research methodology

Certificate Course in Biostatistics, Epidemiology & Research Methodology

Department of Data Science (formerly Department of Statistics), Prasanna School of Public Health (PSPH), MAHE, Manipal has been successfully conducting the “Certificate Course in Bio-statistics, Epidemiology and Research Methodology” from the year 2006 with its 32nd batch completed recently. Certificate course in Bio-statistics, Epidemiology and Research Methodology is a 10-week program , which accounts for a total of 6 credit hours .

Important Notice to the PhD Scholars registering for the course:

In case of any technical issues during registration please contact 0820 29 22072 .

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Online graduate certificate in biostatistics.

Ohio State’s online Graduate Certificate in Biostatistics offers a unique blend of epidemiology and biostatistics for public health. You will learn survival analysis techniques; assess and improve study designs commonly used in public health research; and interpret standard regression models using industry-standard statistical software. Accredited by the Council on Education in Public Health, this 100% online Graduate Certificate in Biostatistics is designed to equip you with the background in epidemiologic study designs, biostatistics, and statistical computing required for public health data analysis in the public and private sectors.

Why Choose Ohio State’s Online Graduate Certificate Program in Biostatistics?

This online graduate certificate offers a rigorous curriculum tailored to applications in population health. You will gain practical hands-on experience by using a wide array of statistical tools such as R, Stata, and SAS to analyze public health data. This will equip you with skills that are highly sought after in job postings for roles like management analysts, medical scientists, statisticians, and epidemiologists.

Campus Requirements: NONE – 100% online

Class Format: Asynchronous – you can complete coursework each week on your own schedule. 

Credit Hours Required: 14

Cost Per Credit Hour: $812.06 per credit hour (includes instructional and general fees). See full breakdown of costs here .

Admission Requirements: Minimum of a bachelor’s degree. GRE not required.  

Time to Completion: 3 semesters, Part-time

Graduate Certificate vs. Master’s Degree: Which is Right for You?

What are the benefits of an online certificate program, how do online classes work, sample courses, principles of epidemiology, regression methods for the health sciences, introduction to r for data science, featured faculty.

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Is this program offered in my state, the selected program is available in the following states.

If you live outside Ohio and are planning to earn a professional license via this program, click on the state where you plan to seek licensure for important information.

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Harvard Catalyst Profiles

Applied Biostatistics Certificate: Methods & Applications

Online professional certificate program on the principles and methods of biostatistics

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Harvard Catalyst Desire2Learn

You will only be able to access this system if you have received a confirmation letter for this program.

For more information:

Program goals.

Understand the most commonly used approaches in medical statistics
Choose an appropriate analysis
Implement these techniques in statistical software
Assess the statistical methods chosen in a paper

This online professional certificate program offers a comprehensive introduction to biostatistics in medical research. The program includes a review of the most common techniques in the field, as well as the manner in which these techniques are applied in standard statistical software. Weekly lectures are combined with learning assessments and practicum exercises to support and engage participants. Optional discussion boards, office hours, and journal club sessions are available for those who wish to engage further with colleagues and faculty.

By the conclusion of the program, participants will be able to:

Choose an appropriate statistical analysis plan
Calculate the sample size needed to complete a study
Analyze the collected data
Communicate the results from their experiment

Wondering what a professional certificate entails? Go to our professional credits and certificate page to learn more.

Free Option for Harvard Affiliates

Participants must select one of the following program tracks:

Track 1: Applied Biostatistics: Core Curriculum

Ideal for participants who are interested in an abbreviated curriculum and shorter program commitment.

Track 2: Applied Biostatistics: Core Curriculum and Advanced Topics

Ideal for learners who are interested in a more comprehensive program, with the opportunity to select from more advanced topics at the end of the program.

For more information, please visit Program Tracks .

Session dates

Track 1: September 11, 2023 – April 1, 2024

Track 2: September 11, 2023 – July 1, 2024

Time commitment

It is expected that program participants will dedicate up to four hours per week to this online program.

Each week, participants will view a multi-part video lecture and will be responsible for completing a quiz and practicum exercise related to the topic for the week. The practicum will demonstrate how to apply the technique in a standard statistical package (STATA®) and provide practice problems so that the concept is reinforced.

Those participants who view all videos and complete the lecture quizzes and practicum quizzes associated with 80% of program content will earn a certificate of completion.

Professionals interested in learning and applying commonly used approaches in medical statistics.

Eligibility

MPH, MD, PhD, DMD, or doctorate-level degree.

Harvard affiliates: $1750.00
Non-Harvard affiliates: $2500.00
Harvard affiliates: $2800.00
Non-Harvard affiliates: $4000.00
Applied Biostatistics Certificate participants who wish to withdraw from the program for a full refund must request to do so by October 10th. Participants who wish to withdraw from the program and defer their enrollment to the following year must do so by October 10th. Enrollment will be deferred to the program offered in the next calendar year. Any paid program fees will not be eligible for refund or transfer after the deadline. Please contact [email protected] for more information.
Cancellation and Refund Policy [PDF]
Additional 10% off for nurses and Allied Health Professionals (can be combined with other discounts)
Community Partners of Harvard Catalyst Programs
Countries with GNI below $13,000

Accreditation statement

The Harvard Catalyst Education Program is accredited by the Massachusetts Medical Society to provide continuing medical education for physicians.

The application process is currently closed. Please check back for future opportunities.

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Biostatistics

Learn Biostatistics Online

Whether you're just starting out or already have some experience, we offer various Biostatistics courses designed to fit your needs. Curated from top educational institutions and industry leaders, our selection of Biostatistics courses aims to provide quality training for everyone—from individual learners seeking personal growth to corporate teams looking to upskill. For those pursuing professional advancement, skill acquisition, or even a new career path, these Biostatistics courses can be a valuable resource. Take the next step in your professional journey and enroll in a Biostatistics course today!

Browse Biostatistics Courses

Johns Hopkins University

Biostatistics in Public Health

Skills you'll gain : Biostatistics, General Statistics, Probability & Statistics, Statistical Analysis, Statistical Tests, Data Analysis, Regression, Basic Descriptive Statistics, Critical Thinking, Estimation, Probability Distribution, Data Visualization, Problem Solving

(2.2K reviews)

Beginner · Specialization · 3 - 6 Months

University of Cape Town

Understanding Clinical Research: Behind the Statistics

Skills you'll gain : Basic Descriptive Statistics, Data Analysis, General Statistics, Probability & Statistics, Statistical Tests, Critical Thinking, Clinical Data Management, Statistical Visualization

(3.3K reviews)

Beginner · Course · 1 - 3 Months

Genomic Data Science

Skills you'll gain : Bioinformatics, Computer Programming, Data Analysis, Python Programming, Computational Thinking, General Statistics, Exploratory Data Analysis, R Programming, Algorithms, Computer Programming Tools, Data Analysis Software, Biostatistics, Statistical Analysis, Programming Principles, Probability & Statistics, Statistical Tests, Data Structures, Operating Systems, Research and Design, Big Data, Computational Logic, Problem Solving, Statistical Programming, Experiment, Correlation And Dependence

(6.2K reviews)

Intermediate · Specialization · 3 - 6 Months

Imperial College London

Statistical Analysis with R for Public Health

Skills you'll gain : Data Analysis, General Statistics, Probability & Statistics, Statistical Analysis, Statistical Tests, R Programming, Regression, Biostatistics, Statistical Programming, Basic Descriptive Statistics, Exploratory Data Analysis, Critical Thinking, Correlation And Dependence, Probability Distribution, Epidemiology

(1.9K reviews)

University of California San Diego

Biology Meets Programming: Bioinformatics for Beginners

Skills you'll gain : Bioinformatics, Computational Thinking, Problem Solving, Biostatistics, Computational Logic, Critical Thinking

(1.5K reviews)

Beginner · Course · 1 - 4 Weeks

Summary Statistics in Public Health

Skills you'll gain : Biostatistics, General Statistics, Probability & Statistics

(1.8K reviews)

Mathematical Biostatistics Boot Camp 1

Skills you'll gain : Biostatistics, General Statistics, Probability & Statistics, Probability Distribution, Statistical Tests, Bayesian Statistics, Statistical Analysis, Basic Descriptive Statistics, Estimation, Mathematical Theory & Analysis

(486 reviews)

Mixed · Course · 1 - 4 Weeks

Introduction to Statistics & Data Analysis in Public Health

Skills you'll gain : Data Analysis, General Statistics, Probability & Statistics, Statistical Analysis, Basic Descriptive Statistics, Probability Distribution, Statistical Tests, Biostatistics, Epidemiology, R Programming

(1.4K reviews)

Duke University

Bayesian Statistics

Skills you'll gain : Bayesian Statistics, General Statistics, Probability & Statistics, Statistical Analysis, Probability Distribution, Bayesian Network, R Programming, Statistical Programming, Statistical Tests

(791 reviews)

Intermediate · Course · 1 - 3 Months

Advanced Statistics for Data Science

Skills you'll gain : General Statistics, Probability & Statistics, Mathematics, Statistical Tests, Linear Algebra, Algebra, Regression, Statistical Analysis, Biostatistics, Probability Distribution, Bayesian Statistics, Correlation And Dependence, Estimation, Basic Descriptive Statistics, Mathematical Theory & Analysis

(722 reviews)

Advanced · Specialization · 3 - 6 Months

Bioinformatics

Skills you'll gain : Bioinformatics, Algorithms, Computer Programming, Probability & Statistics, Computational Thinking, Computational Logic, Python Programming, Theoretical Computer Science, Critical Thinking, Graph Theory, Problem Solving, General Statistics

(1.2K reviews)

Utrecht University

Clinical Epidemiology

Skills you'll gain : Epidemiology

(332 reviews)

Searches related to biostatistics

In summary, here are 10 of our most popular biostatistics courses.

Biostatistics in Public Health : Johns Hopkins University
Understanding Clinical Research: Behind the Statistics : University of Cape Town
Genomic Data Science : Johns Hopkins University
Statistical Analysis with R for Public Health : Imperial College London
Biology Meets Programming: Bioinformatics for Beginners : University of California San Diego
Summary Statistics in Public Health : Johns Hopkins University
Mathematical Biostatistics Boot Camp 1 : Johns Hopkins University
Introduction to Statistics & Data Analysis in Public Health : Imperial College London
Bayesian Statistics : Duke University
Advanced Statistics for Data Science : Johns Hopkins University

Skills you can learn in Probability And Statistics

Frequently asked questions about biostatistics, what are the best free biostatistics courses ‎.

There are some great free biostatistics courses available online that offer an invaluable education in the field. For example, the Coursera Clinical Research course teaches course teaches strategies and techniques needed to develop research protocols. Additionally, the Bioinformatics course examines biological data from a computational perspective. If you are interested in more specialized biostatistics courses, check out the Clinical Research Biostatistics with Wolfram course for an in-depth analysis of biostatistics with Mathematica. Another great option is the Genetic Epidemiology course which looks at the impact of genetics in population health. Finally, if personalized medicine interests you, the Personalized Medicine course provides an examination of modern genomic data analysis. ‎

What are the best biostatistics courses for beginners? ‎

For those interested in learning the basics of Biostatistics, some of the best courses for beginners include the Biostatistics and Public Health Specialization , the Statistical Analysis with R Specialization , the Introduction to Summary Statistics course, the Simple Regression Analysis in Public Health course and the Fundamental Machine Learning for Healthcare course. These courses provide a strong foundation in basic Biostatistical understanding and skills. ‎

What is Biostatistics, and why is it important to learn? ‎

Biostatistics is the application of statistical methods to biological data. Used for clinical, medical, or other types of scientific purposes, they enable learners to properly understand research and interpret common statistical concepts. This research is also conducted in order to be published in scientific literature. In data science software, like R, Biostatistics are formulated in research that involves the creation of data sets and visualizations.

We recommend Biostatistics to learners who want to be able to read, and respond to, scientific literature in fields related to public health, medicine, and biological science. Learners want to understand Biostatistics in order to stay up-to-date with developments in these fields. Successful learners are also prepared to participate as part of research teams by being able to collect, analyze, and make decisions based on the biostatistics they formulate. ‎

What kind of jobs can you get with Biostatistics? ‎

The Bureau of Labor Statistics has predicted that 10,100 statistical positions are expected nationwide in the U.S. through 2024. Some careers for Biostatistics learners include Biostatician, Principal Investigator, Research Scientist, Data Analyst, Quantitative Scientist, Machine Learning Scientist, Public Health and Healthcare Professional, Social Worker, and other roles needed in research. ‎

How can online courses help me learn Biostatistics? ‎

Learners can take courses from major institutions that cover critical parts of formulating statistics for biological research. Instruction will show how to calculate summary statistics from public health and biomedical data; interpret written and visual presentations of statistical data; and choose the most appropriate statistical method to answer your research question. Learners can see lectures on probability, expectations, distributions, bootstrapping, and other key parts of Mathematical Biostatistics. Data science courses are also offered to show learners how they can clean, analyze, and visualize data while exploring ways to ask the right kinds of questions. ‎

What skills or experience do I need to already have, before learning biostatistics? ‎

Before learning biostatistics, it's helpful to have a background in math and science. This could include jobs, internships, volunteer work, or even high school or college-level classes that involve topics like biology, health, genetics, statistics, algebra, calculus, epidemiology, ecology, chemistry, microbiology, anthropology, or environmental science. Experience or previous classes in technical writing can be helpful too. Experience or skills involving programming are always beneficial because the field relies on the use of computers, technology, software, applications, and data. ‎

What kind of people are best suited for roles in biostatistics? ‎

In addition to an interest in math, science, and health, those who seek roles in biostatistics should have analytical minds with a keen eye for detail. Because you'll spend so much time going over data, objectivity, critical thinking, and problem-solving abilities are also important. You must also be a good communicator in both written and verbal skills. On a daily basis, you may interact with biologists, researchers, physicians, scientists, policymakers, and your colleagues, so interpersonal skills are a must. You may also find yourself making presentations or speaking at meetings and conferences, so you can't be afraid of public speaking. You'll also need to be a good manager. That means remaining organized and practicing time management skills as you work to complete each of your projects. ‎

How do I know if learning biostatistics is right for me? ‎

A love for math and science can lead you to learning biostatistics since the field combines both of these subject matters. Learning biostatistics can help you land a job with a competitive salary at a university, government agency, or large company in the private sector. If you've ever dreamed of working in the health care field, but you don't necessarily want to work directly with patients as a physician or nurse, biostatistics may be the right path for your future. In addition to public health, you could end up working in fields like agriculture, veterinary science, or environmental health, so learning biostatistics can open up many doors for a potential career. ‎

What are the benefits of taking an online Biostatistics course? ‎

Online Biostatistics courses offer a convenient and flexible way to enhance your existing knowledge or learn new Biostatistics skills. With a wide range of Biostatistics classes, you can conveniently learn at your own pace to advance your Biostatistics career skills. ‎

What Biostatistics courses are best for training and upskilling employees or the workforce? ‎

When looking to enhance your workforce's skills in Biostatistics, it's crucial to select a course that aligns with their current abilities and learning objectives. Our Skills Dashboard is an invaluable tool for identifying skill gaps and choosing the most appropriate course for effective upskilling. For a comprehensive understanding of how our courses can benefit your employees, explore the enterprise solutions we offer. Discover more about our tailored programs at Coursera for Business here . ‎

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Master of Public Health

Advanced epidemiology.

Pick almost any topic in public health: influenza epidemics, rising cancer rates, healthcare disparities. To understand how any of these problems are distributed across populations—much less, how to design an intervention—requires an epidemiologic mindset.

A cornerstone of public health, epidemiology focuses on the distribution and causes of disease, and on developing and testing ways to prevent and control it. From health departments to academia to the private sector, epidemiologists investigate public health challenges, including communicable and non-communicable diseases, aging, determinants of health, and mental health.

