MS in Biostatistics

Biostatistics is one of the primary skills needed for the development and practice of public health – the methods of this discipline are appropriate for quantifying the possible effects of risk factors and health interventions on individuals and groups. The MS with a concentration in Biostatistics is a two-year program that provides flexible training in areas including clinical trials, epidemiologic methodology, implementation science, statistical genetics, and cutting-edge developments in artificial intelligence and machine learning methods for health sciences.

In contrast to the more general MPH degree, the MS degree emphasizes the mastery of biostatistical skills from the beginning of the plan of study. While graduates of this program may apply to the PhD degree program, the MS degree is itself quite marketable as a terminal degree.

Please note that you may apply to both the MPH and the MS program simultaneously, but they do require separate applications.

The length of study for the MS in Biostatistics is two years. Full-time or part-time enrollment is permissible.

This program requires General GRE test scores. Please send them to institution code 3987 (there is no department code).

Students with strong quantitative skills from all undergraduate majors are welcome to apply. Students should have, at a minimum, previous college-level coursework in multivariable calculus (a.k.a., calculus 3) and linear algebra. Students should also have knowledge of statistics at an introductory level and experience with a relevant programming language (e.g., SAS, R, Python).

To obtain the MS degree, students must complete 16 course credits. Students will take these courses in a scaffolded sequence, beginning with core courses, progressing through a curated list of intermediate courses and advanced courses, culminating in the integrative capstone experience.

In addition, in consultation with their academic advisor, students can take two free graduate-level electives in any department at the University. Students also have two additional non-credit requirements.

• Core Required Courses Year 1: 6 credits
• Intermediate Courses Year 1 or Year 2: 4 credits
• Advanced Courses Year 2: 1 credit (with thesis); 2 credits (if not completing thesis)
• Integrative Learning/Capstone Experience Year 2: 3 credits (with thesis); 2 credits (if not completing thesis)
• Free Graduate-Level Electives Year 1 or Year 2: 2 credits
• Summer Internship in Biostatistics Research: 0 credits
• Leadership Course: 0 credits

The master’s thesis is not required of MS students. If undertaken, the student identifies a YSPH Biostatistics faculty member to serve as faculty adviser and works with them to design the project, conduct appropriate research, and write the thesis.

The thesis is a two-credit course (BIS 649 and BIS 650). It does not replace the first semester of the capstone course, BIS 678 (Statistical Practice I). Students completing a thesis are not required to take BIS 681 (Statistical Practice II).

Full-time students must carry a minimum of 4 course units each semester. Course schedules with more than 5 courses for credit will not be approved. If students have fewer than 4 required courses to take in their last term, it is acceptable to register for just the courses needed to fulfill the degree requirements. Course substitutions must be approved by the student advisor and the DGS.

MS BIS Core Required Courses (6 credits)

These courses should be taken in Year 1:

• BIS 623a Advanced Regression Models
• BIS 527a: Introduction to Health Data Science
• PUBH 508a: Foundations of Epidemiology for Public Health
• S&DS 5410a: Probability [OR S&DS 5510b: Stochastic Processes OR S&DS 6000a: Advanced Probability]
• S&DS 5420b: Theory of Statistics [OR S&DS 610a: Statistical Inference]
• PUBH 608: Foundations in Public Health
• PUBH 550: Professional Leadership for Public Health - 0 units
• PUBH 551: Professional Leadership for Public Health Careers - 0 units

MS Biostatistics: Intermediate Elective Courses (4 credits)

At least three electives must be from this list in Year 1 or 2:

• BIS 540b: Fundamentals of Clinical Trials
• BIS 567a: Bayesian Statistics
• BIS 628b: Longitudinal and Multilevel Data Analysis
• BIS 630b: Applied Survival Analysis
• BIS 645: Statistical Methods of Human Genetics
• BIS 635b: Deep Learning in Biomedical Data Science
• BIS 565b: The Role of Ethics and Equity in Data Science and AI
• BIS 550b: Topics in Biomedical Informatics and Data Science
• BIS 560a: Introduction to Health Informatics
• BIS 634a: Computational Methods in Informatics
• BIS 568b: Applied Artificial Intelligence in Healthcare
• BIS 646b: Nonparametric Statistical Methods and Applications
• BIS 679a: Advanced Statistical Programming in SAS and R

OR... this list in Year 2:

• BIS 537a: Statistical Methods for Causal Inference
• BIS 555b: Machine Learning with Biomedical Data
• BIS 638a: Clinical Database Management and Systems Ontologies
• BIS 640b: User-Centered Design of Digital Health Tools
• BIS 692b: Statistical Methods in Computational Biology

The remaining elective can be from the above lists or from relevant electives offered by other departments:

These electives may be taken in Year 1 or 2:

• EMD 538a: Quantitative Methods for Infectious Disease Epidemiology
• EMD 553b: Transmission Dynamic Models for Understanding Infectious Diseases
• CDE 516b: Principles of Epidemiology II
• S&DS 5790a: Spatial Statistics
• CPSC 5370a: Database Systems
• EHS 568a: Introduction to GIS for Public Health
• MGT 611a: Policy Modeling
• CB&B 5800: Bioinformatics Algorithms in Genomics

These electives should be taken in Year 2:

• CPSC 5770b: Large Language Models: From Foundations to Modern Practice
• CB&B 5740b: Biomedical Natural Language Processing: Methods and Application
• CB&B 5620b: Modeling Biological Systems II
• S&DS 6320b: Advanced Optimization Techniques
• S&DS 6110b: Selected Topics in Decision Theory
• S&DS 6640b: Information Theory
• S&DS 5517b: Applied Machine Learning and Causal Inference
• HSCI 9200a: Pragmatic Trial Design and Implementation
• EHS 569b: Advanced GIS Workshop

Advanced Elective Courses (1 credit with thesis*; 2 credits without thesis)

These electives should be taken in Year 2:

At least one must be from this list (if not completing a thesis):

• BIS 629a: Advanced Methods in Implementation and Prevention Science
• BIS 631b: Advanced Topics in Causal Inference
• BIS 643b: Theory of Survival Analysis
• BIS 691b: Generalized Linear Models

A second elective can be taken from this or the above list (if not completing a thesis):

• S&DS 6000a: Advanced Probability
• CB&B 7520b: Biomedical Data Science: Mining and Modeling
• CPSC 6440a: Geometric Topological Methods in Machine Learning
• CB&B 5760b: Foundations of Real-World Data Science: Electronic Health Records
• GENE 7600: Genomic Methods for Genetic Analysis (instructor permission)

*Students completing a thesis may choose one course from either advanced elective course list.

Required Integrative Learning/Capstone Experience (3 credits with thesis; 2 credits without thesis)

• BIS 678a: Statistical Practice I

And either:

• BIS 681b: Statistical Practice II OR BIS 649a and BIS 650b: Thesis (2 credits)

Mathematical Statistics: Derive and apply the fundamentals of mathematical statistics.

Study Design: Design studies for health science research that are feasible, responsible, valid, and statistically

Analysis: Select from a variety of analytical tools to analyze data and make relevant interpretations, inferences, and prediction.

Programming: Use computational thinking, computing tools, and programming languages to efficiently gather, store, transform, analyze, and visualize data, integrating AI as appropriate.

Communication: Demonstrate comprehension and communication skills to effectively interpret and disseminate results to biostatistician and non-statistician audiences.