Explore Courses at UMass Amherst
How to Use this Page
Use this page to search for courses by selecting the available filters. By clicking on the link under “Course / Catalog #,” you can view available sections of a course.
The first filter is the keyword search edit, it will search for whatever you enter in the number, name, and description of a course. The remaining filters allow you to pick multiple values from a group of checkboxes. Because some filters have hundreds of options we have provided an edit in each filter which will hide all checkboxes that do not match the entered text. For example if you navigate to the Terms H3, enter 'fall' in the terms edit and then navigate to the checkboxes you will find that only the fall terms are available as options. After the available options each filter includes a summary of which checkboxes have been selected. You can use this to quickly uncheck a checkbox whether it matches your search term or not.
Use the “Advanced Filters” to view more options for filtering your course search.
The advanced filters are hidden by default, to reveal them navigate to the Advanced filters H3 and then press the advanced filters button.
Once you are satisfied with your filters navigate to the Form Controls H2 and press the 'apply filters' button to search. If you would like to start a new search there is also a 'clear filters' link that will reset all filters to blank.
Search results load into the search results table. This is the only table on the page. Each row in the table starts with a Class # link which will take you to a page with additional information about the course, including available class sections. These are the only links in the table.
The table loads 10 results at a time. You can navigate to the first page, the previous page, the next page, or the last page using the buttons in the Pagination H2. If you would like to skip to a specific page use the go to page combobox in the pagination H2 and then press the 'Go' button that follows.
Filters
Skip filters| Course / Catalog # | Name | Term | Description |
|---|---|---|---|
| BIOSTATS 530 | Introduction to Statistical Computing in Data Science using R | Fall 2025 | R has emerged as a preferred programming language in data science. This course covers an introduction to topics in R programming to develop powerful, robust, and reusable data science tools. Main topics include importing of data, data wrangling, visualization, and reporting. |
| BIOSTATS 531 | Intermediate Statistical Computing in Data Science using R | Fall 2025 | R has emerged as a preferred programming language in data science. This course covers intermediate topics in R programming to develop powerful, robust, and reusable data science tools. Main topics include programming, iteration, modeling, and building wen-based tools to deliver your data products using R Shiny. |
| BIOSTATS 540 | Intro Biostatistics | Fall 2025 | Principles of statistics applied to analysis of biological and health data, evaluation of public health and clinical programs. |
| BIOSTATS 601 | Probability and Statistical Inference for Health Data Science | Fall 2025 | The goal of this course is to introduce fundamentals of probability theory, statistical inference tools and their application to biostatistics and health data science. The course is intended for first-year graduate students in Biostatistics MS program and students who are interested in learning probability and statistical inference. The topics in this course include basic concepts of probability, random variables, important probability distributions (e.g., normal, exponential, binomial and Poisson), marginal distribution, conditional distribution, joint distribution, expectation and variance, conditional expectation, law of large numbers, central limit theorem, sampling distributions, point estimation, maximum likelihood estimation, method of moments and estimating equations, interval estimation, hypothesis testing. Examples from biomedical applications will be used whenever possible. Simple simulations with R software will be used to illustrate some concepts in probability and statistical inference. |
| BIOSTATS 632 | Advanced Statistical Computing in Health Data Science Using R | Fall 2025 | R has emerged as a preferred programming language in data science. This course covers advanced topics in R programming to develop powerful, robust, and reusable data science tools. By the end of this course, students should be able to use git and GitHub for version control and collaboration, organize statistical programming and data analysis projects into R packages, and make code robust with informative error messages and unit testing. |
| BIOSTATS 640 | Intermediate Biostatistics | Spring 2026 | Principles of statistics applied to analysis of biological and health data. Continuation of BIOSTATS 540 including analysis of variance, regression, nonparametric statistics, sampling, and categorical data analysis. |
| BIOSTATS 680 | Topics in Biostatistics and Data Science in Public Health | Spring 2026 | The course introduces advanced central topics in biostatistics and health data science including survival analysis, design and analysis of clinical trials, models for correlated data, bayesian modeling, and causal inference. The course motivates statistical reasoning and methods through substantive research questions and features of data typically available in public health and biomedical research. Students will obtain hands-on experience in applying selected methods on real data using the statistical programming language R. |
| BIOSTATS 690E | Biostatistics in Action (Culminating Experience Course) | Spring 2026 | A discovery based capstone project provides an essential culminating learning experience in the UMASS Amherst M.S program in biostatistics. This course will guide students as they carry out an individual project in 7 steps: (1) formulate a research question, (2) conduct literature review, (3) select relevant data sources, (4) choose appropriate statistical/machine learning methodology, (5) create and implement their analytical plan including data ingestion, transformation, modeling, and interpretation, (6) write a research paper, and (7) effectively communicate their project and results. Leveraging real world data, industry standard languages and tools, and pertinent applications, this course prepares students with the skills necessary to pursue careers as Healthcare Analysts and Data Scientists across sectors including health insurance, hospitals and clinic systems, health and medical device technology, county and state departments of health, and pharmaceutical and life science companies. |
| BIOSTATS 696 | Independent Study | Fall 2025 | |
| BIOSTATS 696 | Independent Study | Spring 2026 |