The Certificate in Advanced Epidemiology provides graduates with in-depth expertise in epidemiologic methods across different topics like infectious and chronic disease and for diverse populations. The program fosters understanding of the role of epidemiology within the broader fields of public health, medicine, and social and behavioral sciences—areas in which epidemiologists often collaborate.

This certificate enables graduates to identify potential ethical problems in research studies and evaluate alternative approaches. The intensive curriculum prepares MPH graduates with the critical interdisciplinary thinking and methodological skill set needed to address today’s complex public health challenges.

Admissions Eligibility

Advanced Epidemiology is open to Columbia MPH students in:

Biostatistics
Epidemiology

Applicants should have one semester of calculus with a B+ minimum grade or college credit for AP Calculus. .

Students who do not meet the calculus, and GRE requirements can be considered contingent upon receiving an A- grade in the REMA-Quantitative module of the Core during the Fall semester.

The Competencies for this Certificate are as follows:

Compare and contrast the strengths and limitations of the different study designs in epidemiology
Assess and describe reliability and validity of measures used in epidemiologic studies
Apply multivariable regression methods commonly used in biostatistics and epidemiology (e.g., logistic regression, cox proportional hazard models)
Assess confounding, interaction and mediation using multivariable models
Interpret results from advanced techniques and prepare written and oral presentations for non-biostatisticians.

Visit the Certificates Database to learn more about core and credit requirements.

Sample Courses

Epi Modeling for Infectious Diseases: This course is an intro to intermediate level infectious disease mathematical modeling methodological class. It will introduce the fundamental principles of infectious disease modeling. Emphasis will be given to compartmental metapopulation models. Over the course, we will learn a variety of mathematical models for infectious diseases, starting from simple compartmental models to more complex compartmental models with various structures, including multiple risk groups, age groups, spatial network, and multiple hosts. In addition to these models, we will also discuss how key epidemiological parameters (e.g. the basic reproductive number) can be estimated from real disease data. Other topics will include vaccination/antiviral efficacy assessment, survival analysis, and agent based models. Half of the course will be devoted to hands-on computer lab excises, using the R Studio open source program (if students are familiar with Python or Matlab, these programs can be used instead).
Application of Epi Research Methods II : This course will introduce students to the basic programming skills necessary to adapt R statistical computing system to their needs by presenting material spanning a spectrum from basic epidemiological measures of disease outcomes and association to more advanced applications such as mapping, spatial analysis and Bayesian methods using additional open-source tools like GRASS and WinBUGS. The course is structured in three parts: (1) Introduction to R and object based programming, (2) How to apply and extend R for epidemiological methods, and (3) Using R for advanced methods such as spatial and Bayesian analysis. Practice material and data are grounded on actual research questions, often based on the instructor’s recent work, and are intended to illustrate the kinds of issues that often arise when practicing epidemiology.
Applied Regression II : This course introduces the statistical methods for analyzing censored data, non-normally distributed response data, and repeated measurements data that are commonly encountered in medical and public health research. Topics include estimation and comparison of survival curves, regression models for survival data, logit models, log-linear models, and generalized estimating equations. Examples are drawn from the health sciences.

Related Certificates

Epidemiology of Chronic Disease
Infectious Disease Epidemiology
Public Health Research Methods

Biostatistics

We create and apply methods for quantitative research in the health sciences, and we provide innovative biostatistics education, making discoveries to improve health.

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Biostatistics Headlines

Biostatistics Faculty Member Recognized as Emerging Leader

Abhirup Datta, PhD, associate professor in the Department of Biostatistics, was named a 2024 Emerging Leader in Statistics by the Committee of Presidents of Statistical Societies (COPSS).

Alumni Spotlight: Junrui Di, PhD '19

Junrui Di, PhD ’19, works at Pfizer as a Digital Medicine Statistician.

[top left image] Scott Zeger, PhD, Professor of Biostatistics, BSPH, speaking at the podium with a photo of Chuck playing chess in High School behind him. [top right image] Elizabeth Stuart, PhD, Chair and Professor of Biostatistics, BSPH, in conversation with Karen Bandeen-Roche, PhD, MS, Professor of Biostatistics, past-Chair of Biostatistics, BSPH. [bottom image] 5 women reminiscing at a table.

Celebrating Chuck Rohde

On the one-year anniversary of Dr. Charles (Chuck) Rohde’s passing, many of his former colleagues, students, and mentees came together to celebrate his 50+ years with the Department of Biostatistics.

What We Do in the Department of Biostatistics

The Bloomberg School's Department of Biostatistics is the oldest department of its kind in the world and has long been considered one of the best. Our faculty conduct research across the spectrum of statistical science, from foundations of inference to the discovery of new methodologies for health applications.

Our designs and analytic methods enable health scientists and professionals across industries to efficiently acquire knowledge and draw valid conclusions from ever-expanding sources of information.

Biostatistics Highlights

First in u.s..

First freestanding statistics department in the U.S.

Data science driving health and empowering opportunity

Foundational discoveries for inference and modeling

Creative, close-knit community

Biostatistics Programs

The Department of Biostatistics offers three graduate programs to applicants with a bachelor's degree (or higher) interested in professional or academic careers at the interface of the statistical and health sciences.

We also have funded training programs in the Epidemiology and Biostatistics of Aging for PhD students who are U.S. citizens or permanent residents.

Master of Health Science (MHS)

Our one-year MHS program provides study in biostatistical theory & methods. It is also open to students concurrently enrolled in a JHU doctoral program.

Master of Science (ScM)

Our ScM targets individuals who have demonstrated prior excellence in quantitative or biological sciences and desire a career as a professional statistician.

Doctor of Philosophy (PhD)

Our PhD graduates lead research in the foundations of statistical reasoning, data science, and their application making discoveries to improve health.

Stephanie Hicks, PhD, MA

is an applied statistician who develops methods, tools and open source software for the analysis of genomics data. In 2018 Hicks co-founded R-Ladies Baltimore, a non-profit designed to promote gender diversity in the R community worldwide.

Biostatistics Consulting Center

The Johns Hopkins Biostatistics Center is the practice arm of our Department, providing the latest in biostatistical and information science expertise to a wide range of clients both within and outside Johns Hopkins.

Alyssa Columbus, MS '22, PhD Candidate

Congratulations to Alyssa Columbus, MS '22, who was recently selected to be a CERSI Scholar with the Johns Hopkins BSPH Center of Excellence in Regulatory Science and Innovation. The CERSI Scholars program integrates JHU's most outstanding students in the regulatory science arena into all of the activities of the Center by encouraging these students to participate in their research, educational activities, and collaborative activities with other CERSIs and the FDA.

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Home > CTSA Cores and Programs > Research Methods Core > Biostatistics, Epidemiology, and Research Design (BERD)

Biostatistics, Epidemiology, and Research Design (BERD)

Access to biostatistical, epidemiologic, and study design expertise is critical to the success of clinical and translational research (CTR) in a team science setting. Spectrum’s Biostatistics, Epidemiology, and Research Design (BERD) resources provide CTR investigators with support for study design development, data curation and management, and analytic strategies.

As new data science practice methods emerge (e.g., electronic medical records, biologic, imaging, physiologic, and administrative), novel clinical trial designs and analytic approaches are needed to answer questions across the translational spectrum. The ability to take full advantage of this data explosion is necessary for an investigator of the 21st century, and requires a broader approach than we have historically taken with a focus on biostatistics and epidemiology.

BERD leverages the infrastructure of the Quantitative Sciences Unit (QSU) in the Department of Medicine led by Dr. Manisha Desai, with additional high-level expertise from faculty in the Department of Biomedical Data Science led by Dr. Sylvia Plevritis. The QSU is a team science-based collaboratory of more than 25 data scientists at the faculty, Ph.D., and Masters level. The Department of Biomedical Data Science houses 19 primary faculty with diverse statistical and data science expertise.

BERD Leadership and Members

BERD Resources

BERD engages clinical and basic science investigators on:

Study design
State-of-the-art secure database design
Modern data analysis planning
Interpretation of findings
Development of new methods, tools, software, and applications
Education and training in research methods

BERD provides the infrastructure for such resources through several critical means – 1) Data Studio; 2) Clinical Office Hours; and 3) Data Science Navigation.

BERD Initiatives

California berd consortium.

Important Resources for the Clinical and Translational Research Community

Biostats4you.

The Biostats4you website was developed to serve medical and public health researchers and professionals who wish to learn more about biostatistics. The site contains carefully selected and reviewed training materials especially suited for a non-statistician audience. This site is housed by University of Minnesota and is part of the Biostatistics, Epidemiology and Research Design Special Interest Group (BERD SIG) efforts.

More Resources to Come

Additional data science resources at stanford.

BERD members collaborate closely with and rely heavily on other data science teams on campus. BERD will facilitate relationships with these teams on behalf of our peer investigators.

Research Informatics Center: The Research Informatics Center offers a consultation service to Stanford University and Stanford Medicine researchers on topics related to clinical data access for research purposes. LEARN MORE
Research IT Technology & Digital Solutions: The Research IT team assist with the following; Data management strategies, Databases, Software development, Common Data Models, Data types and sources, Data collection and storage, Analytical environments, Regulatory, Security and Privacy, Stanford processes, Institutional, multi-party requirements and the Hospital ecosystem. Learn More
Technology & Digital Solutions: Provides Stanford Medicine community with the most innovative technology services as efficiently as possible. Led by Eric Yablonka, CIO, and Michael Halaas, deputy CIO, the newly unified organization brings together the best of the School of Medicine and Stanford Health Care IT to enable new opportunities for groundbreaking work and compassionate care. Learn More
Center for Population Health Sciences: The Center’s mission is to improve the health of populations by bringing together diverse disciplines and data to understand and address social, environmental, behavioral, and biological factors on both a domestic and global scale. Learn More
Stanford’s Department of Biomedical Data Science: The Department of Biomedical Data Science (DBDS) is an academic research community, comprised of faculty, students, and staff, whose mission is to advance precision health by leveraging large, complex, multi-scale real-world data through the development and implementation of novel analytical tools and methods. LEARN MORE
Stanford’s Quantitative Sciences Unit: The Quantitative Sciences Unit (QSU) is a unit of statistical scientists in the Department of Medicine who engage in interdisciplinary research. Members of the QSU are available to collaborate on a broad data science scope including study design, data management and data analysis for research and clinical studies. LEARN MORE

Biomedical Graduate Studies

Epidemiology and biostatistics graduate group.

GGEB Courses
BSTA Course Descriptions

Courses in Biostatistics and Statistics

The Center for Clinical Epidemiology and Biostatistics, the Department of Biostatistics and Epidemiology, and the Graduate Group in Epidemiology and Biostatistics offer a wide range of courses; a brief description of current offerings is provided below. Not all courses are offered every year. The program may revise these courses over time; the descriptions given here are for guidance only.

BSTA 509: Introductory Epidemiology (EPID 801)

• Fall term (Course no longer offered) • 0.5 credit unit • Instructor (s): TBA • Prerequisites: Permission of instructor.

Description: This course is a series of lectures designed to teach basic principles of epidemiologic research. It provides an overview of the types of research questions that can be addressed by epidemiologic methods. Topics covered include definitions of epidemiology; measures of disease frequency; measures of effect and association; epidemiologic study designs, both experimental and non-experimental; data collection methods; and an overview of analysis of epidemiologic studies. (The lectures for this course are identical to those in EPID 801.)

BSTA 510: Introduction to Human Health and Diseases

• Fall term (Course no longer offered) • 0.5 credit unit • Instructor (s): • Prerequisites: Permission of instructor.

BSTA 511: Biostatistics in Practice I

• Fall/Spring Term (offered to Biostatistics students only) • 1 credit unit • Instructor (s): Nandita Mitra, PhD • Prerequisites: Open to Biostatistics students only.

BSTA 512: Database Management for Clinical Epidemiology I (EPID 532)

Bsta 513: measurement of health in epidemiology (epid 542) , bsta 514: clinical economics and clinical decision making (epid 550), bsta 550: applied regression and analysis of variance (stat 500), bsta 620: probability.

• Fall term • 1.0 credit unit • Instructor : P amela Shaw, PhD • Prerequisites: Two semesters of calculus (through multivariable calculus), linear algebra; permission of instructor.

Description: This core course covers elements of (non-measure theoretic) probability necessary for the further study of statistics and biostatistics. Topics include set theory, axioms of probability, counting arguments, conditional probability, random variables and distributions, expectations, generating functions, families of distributions, joint and marginal distributions, hierarchical models, covariance and correlation, random sampling, sampling properties of statistics, modes of convergence, and random number generation.

BSTA 621: Statistical Inference I

• Spring term • 1.0 credit unit • Instructor: Wen Guo, PhD • Prerequisites: BSTA 620; permission of instructor.

Description: This class will cover the fundamental concepts of statistical inference. Topics include sufficiency, consistency, finding and evaluating point estimators, finding and evaluating interval estimators, hypothesis testing, and asymptotic evaluations for point and interval estimation.

BSTA 622: Statistical Inference II

• Fall/Spring term • 1.0 credit unit • Instructors: Jing Huang, PhD • Prerequisites: BSTA 620; permission of instructor. Description: This class will cover the fundamental concepts of statistical inference. Topics include sufficiency, consistency, finding and evaluating point estimators, finding and evaluating interval estimators, hypothesis testing, and asymptotic evaluations for point and interval estimation.

BSTA 630: Statistical Methods and Data Analysis I

• Fall term • 1.0 credit unit • Instructors: Rui Xiao, PhD • Prerequisites: Multivariable calculus and linear algebra, BSTA 620 (may be taken concurrently); permission of instructor.

Description: This first course in statistical methods for data analysis is aimed at first-year Biostatistics students. It focuses on the analysis of continuous data. Topics include descriptive statistics (measures of central tendency and dispersion, shapes of distributions, graphical representations of distributions, transformations, and testing for goodness of fit); populations and sampling (hypotheses of differences and equivalence, statistical errors); one- and two-sample t tests; analysis of variance; correlation; nonparametric tests on means and correlations; estimation (confidence intervals and robust methods); categorical data analysis (proportions; statistics and test for comparing proportions; test for matched samples; study design); and regression modeling (simple linear regression, multiple regression, model fitting and testing, partial correlation, residuals, multicollinearity). Examples of medical and biologic data will be used throughout the course, and use of computer software demonstrated.

BSTA 631: Statistical Methods and Data Analysis II

• Spring term ( NOTE: Course replaced by BSTA 632: Statistical Methods for Categorical and Survival Data ) • 1.0 credit unit • Instructor (s): • Prerequisites: linear algebra, calculus, BSTA 630, BSTA 620, BSTA 621 (may be taken concurrently); permission of instructor.

Description: This is the second half of the methods sequence, where the focus shifts to methods for categorical and survival data. Topics in categorical include defining rates; incidence and prevalence; the chi-squared test; Fisher's exact test and its extension; relative risk and odds-ratio; sensitivity; specificity; predictive values; logistic regression with goodness of fit tests; ROC curves; the Mantel-Haenszel test; McNemar's test; the Poisson model; and the Kappa statistic. Survival analysis will include defining the survival curve, censoring, and the hazard function; the Kaplan-Meier estimate, Greenwood's formula and confidence bands; the log rank test; and Cox's proportional hazards regression model. Examples of medical and biologic data will be used throughout the course, and use of computer software demonstrated.

BSTA 632: Statistical Methods for Categorical and Survival Data

• Spring term • 1.0 credit unit • Instructors: Warren Bilker, PhD and Sharon Xie, PhD • Prerequisites: Linear algebra, calculus, BSTA 630, BSTA 620, BSTA 621 (may be taken concurrently); permission of instructor.

BSTA 651: Introduction to Linear Models and Generalized Linear Models

• Spring term • 1.0 credit unit • Instructors: Justine Shults , PhD and Yong Chen, PhD • Prerequisites: Linear algebra, calculus, BSTA 620, BSTA 630. BSTA 621 and BSTA 632 (may be taken concurrently); permission of instructor.

Description: This course extends the content on linear models in BSTA 630 and BSTA 631 to more advanced concepts and applications of linear models. Topics include the matrix approach to linear models including regression and analysis of variance; multiple linear regression, collinearity diagnostics; multiple comparisons; fitting strategies; simple experimental designs (block designs, split plot); and prediction. In addition, generalized linear models will be introduced with emphasis on the binomial, logit and Poisson log-linear models. Applications of methods to example datasets will be emphasized.

BSTA 652: Categorical Data Analysis

• Fall term (Course no longer offered) • 1.0 credit unit • Instructor (s): • Prerequisites: BSTA 621, BSTA 631, BSTA 651; permission of instructor.

Description: This course elaborates on the treatment of categorical data analysis in Statistical Methods I and II. Topics include probability models for contingency tables, estimation of odds ratios, exact and asymptotic tests of independence, generalized linear models (logit, complementary log-log, and loglinear), ordinal regression models, Mantel-Haenszel tests, and estimation.

BSTA 653: Survival Analysis

• Fall term ( NOTE: Course replaced by BSTA 754: Advanced Survival Analysis as of Fall 2015) • 1.0 credit unit • Instructor (s): • Prerequisites: BSTA 621, BSTA 631, BSTA 651; permission of instructor.

Description: This course extends the methods for the analysis of time to event data or survival analysis covered in BSTA 631. Concepts include survival distributions, hazard distributions, censoring mechanisms and truncation mechanisms. Parametric and nonparametric methods for estimation and inference will be covered, including the Kaplan-Meier estimator, exponential and Weibull models, logrank tests, the generalized Wilcoxon test, the Cox proportional hazards regression and extensions to time-dependent covariates.

BSTA 656: Longitudinal Data Analysis

• Fall term • 1.0 credit unit • Instructor: I an Barnett, PhD • Prerequisites: BSTA 621, BSTA 631 or 632, BSTA 651, BSTA 653 or 754; permission of instructor.

Description: This course covers both the applied aspects and methods developments in longitudinal data analysis. In the first part, we review the properties of the multivariate normal distribution and cover basic methods in longitudinal data analysis, such as exploratory data analysis, two-stage analysis and mixed-effects models. Focus is on the linear mixed-effects models, where we cover restricted maximum likelihood estimation, estimation and inference for fixed and random effects and models for serial correlations. We will also coverBayesian inference for linear mixed-effects models.The second part covers advanced topics, including nonlinear mixed-effects models, GEE, generalized linear mixed-effects models, nonparametric longitudinal models, functional mixed-effects models, and joint modeling of longitudinal data and the dropout mechanism.

BSTA 657: Design of Biomedical Studies I

• Spring term (Course no longer offered) • 0.5 credit unit • Instructor (s): • Prerequisites: BSTA 621, BSTA 631, BSTA 651, BSTA 652, BSTA 653; permission of instructor.

Description: This course is an introduction to the statistical planning and design of biomedical investigations. It introduces the classical theory of experimental design using case studies in biomedical research as illustrations. Topics include randomization, blocking, complete and incomplete block designs, factorial designs, sample size estimation, random vs fixed effects, and practical applications.

BSTA 658: Design of Biomedical Studies II

• Spring term (Course no longer offered) • 0.5 credit unit • Instructor (s): • Prerequisites: BSTA 621, BSTA 631, BSTA 651, BSTA 652, BSTA 653; BSTA 657 highly recommended; permission of instructor.

Description: This course builds on the basic theory of experimental design in biomedical investigations by focusing on statistical topics and randomized trials. Case studies will be used to illustrate the methods.

BSTA 660: Design of Observational Studies

• Fall term • 0.5 credit unit • Instructor (s): R ebecca Hubbard, PhD • Prerequisites: BSTA 621, BSTA 631 or BSTA 632, BSTA 651; permission of instructor.

Description: This course will cover statistical methods for the design and analysis of observational studies. Topics for the course will include epidemiologic study designs, issues of confounding and hidden bias, matching methods, propensity score methods, sensitivity analysis, and instrumental variables. Case studies in biomedical researchwill be presented as illustrations.

BSTA 661: Design of Interventional Studies

• Fall term • 0.5 credit unit • Instructor: Alisa Stephens-Shields, PhD • Prerequisites: BSTA 621, BSTA 631 or BSTA 632; permission of instructor.

Description: This course is designed for graduate students in statistics or biostatistics interested in the statistical methodology underlying the design, conduct, and analysis of clinical trials and related interventional studies. General topics include designs for various types of clinical trials (Phase I, II, III), endpoints and control groups, sample size determination, and sequential methods and adaptive design. Regulatory and ethical issues will also be covered.

BSTA 670: Programming and Computation for Biomedical Data Science

• Spring term • 1.0 credit unit • Instructor: Kristin Linn, PhD • Prerequisites: BSTA 651,BSTA 620, BSTA 621 or equivalents, or permission of instructor.

Description: This course concentrates on programming and computational tools that are useful for statistical research and data science practice. Programming will mainly be taught in R and Python with a focus on performance and efficiency, including parallelization techniques. Select computational topics will include computer arithmetic; algorithms and complexity; random number generation; simulation design; bootstrap methods; numerical analysis and optimization; numerical integration; and a number of advanced topics.

BSTA 750: Risk Prediction

• Spring term • .5 credit unit • Instructor: J inbo Chen, PhD • Prerequisites: BSTA 630 and BSTA 632 or the equivalents; Or permission by the instructor;

Description:

This is an advanced elective course for graduate students in Biostatistics, Statistics, Epidemiology, and other BGS disciplines. It will cover various topics for evaluating the performance of biomarkers to predict risk of clinical or disease outcomes, specifically including relative, absolute and competing risks for binary and time-to-disease outcomes; statistical inference for quantifying predictive accuracy with binary and time-to-event outcomes; statistical methods and inference for case-control study designs; Efficient study design issues for biomarker evaluation. This course is designed to help students 1) understand various concepts of risk in the medical literature; 2) understand various statistical methods for evaluating prediction performance of biomarkers and diagnostic tests and for designing efficient biomarker studies; 3) improve the ability to read critically papers published in statistical and medical journals on related topics; and 4) develop research ideas for risk prediction. Upon successfully completing this course, students will be able to: 1) Conduct statistical analysis for evaluating prediction performance of biomarkers and diagnostic tests; 2) Have a better ability to read and understand papers published in statistical and medical journals on related topics; and 3) Be well prepared to work on related topics for dissertation.

BSTA 751: Statistical Methods for Neuroimaging

• Spring term • 1.0 credit unit • Instructor: Russell Taki Shinohara, PhD • Prerequisites: BSTA 621, BSTA 651; permission of instructor.

Description: This course is intended for students interested in both statistical methodology, and the process of developing this methodology, for the field of neuroimaging. This will include quantitative techniques that allow for inference and prediction from ultra-high dimensional and complex images. In this course, basics of imaging neuroscience and preprocessing will be covered to provide students with requisite knowledge to develop the next generation of statistical approaches for imaging studies. High-performance computational neuroscience tools and approaches for voxel- and region-level analyses will be studied. The multiple testing problem will be discussed, and the state-of-the art in the area will be examined. Finally, the course will end with a detailed study of multivariate pattern analysis, which aims to harness patterns in images to identify disease effects and provide sensitive and specific biomarkers. The student will be evaluated based on 3 homework assignments and a final in-class presentation.

BSTA 752: Categorical Data Analysis II

• Spring term (Course no longer offered) • 1.0 credit unit • Instructor (s): TBA • Prerequisites: BSTA 652; permission of instructor.

Description: In this course, students present and discuss methodological papers chosen by the instructor from the literature on advanced categorical methods in a variety of areas. These areas include accounting for correlated data with population-averaged and random and fixed effects models fitted with estimation procedures including generalized estimating equations, maximum likelihood, penalize quasilikelihood, and conditional likelihood methods. Additional topics including accommodating non-ignorable missing data, confounding by cluster, treatment non-adherence in randomized trials, mediation analysis, and latent class and latent variable models. Software for implementing these methods will also be considered in the context of some examples from medical research. The student presentations will be reviewed by peer students in the course, who will provide feedback. Grades will be based on the instructor evaluations of these presentations and ensuing discussion. In addition, a data analysis project will be handed in as part of the final grade.

BSTA 753: Survival Analysis II

• (NOTE: Course replaced by BSTA 754: Advanced Survival Analysys) • 1.0 credit unit • Instructor (s): • Prerequisites: Prerequisites: BSTA 653, BSTA 622 (may be taken concurrently).

Description: This course discusses the theoretical basis of concepts and methodologies associated with survival data and censoring, nonparametric tests, and competing risk models. Much of the theory is developed using counting processes and martingale methods. Material is drawn from recent literature.

BSTA 754: Advanced Survival Analysis

• Fall term • 0.5 credit unit • Instructor: D oug Schaubel, PhD • Prerequisites: BSTA 622 (may be taken concurrently); permission of instructor.

Description: This advanced survival analysis course will cover statistical theory in counting processes, large sample theory using martingales, and other state of the art theoretical concepts useful in modern survival analysis research. Examples in deriving rank-based tests and Cox regression models as well as their asymptotic properties will be demonstrated using these theoretical concepts. Additional potential topics may include competing risk, recurrent event analysis, multivariate failure time analysis, joint modeling of survival and longitudinal data, sample size calculations, multistate models, and complex sampling schemes involving failure time data.

BSTA 770: Nonparametric Inference (STAT 915)

Bsta 771: applied bayesian analysis.

• Spring term • 1.0 credit unit • Instructor: TBA • Prerequisites: BSTA 620, BSTA 621, BSTA 651; permission of instructor.

Description: This course introduces Bayesian methods from philosophical, theoretical, and practical perspectives. These methods are compared and contrasted with alternatives, such as maximum likelihood and semiparametric methods. Core topics include Bayes' theorem, the likelihood principle, selection of prior distributions (both informative and non-informative), and computational methods for sampling from the posterior distributions. Bayesian approaches to linear models, generalized linear models, and survival models are presented, along with methods for model checking and model choice such as posterior predictive distributions and Bayes factors. Computational methods include MCMC, Gibbs sampling, metropolis algorithms, and slice sampling. Advanced topics include Bayesian non-parametric models and data augmentation. The course emphasizes the development and estimation of hierarchical models as a means of modeling complicated real-world problems.

BSTA 774: Statistical Methods for Evaluating Diagnostic Tests

• Fall term • 1.0 credit unit • Instructor (s): TBA • Prerequisites: BSTA 621 or equivalent; permission of instructor.

Description: Topics include estimation of ROC curves; comparison of multiple diagnostic tests; development of diagnostic tests using predictive models; effects of measurement errors; random-effects models for multi-reader studies; verification bias in disease classification; methods for time-dependent disease classifications; study design; related software; meta-analyses for diagnostic test data; and current topics in the statistical literature.

BSTA 775: Sample Survey Methods (STAT 920)

Bsta 779: semiparametric inferences and biostatistics.

• Spring term (Course not offered every year) • 1.0 credit unit • Instructor (s): TBA • Prerequisites: The course is designed for students in biostatistics, statistics, or other strongly quantitative disciplines. BSTA 621/622 or equivalent; ability to program in R/S-Plus, SAS, Stata or Matlab; permission of the instructor.

Description: This course will expose students to semiparametric inference theory through its applications to cutting-edge research topics in biostatistics, including two-phase design problems and modeling problems in genetic epidemiology. Thus, this course will benefit those who wish to advance their theoretical statistical training, those who wish to explore biostatistics research in the area of two-phase design problems and in genetic epidemiology, and those who wish to deepen their understanding of commonly used semiparametric biostatistical methods such as partial likelihood inference for Cox regression and the prospective analysis of retrospective case-control studies.

BSTA 781: Asymptotic Theory with Biomedical and Psychosocial Applications

• Fall term (Course not offered every year) • 1.0 credit unit • Instructor (s): TBA • Prerequisites: BSTA 621, BSTA 622, BSTA 630, BSTA 631 or BSTA 632, BSTA 651; permission of instructor.

Description: This course is an introduction to the asymptotic theory of statistics, with an array of applications to motivate as well as demonstrate its utility in addressing problems in biomedicine and psychosocial research. Notions of convergence of random sequences and common asymptotic techniques are introduced without measure theory. In addition to classical likelihood-based asymptotic theory, this course also focuses on distribution-free inference from estimating equations and U-statistics. Examples from AIDS, genetic, and psychosocial research are presented to motivate the methods development and to demonstrate the utility of the asymptotic theory.

BSTA 782: Statistical Methods for Incomplete Data

• Spring term (Course not offered every year) • 1.0 credit unit • Instructor (s): Qi Long, PhD • Prerequisites: BSTA 621 required; BSTA 670 recommended; permission of instructor.

Description: This course reviews the theory and methodology of incomplete data, covering ignorability and the coarse-data model, including MAR, MCAR and their generalizations; computational methods such as the EM algorithm and its extensions; methods for handling missing data in commonly used models such as the generalized linear model and the normal mixed model; methods based on imputation; diagnostics for sensitivity to nonignorability; and nonignorable modeling and current topics.

BSTA 783: Multivariate and Functional Data Analysis

• Fall term • 1.0 credit unit • Instructor (s): TBA • Prerequisites: BSTA 621, BSTA 651, BSTA 656; permission of instructor.

Description: This course covers both the classical theory and recent methods for multivariate exploratory analysis, as well as techniques for handling functional data. The first part reviews classical multivariate exploratory methods such as principal component analysis, factor analysis, cluster analysis and discriminant analysis, as well more recent methods, such as structural equations models, neural networks and classification trees. The second part covers the more advanced topic of functional data analysis, including graphical representations, principal component analysis and linear models for functional data.

BSTA 784: Analysis of Biokinetic Data

• Fall term ( Note: Course no longer offered ) • 0.5 credit unit • Instructor (s): TBA • Prerequisites: Introductory statistics including regression and hypothesis testing; EPID 520, BSTA 630 or equivalent; permission of instructor.

Description: The time-course of a drug monitored via circulation samples gives us a comprehensive account of the number and sizes of body pools within which the drug distributes before its eventual elimination. Furthermore, the pattern of change of the time-course with increasing drug doses will expose the nature of the mechanisms facilitating that transport and metabolism. How these features are elucidated falls under the general topic of Compartmental Analysis, and the tools and technique of kinetics as well as those of drug dynamics form a part of this topic investigating 'the analysis of biokinetic data'. Additionally we will be exploring how metabolic challenges, such as the glucose challenge, the TRH challenge, and the epinephrine challenge expose aspects of the functionality of their targeted tissues, and, most specifically, we will show how indices relating to insulin resistance are derived.

BSTA 785: Statistical Methods for Genomic Data Analysis

• Spring term • 1.0 credit unit • Instructor (s): TBA • Prerequisites: BSTA 620, BSTA 621, these courses can be taken concurrently with this course; permission of the instructor.

Description: This course covers statistical, probabilistic and computational methods for analyzing high-throughput genomic data. With the advent of inexpensive DNA sequencing, statistical genetics is undergoing the transition to big data. The following materials will be selectively covered. Basics of Molecular Biology and Population Genetics; Large-scale inference, empirical Bayes methods, False discovery rate theory and applications to differential expression analysis, RNA-seq data analysis; Network-based analysis of genomic data and Hidden Markov random field models; Sparse segment identification in high dimensional settings with applications to copy number variation analysis using SNP chip data and next generation sequencing data; High dimensional regression and regularization methods in genomics; Genetic networks and Gaussian graphical models, Conditional Gaussian graphical models, Causal inference and directed graphs; Analysis of microbiome data and high dimensional compositional data; Kernel methods and analysis of rare variants; Other miscellaneous topics in analysis of next generation sequencing data (e.g. ChIP-seq data, epigenomics data); Bioconductor/R programs for genomic data analysis.

BSTA 786: Advanced Topics in Clinical Trials

• Spring term • 0.5 credit unit • Instructor (s): TBA • Prerequisites: BSTA 661; permission of instructor.

Description: This course will cover in some depth selected topics of interest in clinical trials that are discussed only minimally in the introductory clinical trials courses. Topics may include methods of treatment allocation and blinding, sequential and/or adaptive trial designs, methods of handling missing data, design of active control/noninferiority trials, constructed endpoints, and other topics based on interest of registrants.

BSTA 787: Methods for Statistical Genetics and Genomics in Complex Human Disease

• Spring term • 1.0 credit unit • Instructor (s): Mingyao Li, PhD • Prerequisites: Introductory graduate-level courses in statistics (such as BSTA 630-632 or EPID 520-521) are required; or permission of the instructor.

Description: This is an advanced elective course for graduate students in Biostatistics, Statistics, Epidemiology, Bioinformatics, Computational Biology, and other BGS disciplines. This course will cover statistical methods for the analysis of genetics and genomics data. Topics covered will include genetic linkage and association analysis, analysis of next-generation sequencing data, including those generated from DNA sequencing and RNA sequencing experiments. Students will be exposed to the latest statistical methodology and computer tools on genetic and genomic data analysis. They will also read and evaluate current statistical genetics and genomics literature.

BSTA 788: Functional Data Analysis

• Spring term • 1.0 credit unit • Instructor (s): Wensheng Guo, PhD • Prerequisites: BSTA 621 and BSTA 651; permission from the instructor.

Description: This course will cover both the basic techniques in functional data analysis and the latest methodological developments in the area. The first half of the course will cover graphical representations, smoothing techniques, curve registration, functional linear models, functional principal component and discriminant analysis. The first half will follow the book by Ramsay and Silverman (2005). The first half aims to prepare the students to analyze functional data. The second half will cover several special topics of the recent development. We will cover around twenty papers in the second half. Each student is expected to complete a term project at the end. The ideal term project can potentially lead to a dissertation topic.

BSTA 789: Big Data

• Fall term • 1.0 credit unit • Instructor: Hongzhe Li, PhD • Prerequisites: BSTA 621 and BSTA 622. BSTA 622 can be taken concurrently.

Description: Selected topics from public health and biomedical research where "Big data" are being collected and methods are being developed and applied, together with some core statistical methods in high dimensional data analysis. Topics include dimension reduction, detection of novel association in large datasets, regularization and high dimensional regression, ensemble learning and prediction, kernel methods, deep learning and network analysis. R programs will be used throughout the course, other standalone programs will also be used.

BSTA 790: Causal Inference in Biomedical Research

• Fall term • 1.0 credit unit • Instructor: Nandita Mitra, PhD , and Peter Yang, PhD • Prerequisites: BSTA 621, BSTA 622; permission of instructor.

Description: This course considers approaches to defining and estimating causal effects in various settings. The potential-outcomes approach provides the framework for the concepts of causality developed here, although we will briefly consider alternatives. Topics considered include: the definition of effects of scalar or point treatments; nonparametric bounds on effects; identifying assumptions and estimation in simple randomized trials and observational studies; alternative methods of inference and controlling confounding; propensity scores; sensitivity analysis for unmeasured confounding; graphical models; instrumental variables estimation; joint effects of multiple treatments; direct and indirect effects; intermediate variables and effect modification; randomized trials with simple noncompliance; principal stratification; effects of time-varying treatments; time-varying confounding in observational studies and randomized trials; nonparametric inference for joint effects of treatments; marginal structural models; and structural nested models.

BSTA 798: Advanced Topics in Biostatistics I

• Spring term • 0.5 credit unit • Instructor (s): TBA • Prerequisites: TBA; permission of instructor.

Description: This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics in biostatistical methodology will vary from year to year. Methodology related to clinical trials, missing data, functional data analysis, generalized linear models, statistical genetics, advances in Bayesian methodology are examples of areas that may be covered.

BSTA 799: Advanced Topics in Biostatistics II

• Fall/Spring term • 0.5 credit unit • Instructor (s): TBA • Prerequisites: TBA; permission of instructor.

BSTA 812: Seminar in Probability Theory (STAT 955)

Bsta 820: statistical inference iii (stat 552), bsta 852: forecasting and time series (stat 910), bsta 870: seminar in advanced applications of statistics (stat 991), bsta 920: guided tutorial: research (0.5 - 3.0 course units), bsta 995: dissertation research (0.5 – 3.0 course units), bsta 999: independent study (0.5 - 1.0 course unit).

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School of Public Health

Courses in epidemiology and biostatistics.

Course listings on the School of Public Health website are updated each semester. This page is meant to be used as a guide for academic planning. For official course listings, always consult the UIC Course Catalog.

Course listings Heading link Copy link

BSTT 400. Biostatistics I. 4 hours.

Descriptive statistics, basic probability concepts, one- and two-sample statistical inference, analysis of variance, and simple linear regression. Introduction to statistical data analysis software. Course Information: Enrollment restricted to public health students and healthcare administration students; other graduate, professional and advanced undergraduate students admitted by consent as space permits. To obtain consent, see the SPH registrar.

BSTT 401. Biostatistics II. 4 hours.

Simple and multiple linear regression, stepwise regression, multifactor analysis of variance and covariance, non-parametric methods, logistic regression, analysis of categorical data; extensive use of computer software. Course Information: Prerequisite(s): BSTT 400 .

BSTT 402. Health Policy for Epidemiologists and Biostatisticians. 1 hour.

Epidemiological data and biostatistics provide the evidence to support the development and justification of policies. Public health policy interventions, factors influencing political and social environments and the evaluation of policy-making. Course Information: Same as EPID 402 .

BSTT 413. Introduction to Data Analysis w/ R. 2 hours.

An introductory overview of statistical programming using R in the context of describing and analyzing public health data. Course Information: Extensive computer use required. Recommended background: BSTT 400 ; or IPHS 402 .

BSTT 426. Health Data Analytics Using Python Programming. 3 hours.

Covers methodologies of online data collection by Python Programming. Topics include: introduction to Python, Information retrieval Techniques, Retrieving and analyzing information from medical data sources, IBM Bluemix. Course Information: Extensive computer use required. Prerequisite(s): No prerequisites except that some very basic understanding of programming in SAS or R or some other programming language is needed along with basic analytical knowledge. Motivation to learn programming concepts is key. Recommended Background: IPHS 402 or EPID 406 or BSTT 494 .

BSTT 494. Introductory Special Topics in Biostatistics. 1-4 hours.

Special topics in biostatistics. Content varies. Course Information: May be repeated. Students may register in more than one section per term. Prerequisite(s): Consent of the instructor.

BSTT 505. Logistic Regression and Survival Analysis. 2 hours.

Interpretation of logistic regression and survival analysis models. Running logistic and proportional hazards regression models and constructing life-tables using SAS. Course Information: Previously listed as BSTT 402 . Prerequisite(s): BSTT 400 and BSTT 401 .

BSTT 506. Design of Clinical Trials. 3 hours.

Rationale for clinical trials, blinding, ethical issues, methods of randomization, crossover trials, power and sample size calculations, data management, protocol deviation, data analysis, interim analysis. Course Information: Previously listed as BSTT 430. Prerequisite(s): BSTT 400 and BSTT 401 .

BSTT 507. Sampling and Estimation Methods Applied to Public Health. 3 hours.

The purpose of this course is to provide a comprehensive overview of current methods and issues in survey sample design and associated estimation procedures. Course Information: Previously listed as BSTT 440. Credit is not given for BSTT 507 if the student has credit in STAT 431 . Restriction applies only to certification for students pursuing the Interdepartmental Graduate Concentration in Survey Methodology. Prerequisite(s): BSTT 401 or BSTT 502 or consent of the instructor.

BSTT 510. Biostatistics Theory I. 3 hours.

Part of a two-semester probability-statistical inference sequence with an emphasis on public health- and biostatistics-related aspects of the probabilistic paradigm. Coverage includes probability, and random variables. Course Information: Extensive computer use required. Prerequisite(s): Two semesters of college calculus; and consent of the instructor.

BSTT 511. Biostatistics Theory II. 4 hours.

Provides to the students approach to probability and statistical inference and their application to research in public health and health science fields. This course covers the fundamental theories of biostatistical inferential procedures. Course Information: Extensive computer use required. Prerequisite(s): BSTT 510 ; or consent of the instructor.

BSTT 521. Applied Multivariate Analysis. 3 hours.

Analysis of vector of responses; MANOVA, data reduction methods; introduction to cluster analysis, discriminant analysis, and structural equation models. Course Information: Prerequisite(s): BSTT 537 and consent of the instructor.

BSTT 523. Biostatistics Methods I. 4 hours.

Foundations for and introduction to statistical inference, including one- and two-sample problems; regression analysis, including multiple regression and indicator variables. Course Information: Previously listed as BSTT 502. Prerequisite(s): College calculus, including multivariable calculus, concurrent registration in BSTT 524 , and consent of the instructor.

BSTT 524. Biostatistics Laboratory. 2 hours.

Use of spreadsheets for statistical investigations; use of statistical software; matrix theory, including methods relevant in biostatistical analysis. Course Information: Previously listed as BSTT 503. Prerequisite(s): Concurrent registration in BSTT 523 and consent of the instructor.

BSTT 525. Biostatistics Methods II. 4 hours.

Analysis of variance and multiple comparisons; model building and diagnostics; generalized linear models; logistic and Poisson regression; introduction to repeated measures and mixed models. Course Information: Previously listed as BSTT 504. Prerequisite(s): Grade of B or better in BSTT 523 and Grade of B or better in BSTT 524 , or consent of the instructor.

BSTT 527. Statistical Learning in Health Analytics. 3 hours.

Covers multivariate statistical methods such as LASSO, ElasticNet, Decision Trees etc, and machine learning methods Bagging, random Forest, Boosting etc in context of statistical learning in PH applications. Course Information: Extensive computer use required. Prerequisite(s): IPHS 402 and BSTT 505 ; or BSTT 523 and BSTT 525 . Recommended Background: IPHS 402 or EPID 406 or BSTT 494 .

BSTT 528. Machine Learning in Health Analytics. 3 hours.

Covers several advanced statistical and machine learning methods including graphical models, natural language processing, neural nets, hierarchical modeling, annealing, deep belief networks. Course Information: Extensive computer use required. Prerequisite(s): BSTT 526 and BSTT 527 .

BSTT 529. Health Analytics Investigations. 2 hours.

This is a main competency measure of MS in Public Health with Health Analytics concentration. Course Information: Satisfactory/Unsatisfactory grading only. Extensive computer use required. Prerequisite(s): BSTT 526 and BSTT 527 and BSTT 528 ; or consent of the instructor.

BSTT 535. Categorical Data Analysis. 3 hours.

Contingency tables and their tests, measures of association, stratified analysis, logistic regression, generalized linear model, Poisson regression, log-linear model, matched data, marginal homogeneity, ordinal data. Course Information: Previously listed as BSTT 511 . Prerequisite(s): Grade of B or better in BSTT 525 ; and STAT 411 , or consent of the instructor.

BSTT 536. Survival Analysis. 3 hours.

Concepts of lifetime or survival distributions, especially with censored data; nonparametric estimation of the survival function; rank tests; proportional hazards regression models; parametric models. Course Information: Previously listed as BSTT 512. Prerequisite(s): Grade of B or better in BSTT 525 and Grade of B or better in STAT 411 , or consent of the instructor.

BSTT 537. Longitudinal Data Analysis. 4 hours.

Application and theory of models for longitudinal data analysis for both continuous and categorical response data, including use of statistical software for these methods. Course Information: Previously listed as BSTT 513. Prerequisite(s): Grade of B or better in STAT 411 and Grade of B or better in BSTT 525 , or consent of the instructor.

BSTT 538. Biostatistical Consulting. 2 hours.

Discussion of techniques required for successful biostatistical consultation; effective communication, problem formulation, data analysis, oral and written reports, supervised consulting experience. Course Information: Previously listed as BSTT 514. Prerequisite(s): Grade of B or better in BSTT 525 and consent of the instructor. Restricted to students enrolled in the biostatistics major.

BSTT 550. Biostatistical Investigations. 4 hours.

Analysis of several large data sets that will require integration of numerous biostatistical tools; written summarization and discussion of results. Course Information: Previously listed as BSTT 522. Prerequisite(s): Grade of B or better in BSTT 535 and Grade of B or better in BSTT 536 and Grade of B or better in BSTT 537 and Grade of B or better in BSTT 538 and Grade of B or better or concurrent registration in BSTT 521 .

BSTT 560. Large Sample Theory. 2 hours.

Deriving and applying large sample statistical theories. The primary focus will be in limit theorums and their applications in biostatistical problems. Course Information: Meets eight weeks of the semester. Previously listed as BSTT 534. Prerequisite(s): Open only to PhD degree students; or consent of the instructor. Adequate training at the level of intermediate mathematical statistics. Masters degree in biostatistics or mathematics.

BSTT 561. Advanced Statistical Inference. 3 hours.

An in-depth consideration of some important ideas of statistical inference including large-sample theory, estimation and testing. Specific topics to be covered include asymptotic theory, parameter estimation methods and hypothesis testing. Some computer use in class. Course Information: Previously listed as BSTT 531. Prerequisite(s): Open only to Ph.D. degree students; and consent of the instructor. Recommended background: MS degree in Biostatistics or the equivalent.

BSTT 562. Linear Models. 4 hours.

Generalized inverse matrices; distributions for quadratic forms; estimability and testable hypotheses; constrained linear model; applications to regression, ANOVA, ANCOVA models; variance component models. Course Information: Previously listed as BSTT 533. Prerequisite(s): Open only to Ph.D. degree students; or consent of the instructor. Recommended background: MS degree in Biostatistics or the equivalent.

BSTT 563. Generalized Linear Models. 4 hours.

Teaches students the components of generalized linear models and their extensions. Course Information: Previously listed as BSTT 541. Prerequisite(s): BSTT 561 and concurrent registration in or prior completion of BSTT 560 . Open only to PhD degree students; or consent of the instructor. Adequate training at level of intermediate mathematical statistics. Masters degree in biostatistics, mathematical statistics or methematics.

BSTT 564. Missing Data. 4 hours.

Students will learn the statistical methods used for analyzing data with missing values. Course Information: Previously listed as BSTT 542. Prerequisite(s): BSTT 561 and concurrent registration in or prior completion of BSTT 560 . Open only to PhD degree students; or consent of the instructor. Adequate training at level of intermediate mathematical statistics. Masters degree in biostatistics, mathematical statistics or methematics.

BSTT 565. Computational Statistics. 4 hours.

Developing a broad and thorough working knowledge of modern statistical computing and computational statistics on a practical, conceptual, philosophical and mathematical level. Course Information: Previously listed as BSTT 543. Extensive computer use required. Prerequisite(s): Concurrent registration in or prior completion of BSTT 560 . Open only to Ph.D. degree students; or consent of the instructor. Adequate training at level of intermediate mathematical statistics. Masters degree in biostatistics, mathematical statistics or mathematics.

BSTT 566. Bayesian Methods. 4 hours.

Developing a broad and thorough working knowledge of Bayesian applications on a practical, conceptual, philosophical and mathematical level. Course Information: Previously listed as BSTT 544. Prerequisite(s): Concurrent registration in or prior completion of BSTT 560 . Open only to Ph.D. degree students; or consent of the instructor. Adequate training at level of intermediate mathematical statistics. Masters degree in biostatistics, mathematical statistics or mathematics. Class Schedule Information: Extensive computer use required.

BSTT 567. Advanced Survival Analysis. 4 hours.

Methods of analysis for multivariate survival data, including transition models and shared frailty models. Theory behind existing methodology is covered as well as implementation. Course Information: Prerequisite(s): Grade of B or better or concurrent registration in BSTT 536 ; and consent of the instructor. Recommended background: Intended for students in the Biostatistics PhD program.

BSTT 568. Programming and Simulation in R. 2 hours.

Applications in R on a practical, conceptual, philosophical and mathematical level. The focus is on simulation and computation, not on data analysis. Course Information: Extensive computer use required. Prerequisite(s): BSTT 400 ; or both BSTT 523 and BSTT 524 ; and graduate or professional standing; or consent of the instructor.

BSTT 594. Special Topics in Biostatistics. 1-4 hours.

Advanced special topics. Content varies. Course Information: May be repeated. Students may register in more than one section per term. Prerequisite(s): Consent of the instructor.

BSTT 595. Biostatistics Research Seminar. 1 hour.

EPID 400. Principles of Epidemiology. 3 hours.

Introduction to descriptive and analytic epidemiology, determinants of health and disease in populations, and application of epidemiologic methods to disease control; includes use of basic epidemiologic software. Course Information: Prerequisite(s): Credit or concurrent registration in BSTT 400 or consent of the instructor. Enrollment restricted to public health students; other graduate, professional, and advanced undergraduate students admitted by consent as space permits. To obtain consent, see the SPH registrar.

EPID 402. Health Policy for Epidemiologists and Biostatisticians. 1 hour.

EPID 403. Introduction to Epidemiology: Principles and Methods. 3 hours.

Introduction to descriptive and analytic epidemiology, and determinants of health and disease in populations. Measures of occurrence, association and statistical testing will be addressed, along with study designs, bias and confounding. Course Information: Prerequisite(s): Credit or concurrent registration in BSTT 400 and graduate or professional standing; or consent of the instructor.

EPID 404. Intermediate Epidemiologic Methods. 4 hours.

Introduction to multivariable methods in Epidemiology, including stratified analysis and regression modeling. Students will use statistical software to analyze data from epidemiologic studies. Course Information: Prerequisite(s): EPID 403 and EPID 406 ; and credit or concurrent registration in BSTT 401 ; and graduate or professional standing; or consent of the instructor.

EPID 406. Epidemiologic Computing. 3 hours.

Hands on course for students using SAS for epidemiologic analysis. Addresses practical issues in statistical programming for epidemiology students. Course Information: Extensive computer use required. Prerequisite(s): Credit or concurrent registration in BSTT 400 and Credit or concurrent registration in EPID 403 ; or Credit or concurrent registration in BSTT 400 and Credit or concurrent registration in EPID 400 ; or consent of the instructor.

EPID 408. Biological, Chemical, Explosives, and Nuclear Weapons as Public Health Threats. 3 hours.

Preparation, understanding of threats, and rescue & response issues pertaining to potential terrorist incidents from a public health perspective. Course Information: Same as EOHS 408 . Prerequisite(s): Graduate or professional standing; or consent of the instructor. Recommended background: EOHS 400 and EPID 410 .

EPID 409. The Epidemiology of HIV/AIDS. 2 hours.

Review of the HIV/AIDS pandemic and the global response to it focusing on patterns of transmission, risk factors and prevention/ intervention. Course Information: Prerequisite(s): EPID 400 or consent of the instructor.

EPID 410. Epidemiology of Infectious Diseases. 2 hours.

Epidemiology of selected infectious diseases, including incidence, prevalence and control of disease. Epidemic investigation is emphasized. Course Information: Prerequisite(s): Credit or concurrent registration in EPID 400 ; or credit or concurrent registration in EPID 403 .

EPID 411. Epidemiology of Chronic Diseases. 3 hours.

Selected topics in chronic diseases with critical analysis of current epidemiologic literature. Course Information: Prerequisite(s): EPID 400 or consent of the instructor.

EPID 412. Introduction to Psychosocial Epidemiology. 2 hours.

Reviews landmark studies of psychosocial and psychiatric disorders in U.S. communities; evaluates research methodology, case definition, identification, and empirical findings. Course Information: Prerequisite(s): EPID 400 or consent of instructor.

EPID 428. Epidemiology of Violence. 2 hours.

Reviews public health aspects of violence-related mortality and morbidity, examines existing data bases and conceptual frameworks focusing on etiology, epidemiology, surveillance and prevention. Course Information: Prerequisite(s): EPID 400 or consent of the instructor.

EPID 471. Population. 3 or 4 hours.

The measurement and study of major trends and differentials in fertility, mortality, migration, growth, and compositional characteristics of the population of the United States and other nations. Course Information: Same as SOC 471 . 3 undergraduate hours. 4 graduate hours. Prerequisite(s): 6 hours of upper-division sociology, including SOC 201 , or consent of the instructor.

EPID 494. Introductory Special Topics in Epidemiology. 1-4 hours.

Special topics in substantive areas of Epidemiology (including infectious disease, chronic disease, environmental/occupational, social). Course content will vary with each offering. Course Information: May be repeated. Students may register in more than one section per term. Prerequisite(s): EPID 400 or EPID 403 or consent of instructor; and graduate or professional standing.

EPID 500. Applied Methods for the Analysis of Epidemiologic Data. 4 hours.

Students will learn how to apply, interpret and report the findings from quantitative analyses of various types of epidemiologic data, including case-control, cohort, longitudinal and meta-analysis. Course Information: Extensive computer use required. Prerequisite(s): EPID 403 and BSTT 400 ; or IPHS 402 ; or IPHS 404 and IPHS 405 ; or consent of the instructor Additional course requirements: EPID 404 , EPID 406 , BSTT 401 , and BSTT 505 .

EPID 501. Advanced Quantitative Methods in Epidemiology. 4 hours.

The main objective of this course is for students to learn how to quantitatively analyze an epidemiologic dataset and interpret findings in the context of theoretical causal models. Course Information: Prerequisite(s): EPID 403 and EPID 404 ; and BSTT 401 and BSTT 505 ; and consent of the instructor.

EPID 509. Current Topics in HIV/AIDS Research. 3 hours.

Designed to be a collaboration among advanced students in the Graduate College and the instructor to explore, critique and analyze in depth selected topics in current research and practice around HIV/AIDS prevention. Course Information: Prerequisite(s): Grade of B or better in EPID 403 or grade of B or better in EPID 409 ; or consent of the instructor.

EPID 510. Advanced Epidemiology of Infectious Diseases. 2 hours.

Controversies regarding the etiology, transmission and prevention of selected infectious diseases. Literature reviews and study designs developed by students are a prominent part of course. Course Information: Prerequisite(s): EPID 410 or consent of instructor.

EPID 512. Molecular Epidemiology and Biomarkers of Disease. 3 hours.

Major theoretical concepts and practical issues involved in research involving molecular biomarkers in human populations, emphasizing examples from the cancer research literature. Course Information: Same as PATH 512 . Prerequisite(s): Consent of the instructor. Recommended background: Some biology or medical background is recommended for epidemiology students taking this course.

EPID 513. Epidemiology of Aging. 2 hours.

Current methodologic and public health issues in the epidemiology of aging will be explored. Course Information: Prerequisite(s): EPID 401 or EPID 411 ; and consent of the instructor.

EPID 515. Cancer Epidemiology. 3 hours.

Critical review of topics and issues relevant to cancer epidemiology, to promote synthesis of current knowledge and awareness of research issues. Course Information: Prerequisite(s): EPID 401 and EPID 411 ; or consent of the instructor.

EPID 516. Advanced Cancer Epidemiology. 2 hours.

Critical review of the epidemiology of selected cancer sites to promote synthesis of knowledge, awareness of methodologic issues, and stimulate future research. Course Information: Prerequisite(s): EPID 501 and EPID 515 ; or consent of the instructor. Recommended background: EPID 520 .

EPID 517. Epidemiology of Cardiovascular Diseases. 2 hours.

Epidemiology and risk factors of cardiovascular diseases. Course Information: Prerequisite(s): EPID 411 or consent of instructor.

EPID 518. The Epidemiology of Pediatric Diseases. 3 hours.

Provides students with experience in pediatric epi through review of seminal studies and available child health data. Condition-specific lectures include discussions of study design and methodological considerations specific to studying children. Course Information: Same as CHSC 518 . Extensive computer use required. Prerequisite(s): EPID 404 and EPID 406 and BSTT 401 ; and graduate or professional standing; or consent of the instructor. Recommended background: EPID 501 .

EPID 519. Research Protocol and Grant Development. 1 hour.

A review of funding options and examples of developing fundable research proposals. Course Information: Satisfactory/Unsatisfactory grading only. Prerequisite(s): EPID 400 .

EPID 520. Genetics in Epidemiology. 2 hours.

Topics in genetic/molecular epidemiology, including genetics, population genetics, molecular biology, molecular genetics. Familiarizes students with laboratory/statistical concepts and applications in epidemiological studies. Course Information: Prerequisite(s): EPID 401 or consent of the instructor.

EPID 526. Pharmacoepidemiology. 3 hours.

Provides an introduction to pharmacoepidemiology and key concepts and principles that are unique to the study of medications in large populations. Course Information: Same as PSOP 526 . Previously listed as PSOP 426. Extensive computer use required. Taught online. A computer with sufficient memory and Internet access is required. Prerequisite(s): EPID 400 or EPID 403 or consent of the instructor.

EPID 529. Epidemiology of Sexually Transmitted Infections. 3 hours.

Students in this class will examine the epidemiology of sexually transmitted infections (STIs), the etiology of the specific diseases, and how these factors are relevant to their control. Course Information: Prerequisite(s): Credit or concurrent registration in EPID 404 ; and graduate or professional standing; or consent of the instructor.

EPID 530. Current Topics in Occupational and Environmental Epidemiology. 2 hours.

Reviews the literature on health effects of environmental and occupational exposures and integrates our current knowledge with relevant policy issues. Course Information: Same as EOHS 530 . Meets eight weeks of the semester. Prerequisite(s): EPID 403 ; or consent of the instructor.

EPID 535. Applied Methods in Occupational Epidemiology. 2 hours.

Provides students with knowledge of the study designs, measures, and experience in applying statistical methods commonly used in occupational epidemiology. Includes didactic lectures and case studies. Course Information: Same as EOHS 535 . Extensive computer use required. Prerequisite(s): Credit or concurrent registration in EPID 404 and EPID 406 and BSTT 401 ; and graduate or professional standing; or consent of the instructor. Recommended background: EOHS 400 .

EPID 536. Applied Methods in Environmental Epidemiology. 2 hours.

Provides students with experience in environmental epidemiology methodology through review of literature; discussion of study design and analysis; and analysis of existing data from the National Health and Nutrition Examination Survey. Course Information: Same as EOHS 536 . Extensive computer use required. Prerequisite(s): Credit or concurrent registration in EPID 404 and EPID 406 and Credit or concurrent registration in BSTT 401 ; and graduate or professional standing; or consent of the instructor. Recommended background: Credit or concurrent registration in EOHS 400 .

EPID 545. Reproductive and Perinatal Health. 3 hours.

Examines the epidemiology of key reproductive and perinatal health outcomes and cutting edge research issues. Course Information: Same as CHSC 545 . Prerequisite(s): IPHS 402 ; and graduate or professional standing; or approval of the department.

EPID 548. Readings in Reproductive and Perinatal Epidemiology. 3 hours.

Advanced seminar in reproductive/perinatal epidemiology with particular emphasis on methodological issues. Course Information: Same as CHSC 548 . Prerequisite(s): CHSC 511 and EPID 402 and EPID 404 ; and graduate or professional standing; or approval of the department. Recommended background: Maternal and child health and epidemiology.

EPID 549. Advanced Applied Methods in MCH Epidemiology. 3 hours.

Gives conceptual and technical understanding of statistical and epidemiological methods, builds skills/proficiency in applying these. Attention is given to data handling tasks and to statistical/epidemiologic strategies for analysis and presentation. Course Information: Same as CHSC 549 . Prerequisite(s): EPID 402 or EPID 404 ; and BSTT 401and EPID 406 ; or consent of the instructor. Recommended background: Credit or concurrent registration in EPID 501 .

EPID 550. Public Health Surveillance. 3 hours.

Examines the fundamental public health activity known as surveillance from several angles including history, design, illustrative examples, evaluation, data analysis, and communication of findings. Course Information: Meets eight weeks of the semester. Prerequisite(s): EPID 403 .

EPID 554. Occupational and Environmental Epidemiology. 2 hours.

Methods and issues of environmental epidemiology: outbreak, clusteranalysis, cross-sectional, case-control, cohort, ecological, and time series designs; contemporary issues: cancer and reproductive hazards. Course Information: Same as EOHS 554 . Prerequisite(s): EPID 401 and BSTT 401 and EOHS 400 ; or consent of the instructor.

EPID 555. Outbreak Investigation and Field Epidemiology. 3 hours.

Emphasize practical issues and decisions that arise during outbreak investigations and will try to prepare the student for participating in and leading outbreak investigations. Course Information: Meets eight weeks of the semester. Prerequisite(s): EPID 403 ; or consent of the instructor.

EPID 571. Injury Epidemiology and Prevention. 3 hours.

Covers general principles of injury epidemiology and intervention research and will engage students in development and application of preventive activities in workplaces and in the community. Course Information: Same as EOHS 571 . Prerequisite(s): Grade of B or better in EPID 400 or Grade of B or better in EPID 403 ; and graduate or professional standing; or consent of the instructor. Recommended background: Grade of B or better in EOHS 400 .

EPID 591. Current Epidemiologic Literature. 2 hours.

Student presentation of recently published scientific papers of epidemiologic interest, to promote breadth of knowledge and critical examination of evidence. Course Information: Satisfactory/Unsatisfactory grading only. May be repeated. Prerequisite(s): EPID 401 or EPID 403 or consent of instructor.

EPID 594. Advanced Special Topics in Epidemiology. 1-4 hours.

Advanced special topics in substantive areas of Epidemiology (including infectious disease, chronic disease, environmental/occupational, social, methods, etc). Course content will vary with each offering. Course Information: May be repeated. Students may register in more than one section per term. Prerequisite(s): EPID 401 or EPID 403 or consent of instructor.

EPID 595. Epidemiology Research Seminar. 1 hour.

Current developments in theory and application of biostatistics and epidemiology with presentations by faculty and visiting scientists. Course Information: Satisfactory/Unsatisfactory grading only. May be repeated. Prerequisite(s): Credit or concurrent registration in EPID 400 or EPID 403 or consent of the instructor.

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Certificate In Biostatistics, Epidemiology & Research Methodology

The "Certificate in Biostatistics, Epidemiology & Research Methodology" is a comprehensive educational program designed to equip individuals with essential skills and knowledge in the fields of biostatistics, epidemiology, and research methodology. This course offers a thorough exploration of statistical techniques, disease patterns, and research design, catering to professionals and students in various health-related disciplines.

The program is structured to provide participants with a solid foundation in statistical analysis, enabling them to interpret and draw meaningful conclusions from complex healthcare data. It covers a wide array of statistical methods, including data collection, data cleaning, hypothesis testing, regression analysis, and more. Participants learn to use statistical software tools to analyze data effectively, enhancing their ability to contribute to evidence-based decision-making in healthcare and research settings.

Epidemiology, another key component of the course, delves into the distribution and determinants of diseases within populations. Participants gain insights into various study designs, data collection techniques, and the interpretation of epidemiological findings. This knowledge proves invaluable in identifying patterns of diseases, understanding risk factors, and formulating strategies for disease prevention and health promotion.

Research methodology forms an integral part of the curriculum, providing learners with a systematic approach to designing and conducting research studies. Topics covered include research ethics, study design selection, sampling methods, and data analysis planning. By understanding the principles of research methodology, participants are better equipped to formulate research questions, collect relevant data, and generate credible and applicable conclusions.

Specializations

Clinical Trials: Participants interested in clinical research can specialize in designing and analyzing clinical trials, essential for testing the efficacy and safety of medical interventions.

Infectious Disease Epidemiology: This specialization focuses on the patterns and spread of infectious diseases, crucial for responding to outbreaks and designing preventive measures.

Environmental Epidemiology: Participants can delve into the impact of environmental factors on public health, studying associations between exposures and health outcomes.

Genetic Epidemiology: This specialization explores the genetic basis of diseases within populations, aiding in understanding hereditary factors in health and disease.

Cancer Epidemiology: Focusing on cancer patterns and risk factors, this specialization contributes to cancer prevention and control strategies.

In conclusion, the "Certificate in Biostatistics, Epidemiology & Research Methodology" program offers a comprehensive curriculum that combines biostatistics, epidemiology, and research methodology. Its scope encompasses diverse career opportunities, research advancement, healthcare decision-making, policy formulation, and further education. Specializations within the program allow participants to delve deeper into speci...

Colleges Offering Certificate In Biostatistics, Epidemiology & Research Methodology

Eligibility Criteria

1. Educational Background: Candidates are generally required to hold a bachelor's degree in a relevant field from a recognized university or educational institution. Fields such as medicine, pharmacy, nursing, public health, life sciences, statistics, mathematics, or a related discipline are often considered suitable. A strong academic foundation in these disciplines provides the necessary groundwork for understanding the complex concepts and methodologies covered in the program.
2. Academic Performance: Institutions often have minimum academic performance standards that applicants must meet. This could be in the form of a specified minimum grade point average (GPA) or equivalent percentage in their previous degree program. A competitive academic background ensures that participants have the capacity to engage with the rigorous coursework and excel in the program.
3. Prerequisite Knowledge: Basic knowl...

The scope of the "Certificate in Biostatistics, Epidemiology & Research Methodology" program is extensive and multifaceted:

Career Advancement: Graduates of this program can pursue careers in public health agencies, healthcare institutions, pharmaceutical companies, academic research, and consulting firms. They can take up roles such as biostatistician, epidemiologist, data analyst, clinical researcher, and research coordinator.

Research Enhancement: For individuals engaged in research, the program equips them with the skills to design, analyze, and interpret studies effectively. This boosts the quality and credibility of their research outputs.

Healthcare Decision-Making: Professionals working in healthcare settings can utilize their newfound knowledge to make informed decisions based on statistical evidence and epidemiological insights, improving patient c...

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Introduction to Biostatistics

The program's duration typically ranges from 6 months to 1 year, depending on the institution. It's designed to provide a comprehensive understanding of biostatistics, epidemiology, and research methodology within a relatively short timeframe.

This program is ideal for individuals with a background in healthcare, life sciences, statistics, or related fields who wish to enhance their skills in data analysis, disease patterns, and research design. It's suitable for healthcare professionals, researchers, and those looking to pivot their careers toward evidence-based healthcare decision-making.

Graduates of this program can pursue various career paths, including roles as biostatisticians, epidemiologists, clinical researchers, data analysts, and research coordinators. They can find employment in public health agencies, healthcare institutions, pharmaceutical companies, academic research settings, and consulting firms. The program also provides a foundation for advanced studies in biostatistics, epidemiology, or related fields.

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Summer online short-courses through University of Washington Biostatistics

1. summer online short-courses through university of washington biostatistics.

Registration is now open for online short courses for the Summer Institutes in:

Statistics for Clinical & Epidemiological Research (SISCER) , July 8 – August 2, 2024

SISCER offers introductory and advanced short courses in methods for clinical research and epidemiology. Participants will find learning opportunities for clinical trials, causal inference, biomarker research, and analyzing observational data and complex surveys.

Statistics for Big Data (SISBID) , August 12-24, 2024

SISBID is designed to introduce biologists, quantitative scientists, and statisticians to modern statistical techniques for the analysis of biological big data.

SISCER short courses are 1, 2, 3, or 4 days long. SISBID short courses are 3 days long. Discount rates available through June 10. Learn more .

Kathleen Kerr , Director, Summer Institute in Statistics for Clinical and Epidemiological Research

Ali Shojaie , Director, Summer Institute in Statistics for Big Data

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Related content, 2023 summer institute in statistics for clinical & epidemiological research (siscer) hosted by uw biostatistics, summer institute in statistics for big data (sisbid), 2023 uw biostatistics short courses -- early rates extended through june 15, 2019 high-throughput sequencing summer short course..

American Statistical Association 732 North Washington Street Alexandria, VA 22314-1943 Email: [email protected] Phone: (703) 684-1221

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About Us Code of Conduct

The Research Intensive Summer in Epidemiology (RISE) Program aims to provide undergraduates with a comprehensive understanding of the vital link between mathematics, quantitative methods, and public health, helping them realize their interest in pursuing Epidemiology at a career or academic level. Through interactive coursework and hands-on experiences, participants develop analytical skills, track trends, identify risk factors, and devise effective public health strategies. Held in *Boston over six weeks, the program emphasizes quantitative proficiency and practical application through data analysis and strategy development. By gaining a solid understanding of statistical methods and epidemiological principles, interns are equipped for meaningful contributions to public health research, policy-making, and professional roles.

* Location may be subject to change at the discretion of The Department of Epidemiology

During the program, interns will:

Attend Introduction to Epidemiology and Biostatistics courses.
Participate in faculty roundtables.
Engage in writing-intensive courses.
Take part in an R boot camp.
Attend ODI workshops.
Receive support from alumni mentors.
Collaborate on research projects with faculty and postdocs.
Deliver presentations to faculty, staff, current students, and fellow interns to share the conclusions of their research projects.
Network with Harvard faculty and community members.
Cultivate a peer social network through active engagement in various events and gatherings.

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Admissions and Requirements for the Medical College of Wisconsin Biostatistics and Data Science MA Program

Admissions and Tuition

Minimum admission requirements .

Students should satisfy the minimum requirements for admission:

Baccalaureate degree - official transcripts required
Prior coursework in calculus (including integrals, such as Calculus II), probability and/or statistics, linear/matrix algebra, and computer programming experience
An overall grade point average of 3.0 or better
Three letters of recommendation
Applicants who studied overseas or via an online U.S.-based institution are required to take a Test of English as a Foreign Language (TOEFL) or International English Language Testing System (IELTS) and make arrangement for an official score report to be sent directly to the Graduate School. A TOEFL score is 100 or higher or a band score of 6.0 or higher on the IELTS is ideal. Our Institution Code is 1519.

Tuition Information

If you have questions regarding tuition or your account, please contact the Office of Student Accounts, at (414) 955-8172 or [email protected]. Please refer to the All Student Handbook (PDF) for tuition payment policies and information.

Tuition and Fees Schedule
View your Tuition Statement (login required)

Key Areas of Focus

Recommended courses, biology or public health (optional but recommended), computer science, mathematics.

Epidemiology & Biostatistics Online Course

This course provides an introduction to various epidemiological and biostatistical methods used in public health practice and research, with an emphasis on application of appropriate methods and interpretation of results.

About the Epidemiology & Biostatistics Course

This is a three credit hour course designed to provide students with an introduction to various epidemiological and biostatistical methods used in public health practice and research, with an emphasis on application of appropriate methods and interpretation of results. Examples and problems from public health settings will be included. Topics covered include methods of summarizing data and estimation and hypothesis testing techniques, including the t-test, the chi-square test, the analysis of variance, correlation analysis, and linear regression.

After successfully completing this course, students should be able to:

Identify the main types of measurement scales, including quantitative, ordinal and categorical scales
Describe the shape, location, and spread of sample distribution
Compute measures of central tendency (mean, median, and mode) and variability (variance, standard deviation)
Distinguish between discrete and continuous random variables
Determine approximate probabilities for normal random variables
Characterize the sampling distribution of a sample mean from a normal population
Construct and interpret confidence intervals around means
Perform and interpret one-sample, two-sample, and paired t-hypothesis tests on means
Interpret the components of an analysis of variance and perform an F test to compare means
Perform and interpret z-tests and chi-square tests of independence and homogeneity

Students have 3-9 months to complete 38 lessons. Although it is estimated that a quarter of students attending four-year colleges experience moderate of high levels of math anxiety, by following a sequential order that lets us build knowledge and skills, you'll create avenues for success in the course. The course content is organized sequentially, with content, and recordings, building as lessons continue. Each lesson contains links to video presentations, ancillary readings, and other instructional resources selected to enhance your learning experience and support the various topics. Assignments will be used to assess your comprehension and application of those materials. This course has 3 proctored exams. Lesson topics include:

An Introduction to Biostatistics
An Introduction to Epidemiological Study Designs
Observational Studies: Case Report and Case Series Research
Observational Studies: Cohort and Case-Control Study Design
Cross-Sectional Research Study Design
Defining Risk
Experimental Studies: Randomized Control Trials
Quantifying Disease: Prevalence
Quantifying Disease: Incidence
Comparing the Extent of Disease Between Groups
Applied Biostatistics and Measurement Variables
Measuring Dichotomous and Categorical Variables
Measuring Ordinal Variables
Measuring Continuous Variables: Mean, Variance, and Standard Deviation
Measures of Central Tendency
Sampling Techniques
Basic and Conditional Probability
Sensitivity, Specificity, and Independence
Binomial Distribution
Normal Distribution
Normal Distribution and Z-Scores
Percentiles of the Normal Distribution
An Introduction to Confidence Intervals
Calculating a Confidence Interval for a Mean
Calculating a Confidence Interval for a Proportion
Calculating a Confidence Interval for the Difference Between Two Means
Calculating a Confidence Interval for Mean Differences and Crossover Trials
Calculating a Confidence Interval for the Difference Between Proportions
Introduction to Hypothesis Testing Procedures
Applied Hypothesis Testing Procedures
Hypothesis Testing for a Mean
Hypothesis Testing for a Proportion
Hypothesis Testing for Categorical and Ordinal Outcomes
Hypothesis Testing for Differences Between Means
Hypothesis Testing for Matched Samples
Hypothesis Testing for Differences Between Proportions
Hypothesis Testing for More than Two Means: Analysis of Variance

Required Textbook and Materials

Sullivan, L.M. (2018). Essentials of Biostatistics in Public Health (3 rd ed.). Sudbury, MA: Jones and Bartlett Publishers, Inc. ISBN: 9781284108194

How will the course appear on my transcript?

You may enroll at any time and have up to 9 months to complete this online course. The credits earned will be recorded on your UND transcript based on the date you registered for the course. It will appear on your transcript in the same way as a course taken during a regular semester. There is no indication that the course was taken online or that you completed it at your own pace.

Why Take Online Classes at UND?

Here are a few reasons why you should take an online enroll anytime course at UND:

Great customer service – Our registration team is ready to answer questions quickly so you can focus on your coursework.
Affordable – UND's enroll anytime courses are priced at North Dakota's affordable, in-state tuition rate.
Accredited – UND is accredited by the Higher Learning Commission .
Easily transfer credits – Transferring credits is always at the discretion of the institution to which the credits are being transferred. In general, credits from schools/universities that are regionally accredited by the Higher Learning Commission transfer to other regionally accredited institutions. UND's online courses appear on your UND transcript in the same way as other courses.

Flexible 100% Online Course

You'll take this online course at your own pace. Some students thrive in this environment, while other students may struggle with setting their own deadlines. If you have successfully taken an independent study or correspondence course previously, UND’s enroll anytime courses may be right for you. Still not sure? Take our online quiz to help determine if online enroll anytime courses are right for you.

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Graduate Programs

Biostatistics (online).

The online Master’s in Biostatistics, Health Data Science concentration, provides students with a strong foundation in biostatistical, health data science methods and rigorous training in applied skills to meet the growing demands of this industry and become future leaders in the life science and public health domains.

This program is offered 100% online and designed to serve a broad and diverse audience of learners to meet the pressing need for well-trained biostatistics professionals around the country and the world. The online Sc.M. in Biostatistics can be completed across five consecutive semesters. The structured curriculum will deliver the core of the Biostatistics ScM degree shared by all Biostatistics degrees at Brown and across the country (6 competencies defined by the Council on Education in Public Health [CEPH]).

Additional Resources

All students have access to the computing infrastructure at the Center for Statistical Sciences, a high-end, continuously updated computing environment featuring both Unix and PC/MAC networks, with access to all major software for data analysis and statistical computing.

Students in the online Biostatistics program will have access to the same learning resources as in-person Biostatistics students. This includes Brown University’s extensive collection of library/information resources; the Sheridan Center for Teaching and Learning’s English language support and Writing Center; Student Accessibility Services; and telehealth and tele-mental health services. They will also have access to help desk support for registration, Brown email, Canvas, and all University-supported software.

Application Information

Entering students are expected to meet the necessary admissions requirements, including:

A bachelor’s degree from an accredited institution
At least one semester of calculus (including differentiation & integration)
At least one semester of linear algebra
At least one semester of probability
At least two years of professional work experience
Prior experience in programming languages, such as R or Python is recommended and preferred, but not required. Applicants who lack this experience in their academic or work background are encouraged to complete an introductory programming course prior to matriculation.

Applicants to this School of Public Health program should apply through SLATE . Brown University School of Public Health GRE reporting code: 7765.

If you have any questions regarding the application process for this program, please email [email protected] .

Spring 2025 Application Opens: March 4, 2024 Early Action Deadline: July 15, 2024 Priority Deadline: September 15, 2023 Final Deadline: October 15, 2023 Semester Starts: January 22, 2025

Fall 2025 Application Opens: October 16, 2024 Early Action Deadline: March 15, 2025 Priority Deadline: May 1, 2025 Final Deadline: June 1, 2025 Semester Starts: September 3, 2025

Application Requirements

Gre subject:.

Not required

GRE General:

Toefl/ielts:.

All international applicants whose native language is not English must submit an official Test of English as a Foreign Language (TOEFL) or International English Language Testing System (IELTS) score. Visit https://graduateschool.brown.edu/application-information/international-applicants/language-proficiency-toefl-or-ielts for exceptions. The TOEFL iBT Special Home Edition and the IELTS Indicator exam are accepted. Students from mainland China may submit the TOEFL ITP Plus exam.

Official Transcripts:

Letters of recommendations:.

(3) Required

Personal Statement:

Additional materials:.

Application Fee

Additional Requirements:

INTERNATIONAL APPLICANTS

Language Proficiency (TOEFL or IELTS if applicable)

Transcript Evaluation (if applicable)

RESTRICTIONS AND LICENSING REQUIREMENTS

To comply with OFAC restrictions and licensing requirements, Brown University currently does not offer online programs to individuals physically located in the following areas: Cuba, Iran, North Korea, Syria, and the Crimea, Donetsk and Luhansk regions of Ukraine. Please note that this limitation is location based, as well as applies to all citizens of these countries, regardless of location, who are ordinarily resident in these countries. View more information pertaining to embargoed country regulations for online courses .

Dates/Deadlines

Early admission deadline, application deadline, tuition and funding.

Graduate Tuition & Fees: Please visit the Bursar's Office for up-to-date tuition rates.

Most students are self-supported through a combination of scholarships, loans, outside funding, and/or assistantships. Please visit the master's funding website for more information.

Completion Requirements

Students are required to successfully complete 9 online courses, including a final capstone project. Courses will have both required asynchronous and optional synchronous components to support student learning.

Non-traditional adult learners and working professionals can expect to complete the course sequence in as few as 20 months. Up to one graduate course from another institution can be transferred.

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PG Diploma in Epidemiology and Biostatistics - Course, Admission, Syllabus, Fees, Scope

The PGD in Epidemiology and Biostatistics is a specialist programme that focuses on advanced methods for analysing public health data. The duration is one year which helps to develop students’ epidemiological and statistical analysis skills for informed public health decision-making.

A PGD in epidemiology and biostatistics usually requires a qualifying graduate degree in B.tech / B.Sc. in Microbiology / BSc Life Sciences/ B.Pharm / MBBS / BDS or any other discipline. The curriculum covers topics such as epidemiological methods, statistical methods, disease control, and research methods to prepare students for the role of public health research and analysis.

PGD Epidemiology and Biostatistics Highlights

Pgd epidemiology and biostatistics eligibility criteria, pgd epidemiology and biostatistics admission process, pgd epidemiology and biostatistics skills required, pgd epidemiology and biostatistics subject/syllabus, pgd epidemiology and biostatistics fees structure, pgd epidemiology and biostatistics scope, pgd epidemiology and biostatistics career options, pgd epidemiology and biostatistics benefit of studying, pgd epidemiology and biostatistics expected salary, top pgd epidemiology and biostatistics colleges in india.

The PGD in epidemiology and biostatistics broadens and provides knowledge in health research, medicine, and public health agencies. Graduates can work as epidemiologists, biostatisticians, and research investigators, or contribute to public health policy development and research.

Candidates must meet specific criteria for enrollment in the Postgraduate Diploma in Epidemiology and Biostatistics. The programme welcomes graduates from a variety of disciplines and emphasises a multidisciplinary approach to creating a broad understanding of epidemiological research and biostatistics.

Graduates in science fields such as microbiology , life sciences , botany , and zoology are eligible.

Candidates having qualifications (B.Tech/BE) in Food Science, Food Technology , and Engineering (B.Tech/BE) are encouraged to apply.

Medical professionals including MBBS, BDS, BHMS, BUMS , and BAMS graduates are eligible for admission.

Individuals with B. Pharm degrees are also eligible to apply for the PGD programme.

Students who wish to be from a variety of disciplines beyond those indicated can also participate, demonstrating that the programme is inclusive and open to a variety of subjects.

The process for entry into the Epidemiology and Biostatistics PGD includes planning to ensure the selection of candidates with the qualifications and background necessary to succeed in this specialist programme.

Candidates must submit an online application form providing academic and personal information.

The application is subjected to a comprehensive review based on academic requirements.

Shortlisted candidates will undergo counselling or interview to assess their motivations and suitability for the programme.

Admission is generally merit-based, taking into account academic performance on standardised tests and other evaluation criteria.

Shortlisted candidates will be required to produce the required documents for verification to confirm their eligibility.

Successful applicants will receive formal admissions information, specific programme details, and any other requirements.

A PGD in epidemiology and biological statistics requires specific skills to conduct complex public health research and statistics. Candidates must possess a variety of abilities to succeed in this dynamic field.

Skills Required:

Statistical analysis

Research Methodology

Data Interpretation

Disease modelling

Critical Thinking

Pay attention to details

Scientific Writing

Communication skills

The PGD in Epidemiology and Biostatistics offers an advanced course designed to advance knowledge in epidemiology and statistics. The programme integrates disciplines, providing a strong foundation for professionals in public health research.

The fee for PGD in Biostatistics in Epidemiology at the Center for Health Management Research (CHMR) is Rs 80,000. This cost includes tuition, materials, and any other resources provided by the Institute to ensure a comprehensive learning experience for those wishing to pursue a career in epidemiology and biostatistics.

The Postgraduate Diploma in Epidemiology and Biostatistics has a wide scope. Professionals with advanced statistical and analytical skills find roles in public health organisations, research institutes, pharmaceutical companies, and government health agencies. They contribute significantly to disease surveillance, policy development, and policy analysis, and they play an important role in shaping public health policies.

In addition, graduates can continue their studies or transition into academic and teaching roles, training the next generation of epidemiologists and biostatisticians. This course provides a platform for impactful contributions to health research and public health improvement.

The PGD in epidemiology and biological statistics opens the way for active collaboration between health care and statistical research. Graduates will emerge equipped with the skills necessary for impactful roles in public health research and evaluation.

Epidemiologist : An epidemiologist analyses disease patterns, assesses public health risks, and develops prevention strategies based on rigorous research. This contributes significantly to understanding and controlling disease at the population level.

Biostatistician: The Biostatistician uses statistical methods to analyse health data, assists in the interpretation of research findings, and conducts systematic tests. He/She has to ensure the accuracy and reliability of results in health research.

Public Health Analyst: The Public Health Analyst analyses health data, analyses health systems, and provides data-driven insights to inform public health policies. This contributes to the well-being of communities and improves health systems.

Clinical Research Coordinator : The Clinical Research Coordinator oversees clinical trials, and ensures protocol compliance. He/She facilitates the collection of reliable data to advance medical knowledge and treatment strategies.

Healthcare Analyst: A healthcare analyst interprets complex healthcare data and identifies trends that inform decisions. He/She improves healthcare delivery and improves patient outcomes.

Epidemiology Researcher: An epidemiology researcher conducts in-depth research to investigate the causes and distribution of diseases. He/She advances scientific knowledge to inform public health policies and programmes.

Biostatistics Consultant: A Biostatistics Consultant provides expert advice on statistical methods for health research. He/She assists researchers and organisations in study design and analyses to ensure robust and meaningful results.

Health Policy Analyst: A health policy analyst analyses the impact of health care policies and researches to inform and develop health policies. He/She ensures that policies are consistent with the public health interest.

Infectious Diseases Specialist: An infectious disease specialist diagnoses and manages infectious diseases. He/She provides expertise and support for disease prevention, treatment, and public health programs.

Clinical Trial Coordinator: A Clinical Trials Coordinator oversees and monitors various aspects of the clinical trial, ensuring protocol adherence, patient recruitment, and data collection. He/She facilitates the smooth conduct of clinical research studies.

Top Recruiters:

Apollo Hospitals

Indian Council of Medical Research (ICMR)

National Institute of Epidemiology (NIE)

Fortis Healthcare

Public Health Foundation of India (PHFI)

Max Healthcare

Tata Consultancy Services (TCS)

Earning a PGD in Epidemiology and Biostatistics offers a wide range of benefits, providing individuals with specialised skills essential for an interesting career in health research and research. The programme provides an in-depth understanding of epidemiological techniques and statistical techniques. Graduates develop skills in the design and execution of research studies.

In addition, the PGD provides avenues for professionals to participate in groundbreaking research, participate in impactful public health initiatives, and contribute to health care policy. The demand for experienced epidemiologists and biostatisticians in education and industry underscores the importance of the programme. The potential of its graduates to make significant contributions to the advancement of public health initiatives.

Having completed a Post Graduate Diploma in Epidemiology and Biostatistics, employees can predict lucrative salaries, reflecting the core skills acquired during the programme. Salaries vary based on role and experience in active areas of healthcare research and statistical analysis.

Source: Ambitionbox

The salary figures mentioned anywhere in these articles are just for reference purposes. Please treat them as such. Actual salaries may vary depending on respective candidates, employer, job location and numerous other factors.

Top colleges providing a postgraduate diploma in epidemiology and biostatistics combine theoretical knowledge with practical skills. These colleges prepare aspiring professionals for breakthrough careers in health research.

The Postgraduate Diploma in Epidemiology and Biostatistics is a transformative educational path, arming professionals with advanced skills for a challenging career. With a focus on evidence-based practice, graduates are well-equipped to navigate the complexities of health research and make significant contributions to public health and health policy.

Frequently Asked Question (FAQs)

The PGD in Epidemiology and Biostatistics provides individuals with the unique skills needed for influential roles in health research, providing a unique blend of epidemiology and statistics.

The programme enhances career prospects by providing in-depth knowledge of epidemiological techniques and statistical methods. It enables graduates to make significant contributions to public health research, policy development, and analysis.

Yes, eligibility generally requires a bachelor’s degree in relevant fields of science, health, or related disciplines. Specific eligibility criteria can vary among organisations.

The course covers topics such as epidemiological methods, statistical methods, research methods, and disease surveillance, and provides a broad foundation for public health professionals.

This programme combines practical experience with internships, enabling students to apply theoretical skills in real-world settings. It enhances skills and understanding of epidemiological and statistical practices.

Graduates can work as epidemiologists, biostatisticians, public health researchers, and clinical researchers contributing to a variety of careers in health and research.

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A Naval Architect is a professional who designs, produces and repairs safe and sea-worthy surfaces or underwater structures. A Naval Architect stays involved in creating and designing ships, ferries, submarines and yachts with implementation of various principles such as gravity, ideal hull form, buoyancy and stability.

Orthotist and Prosthetist

Orthotists and Prosthetists are professionals who provide aid to patients with disabilities. They fix them to artificial limbs (prosthetics) and help them to regain stability. There are times when people lose their limbs in an accident. In some other occasions, they are born without a limb or orthopaedic impairment. Orthotists and prosthetists play a crucial role in their lives with fixing them to assistive devices and provide mobility.

Veterinary Doctor

Pathologist.

A career in pathology in India is filled with several responsibilities as it is a medical branch and affects human lives. The demand for pathologists has been increasing over the past few years as people are getting more aware of different diseases. Not only that, but an increase in population and lifestyle changes have also contributed to the increase in a pathologist’s demand. The pathology careers provide an extremely huge number of opportunities and if you want to be a part of the medical field you can consider being a pathologist. If you want to know more about a career in pathology in India then continue reading this article.

Speech Therapist

Gynaecologist.

Gynaecology can be defined as the study of the female body. The job outlook for gynaecology is excellent since there is evergreen demand for one because of their responsibility of dealing with not only women’s health but also fertility and pregnancy issues. Although most women prefer to have a women obstetrician gynaecologist as their doctor, men also explore a career as a gynaecologist and there are ample amounts of male doctors in the field who are gynaecologists and aid women during delivery and childbirth.

An oncologist is a specialised doctor responsible for providing medical care to patients diagnosed with cancer. He or she uses several therapies to control the cancer and its effect on the human body such as chemotherapy, immunotherapy, radiation therapy and biopsy. An oncologist designs a treatment plan based on a pathology report after diagnosing the type of cancer and where it is spreading inside the body.

Audiologist

The audiologist career involves audiology professionals who are responsible to treat hearing loss and proactively preventing the relevant damage. Individuals who opt for a career as an audiologist use various testing strategies with the aim to determine if someone has a normal sensitivity to sounds or not. After the identification of hearing loss, a hearing doctor is required to determine which sections of the hearing are affected, to what extent they are affected, and where the wound causing the hearing loss is found. As soon as the hearing loss is identified, the patients are provided with recommendations for interventions and rehabilitation such as hearing aids, cochlear implants, and appropriate medical referrals. While audiology is a branch of science that studies and researches hearing, balance, and related disorders.

Hospital Administrator

The hospital Administrator is in charge of organising and supervising the daily operations of medical services and facilities. This organising includes managing of organisation’s staff and its members in service, budgets, service reports, departmental reporting and taking reminders of patient care and services.

For an individual who opts for a career as an actor, the primary responsibility is to completely speak to the character he or she is playing and to persuade the crowd that the character is genuine by connecting with them and bringing them into the story. This applies to significant roles and littler parts, as all roles join to make an effective creation. Here in this article, we will discuss how to become an actor in India, actor exams, actor salary in India, and actor jobs.

Individuals who opt for a career as acrobats create and direct original routines for themselves, in addition to developing interpretations of existing routines. The work of circus acrobats can be seen in a variety of performance settings, including circus, reality shows, sports events like the Olympics, movies and commercials. Individuals who opt for a career as acrobats must be prepared to face rejections and intermittent periods of work. The creativity of acrobats may extend to other aspects of the performance. For example, acrobats in the circus may work with gym trainers, celebrities or collaborate with other professionals to enhance such performance elements as costume and or maybe at the teaching end of the career.

Video Game Designer

Career as a video game designer is filled with excitement as well as responsibilities. A video game designer is someone who is involved in the process of creating a game from day one. He or she is responsible for fulfilling duties like designing the character of the game, the several levels involved, plot, art and similar other elements. Individuals who opt for a career as a video game designer may also write the codes for the game using different programming languages.

Depending on the video game designer job description and experience they may also have to lead a team and do the early testing of the game in order to suggest changes and find loopholes.

Radio Jockey

Radio Jockey is an exciting, promising career and a great challenge for music lovers. If you are really interested in a career as radio jockey, then it is very important for an RJ to have an automatic, fun, and friendly personality. If you want to get a job done in this field, a strong command of the language and a good voice are always good things. Apart from this, in order to be a good radio jockey, you will also listen to good radio jockeys so that you can understand their style and later make your own by practicing.

A career as radio jockey has a lot to offer to deserving candidates. If you want to know more about a career as radio jockey, and how to become a radio jockey then continue reading the article.

Choreographer

The word “choreography" actually comes from Greek words that mean “dance writing." Individuals who opt for a career as a choreographer create and direct original dances, in addition to developing interpretations of existing dances. A Choreographer dances and utilises his or her creativity in other aspects of dance performance. For example, he or she may work with the music director to select music or collaborate with other famous choreographers to enhance such performance elements as lighting, costume and set design.

Videographer

Multimedia specialist.

A multimedia specialist is a media professional who creates, audio, videos, graphic image files, computer animations for multimedia applications. He or she is responsible for planning, producing, and maintaining websites and applications.

Social Media Manager

A career as social media manager involves implementing the company’s or brand’s marketing plan across all social media channels. Social media managers help in building or improving a brand’s or a company’s website traffic, build brand awareness, create and implement marketing and brand strategy. Social media managers are key to important social communication as well.

Copy Writer

In a career as a copywriter, one has to consult with the client and understand the brief well. A career as a copywriter has a lot to offer to deserving candidates. Several new mediums of advertising are opening therefore making it a lucrative career choice. Students can pursue various copywriter courses such as Journalism , Advertising , Marketing Management . Here, we have discussed how to become a freelance copywriter, copywriter career path, how to become a copywriter in India, and copywriting career outlook.

Careers in journalism are filled with excitement as well as responsibilities. One cannot afford to miss out on the details. As it is the small details that provide insights into a story. Depending on those insights a journalist goes about writing a news article. A journalism career can be stressful at times but if you are someone who is passionate about it then it is the right choice for you. If you want to know more about the media field and journalist career then continue reading this article.

For publishing books, newspapers, magazines and digital material, editorial and commercial strategies are set by publishers. Individuals in publishing career paths make choices about the markets their businesses will reach and the type of content that their audience will be served. Individuals in book publisher careers collaborate with editorial staff, designers, authors, and freelance contributors who develop and manage the creation of content.

In a career as a vlogger, one generally works for himself or herself. However, once an individual has gained viewership there are several brands and companies that approach them for paid collaboration. It is one of those fields where an individual can earn well while following his or her passion.

Ever since internet costs got reduced the viewership for these types of content has increased on a large scale. Therefore, a career as a vlogger has a lot to offer. If you want to know more about the Vlogger eligibility, roles and responsibilities then continue reading the article.

Individuals in the editor career path is an unsung hero of the news industry who polishes the language of the news stories provided by stringers, reporters, copywriters and content writers and also news agencies. Individuals who opt for a career as an editor make it more persuasive, concise and clear for readers. In this article, we will discuss the details of the editor's career path such as how to become an editor in India, editor salary in India and editor skills and qualities.

Linguistic meaning is related to language or Linguistics which is the study of languages. A career as a linguistic meaning, a profession that is based on the scientific study of language, and it's a very broad field with many specialities. Famous linguists work in academia, researching and teaching different areas of language, such as phonetics (sounds), syntax (word order) and semantics (meaning).

Other researchers focus on specialities like computational linguistics, which seeks to better match human and computer language capacities, or applied linguistics, which is concerned with improving language education. Still, others work as language experts for the government, advertising companies, dictionary publishers and various other private enterprises. Some might work from home as freelance linguists. Philologist, phonologist, and dialectician are some of Linguist synonym. Linguists can study French , German , Italian .

Public Relation Executive

Travel journalist.

The career of a travel journalist is full of passion, excitement and responsibility. Journalism as a career could be challenging at times, but if you're someone who has been genuinely enthusiastic about all this, then it is the best decision for you. Travel journalism jobs are all about insightful, artfully written, informative narratives designed to cover the travel industry. Travel Journalist is someone who explores, gathers and presents information as a news article.

Quality Controller

A quality controller plays a crucial role in an organisation. He or she is responsible for performing quality checks on manufactured products. He or she identifies the defects in a product and rejects the product.

A quality controller records detailed information about products with defects and sends it to the supervisor or plant manager to take necessary actions to improve the production process.

Production Manager

Merchandiser.

A QA Lead is in charge of the QA Team. The role of QA Lead comes with the responsibility of assessing services and products in order to determine that he or she meets the quality standards. He or she develops, implements and manages test plans.

Metallurgical Engineer

A metallurgical engineer is a professional who studies and produces materials that bring power to our world. He or she extracts metals from ores and rocks and transforms them into alloys, high-purity metals and other materials used in developing infrastructure, transportation and healthcare equipment.

Azure Administrator

An Azure Administrator is a professional responsible for implementing, monitoring, and maintaining Azure Solutions. He or she manages cloud infrastructure service instances and various cloud servers as well as sets up public and private cloud systems.

AWS Solution Architect

An AWS Solution Architect is someone who specializes in developing and implementing cloud computing systems. He or she has a good understanding of the various aspects of cloud computing and can confidently deploy and manage their systems. He or she troubleshoots the issues and evaluates the risk from the third party.

Computer Programmer

Careers in computer programming primarily refer to the systematic act of writing code and moreover include wider computer science areas. The word 'programmer' or 'coder' has entered into practice with the growing number of newly self-taught tech enthusiasts. Computer programming careers involve the use of designs created by software developers and engineers and transforming them into commands that can be implemented by computers. These commands result in regular usage of social media sites, word-processing applications and browsers.

ITSM Manager

Information security manager.

Individuals in the information security manager career path involves in overseeing and controlling all aspects of computer security. The IT security manager job description includes planning and carrying out security measures to protect the business data and information from corruption, theft, unauthorised access, and deliberate attack

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Cetifications

We Appeared in

Doctor of Philosophy (PhD) Program

Xinhua Yu, MS, MD, PhD Associate Professor and Graduate Program Coordinator 305 Robison Hall 901.678.3433

Email: [email protected]

The School of Public Health at The University of Memphis offers a PhD degree in Epidemiology and Biostatistics, the highest academic degree for individuals planning to pursue scholarly careers in this discipline. This program is designed for those who intend to teach and conduct original research utilizing rigorous scientific theories and methods, as well as be active in advocating and promoting health policies and intervention programs to improve the general health of societies.

Program Admission

A master’s degree in related health field is required for admission. Applicants must show potential for further study by having maintained a GPA of at least a 3.0 average in their master’s-level coursework. The Graduate Record Examination (GRE) completed within the past five years is required. Competitive scores on the GRE are considered in the admissions decision. Applicants already holding a doctoral degree or its professional equivalent may be exempted from the GRE requirement. Other professional school standardized test scores (MCAT, DAT, or LSAT,) may be substituted for the GRE by applicants who are working toward or who have already earned post-baccalaureate degrees for example, in medicine, dentistry, management, or law.

All applicants who will be attending the University on a visa and who are not native speakers of English must supply a minimum score of 96 (80%) on the computer-based Test of English as a Foreign Language (TOEFL iBT), or an equivalent score on the paper-based test (TOEFL PBT).

Letters of recommendation from three individuals (at least one letter from a former professor or instructor) familiar with the applicant’s academic background or experience in public health related issues, specifying in detail the applicant’s capabilities for graduate study and for future performance as a public health scholar, are required. Applicants must also submit a personal statement of approximately 750 to 1000 words indicating his/her present interests and career goals, including how the PhD in Epidemiology will prepare the candidate to achieve these goals.

All doctoral students are expected to be active in research collaboratively with members of the Division faculty each semester they are enrolled. Students may receive credit for research involvement by enrolling in PUBH 8800 Guided Research in PUBH .

Retention Requirements:

Students must earn a grade of B (3. 0) or higher in all required courses. The PhD program will adhere to Graduate School policy regarding course grades and repetition of courses. All courses applied toward PhD degree program requirements must have the advisor’s written approval.

Residency Requirements:

The last 30 credit hours must be earned at The University of Memphis. Credit will be transferred to apply toward a doctoral program upon approval of the student’s advisory committee in accordance with Graduate School policy.

Comprehensive Examination:

Upon completion of required coursework and prior to enrolling in dissertation hours ( PUBH 9000 ), the student must successfully complete a written and oral comprehensive exam. The exam will assess mastery of areas covered in the student’s program. The content of the examination will consist of core competencies in public health, epidemiology, and biostatistics. Epidemiology and biostatistics faculty will be responsible for organizing and evaluating the comprehensive examination.

Dissertation:

To fulfill the requirements for the PhD in Epidemiology, the student must write and defend a dissertation. The dissertation must adhere to the format outlined by the Graduate School. The dissertation topic will be determined by the student in consultation with the advisor and input from the advisory committee.

Program Curriculum:

The Epi PhD Program is a 54 semester hour degree program. Students are required to fulfill prerequisite courses PUBH 8150 Biostatistical Methods I , and PUBH 8170 Epidemiology in PUBH I , or document their equivalent. Credit hours for these prerequisite courses will not count toward the 54 hours required for graduation.

Epidemiology Research Methods Core: 9 credit hours

PUBH 8141 - Epidemiologic Survey Method Credit Hours: (3)
PUBH 8172 - Epidemiology PUBH II Credit Hours: (3)
PUBH 8174 - Epidemiology PUBH III Credit Hours: (3)

Biostatistics Core: 15 credit hours

PUBH 8152 - Biostatistical Methods II Credit Hours: (3)
PUBH 8309 - Appl Surv Analys in Pub Hlth Credit Hours: (3)
PUBH 8310 - Mixed Model Regression Analys Credit Hours: (3)
PUBH 8311 - Appl Categorical Data Analys ** Credit Hours: (3)
PUBH 8190 - Adv SAS for PUBH Prof 1 Credit Hours: (3)

Doctoral Seminar: 9 credit hours

PUBH 8901 - Doctoral Professional Dev Sem Credit Hours: (3)
PUBH 8192 - Intro to Human Disease for PH Credit Hours: (3)
PUBH 8720 - Grant Writing in HealthScience ** Credit Hours: (3)

Epidemiology Electives: 15 credit hours

Public health electives:.

PUBH 8124 - Environmental Toxicology Credit Hours: (3)
PUBH 8140 - Epidemiology of Chronic Diseases Credit Hours: (3)
PUBH 8442 - Cancer Epidemiology Credit Hours: (3)
PUBH 8443 - Infectious Disease Epidemiolgy Credit Hours: (3)
PUBH 8445 - Genetic Epidemiology Credit Hours: (3)
PUBH 8450 - Randomized Clinical Trials I Credit Hours: 3

Dissertation: 6 credit hours

PUBH 9000 - Dissertation Credit Hours: (1-9)

Epi PhD Program Requirements

Questions about the epi phd program curriculum and degree requirements.

Xinhua Yu, MD, PhD, MS, Associate Professor and Coordinator Epidemiology Doctoral Program 901.678.3433 [email protected]

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COMMENTS

Certificate Course in Biostatistics, Epidemiology & Research
Certificate course in Bio-statistics, Epidemiology and Research Methodology is a 10-week program, which accounts for a total of 6 credit hours. Important Notice to the PhD Scholars registering for the course: In case of any technical issues during registration please contact 0820 29 22072.
Biostatistics Certificate for Public Health Online at Ohio State
Ohio State's online Graduate Certificate in Biostatistics offers a unique blend of epidemiology and biostatistics for public health. You will learn survival analysis techniques; assess and improve study designs commonly used in public health research; and interpret standard regression models using industry-standard statistical software.
Applied Biostatistics Certificate: Methods & Applications
Track 1: Applied Biostatistics: Core Curriculum. Harvard affiliates: $1750.00. Non-Harvard affiliates: $2500.00. Track 2: Applied Biostatistics: Core Curriculum and Advanced Topics. Harvard affiliates: $2800.00. Non-Harvard affiliates: $4000.00. Applied Biostatistics Certificate participants who wish to withdraw from the program for a full ...
Degree and Certificate Programs
Degree and Certificate Programs. Degree and Certificate Programs associated with the Clinical Epidemiology Area of Interest are designed primarily to train clinicians and other health care professionals with the quantitative skills needed for clinical research. This area offers rigorous training in research methodology, whereby students take ...
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This certificate program is intended to provide the concepts, methods and tools needed for the assessment of health situations and trends of population groups. Educational Objectives Upon completion of the core courses in this certificate program, individuals will have gained specialized knowledge and skills on the application of epidemiologic ...
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Specialization - 4 course series. This specialization is intended for public health and healthcare professionals, researchers, data analysts, social workers, and others who need a comprehensive concepts-centric biostatistics primer. Those who complete the specialization will be able to read and respond to the scientific literature, including ...
Epidemiology and Clinical Research Graduate Certificate
What You'll Earn. You'll earn a Stanford Graduate Certificate in Epidemiology and Clinical Research when you successfully earn a grade of B (3.0) or better in each course in the program.. With each successful completion of a course in this program, you'll receive a Stanford University transcript and academic credit, which may be applied to a relevant graduate degree program that accepts ...
Applied Biostatistics and Epidemiology Certificate
This 15-hour certificate program provides applied knowledge and skills in common statistical analyses and epidemiologic methods. These skills are in high demand in research and academia, in clinical and laboratory settings, in health care organizations, in non-profits and NGOs - anywhere data analysis is used to promote population health.
Best Biostatistics Courses & Certificates Online [2024]
There are some great free biostatistics courses available online that offer an invaluable education in the field. For example, the Coursera Clinical Research course teaches course teaches strategies and techniques needed to develop research protocols. Additionally, the Bioinformatics course examines biological data from a computational perspective. If you are interested in more specialized ...
Certificate in Epidemiology and Biostatistics
The Certificate in Epidemiology and Biostatistics offered though Drexel University Online, is an inter-disciplinary program designed for working professionals coming from diverse backgrounds: public health administrators, physicians, nurses, clinical research professionals, health educators, policy experts and more. The program provides ...
Certificate Programs
We offer clinical research and implementation science training opportunities to meet the educational needs of trainees, faculty and staff, from individual courses to full certificate programs. Advanced Training in Clinical Research Preparing students in clinical research methods and the analysis, interpretation and presentation of their data.
Advanced Epidemiology
Apply multivariable regression methods commonly used in biostatistics and epidemiology (e.g., logistic regression, cox proportional hazard models) ... Application of Epi Research Methods II: This course will introduce students to the basic programming skills necessary to adapt R statistical computing system to their needs by presenting material ...
Best Online Biostatistics Courses and Programs
Today, biostatistics is aided by the power of technological and intellectual advances, allowing for more complex analyses. Biostatisticians can fuel advancements in biomedical research, and provide health sciences with better insights through statistical exploration of big data. Biostatistics is closely linked with clinical trials.
Biostatistics
What We Do in the Department of Biostatistics. The Bloomberg School's Department of Biostatistics is the oldest department of its kind in the world and has long been considered one of the best. Our faculty conduct research across the spectrum of statistical science, from foundations of inference to the discovery of new methodologies for health ...
Biostatistics, Epidemiology, and Research Design (BERD)
BERD engages clinical and basic science investigators on: Study design. State-of-the-art secure database design. Modern data analysis planning. Interpretation of findings. Development of new methods, tools, software, and applications. Education and training in research methods. BERD provides the infrastructure for such resources through several ...
Epidemiologic Methods (EPI 203)
Epidemiologic Methods (EPI 203) Research related to health in which individual human beings or groups of human beings are the unit of observation has, over the years, been known and classified by many names. These include patient-oriented, clinical, translational, epidemiologic, comparative effectiveness, behavioral, outcomes, or health ...
Courses in Biostatistics and Statistics
The Graduate Group in Epidemiology and Biostatistics (GGEB) is responsible for developing and administering the PhD degree programs in epidemiology and biostatistics as well as the MS program in biostatistics. The PhD programs train individuals to be rigorous and independent academic investigators, able to develop, apply and extend biostatistical and epidemiological methodology to address ...
Courses in Epidemiology and Biostatistics
EPID 536. Applied Methods in Environmental Epidemiology. 2 hours. Provides students with experience in environmental epidemiology methodology through review of literature; discussion of study design and analysis; and analysis of existing data from the National Health and Nutrition Examination Survey. Course Information: Same as EOHS 536 ...
Advanced Training in Clinical Research Certificate
Applications for the 2024-25 academic year are now open: Apply by May 15, 2024. For questions, please contact [email protected]. The Advanced Training in Clinical Research (ATCR) Certificate is a one-year program intended for individuals who desire rigorous training in the methods and conduct of clinical research.
Certificate In Biostatistics, Epidemiology & Research Methodology
Get Latest information on Certificate In Biostatistics, Epidemiology & Research Methodology Admission,Certificate In Biostatistics, Epidemiology & Research Methodology Duration, Fees, Syllabus, After Certificate In Biostatistics, Epidemiology & Research Methodology Salary in India only on AAJTAKCAMPUS
Summer online short-courses through University of Washington
Statistics for Clinical & Epidemiological Research (SISCER), July 8 - August 2, 2024 ; SISCER offers introductory and advanced short courses in methods for clinical research and epidemiology. Participants will find learning opportunities for clinical trials, causal inference, biomarker research, and analyzing observational data and complex ...
About the Program
* Location may be subject to change at the discretion of The Department of Epidemiology During the program, interns will: Attend Introduction to Epidemiology and Biostatistics courses. Participate in faculty roundtables. Engage in writing-intensive courses. Take part in an R boot camp. Attend ODI workshops. Receive support from alumni mentors.
Admissions
Take relevant coursework in biostatistics, epidemiology, data analysis, and research methods to demonstrate a strong interest and aptitude in the field. Courses in programming languages such as R or Python can also be beneficial for acquiring essential data analysis skills. Research Experience:
Epidemiology & Biostatistics Online Course
About the Epidemiology & Biostatistics Course. This is a three credit hour course designed to provide students with an introduction to various epidemiological and biostatistical methods used in public health practice and research, with an emphasis on application of appropriate methods and interpretation of results.
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The online Master's in Biostatistics, Health Data Science concentration, provides students with a strong foundation in biostatistical, health data science methods and rigorous training in applied skills to meet the growing demands of this industry and become future leaders in the life science and public health domains.
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PG Diploma in Epidemiology and Biostatistics
PGD Epidemiology and Biostatistics Expected Salary. Having completed a Post Graduate Diploma in Epidemiology and Biostatistics, employees can predict lucrative salaries, reflecting the core skills acquired during the programme. Salaries vary based on role and experience in active areas of healthcare research and statistical analysis.
Program: Epidemiology and Biostatistics, (PhD)
The Epi PhD Program is a 54 semester hour degree program. Students are required to fulfill prerequisite courses PUBH 8150 Biostatistical Methods I , and PUBH 8170 Epidemiology in PUBH I , or document their equivalent. Credit hours for these prerequisite courses will not count toward the 54 hours required for graduation.
April 2024: Melissa Jay Smith, PhD
The field of environmental epidemiology has placed an increasing emphasis on understanding the effects of mixtures of metals, chemicals and pollutants rather than a single exposure in isolation. A method that has gained significant traction to model mixture effects is called Bayesian Kernel Machine Regression (BKMR), but it makes several ...