MS in Biostatistics and Data Science

Course Director: Xi Kathy Zhou, PhD
4 credits

This course introduces the fundamentals of biostatistics with a primary emphasis on the understanding of statistical concepts behind data analytic principles through applications in biomedical studies. This course will enhance students’ proficiency in using R, a freely available software, to explore, visualize, and perform statistical analysis with data. Topics covered include: exploratory data analysis; basic concepts of statistics; construction of hypothesis tests and confidence intervals; performance of statistical comparisons; simple modeling; and determination of power and sample size.

Course Director: Mila Sun, PhD, MS
3 credits

This is a practical introductory course in R programming and unsupervised learning. Students will learn to create clear and effective visualizations, manipulate and summarize complex datasets, and develop interactive tools for data exploration. This course covers foundational topics in unsupervised learning, including dimension reduction, clustering methods, and matrix factorization with illustrative applications. The primary goal of this course is to equip students with the skills and conceptual understanding needed to explore data structure, identify patterns, and critically apply unsupervised learning methods in practice.

Course Director: Faculty
2 credits

This is the culminating capstone course of all masters-level graduate education programs. It has two aims: (1) helping students to discover and develop new and effective ways of managing and working together with all the stakeholders within the healthcare field and (2) helping accelerate a student's development of 12 the context awareness, integrative management, and industry skills that are needed to lead in a rapidly changing healthcare sector. This capstone course puts students in a new organization, one they don’t already know well, and gives them the chance to practice hitting the ground running. This culminating course provides a deeper preparation for the next stages of a student's career. The capstone project will last the entire year: the first term involves matching students with the right project, the second term has students working with their client, and the third term consists of a detailed report and final presentation in front of the client as well as faculty and fellow classmates.

Course Director: Faculty
3 credits

This one-semester course provides an introduction to statistical computing and modern data analysis using R and Python. Students will learn the complete data analysis workflow, including data import, cleaning, visualization, exploratory data analysis, statistical inference, and model building.

The first part of the course introduces fundamental statistical concepts, including probability, statistical distributions, estimation, confidence intervals, hypothesis testing, and methods for comparing categorical and continuous data, such as chi-square tests, t-tests, analysis of variance (ANOVA), and nonparametric methods. These methods are implemented through hands-on programming exercises in both R and Python.

The second part focuses on statistical modeling and computing, covering simple and multiple linear regression, model diagnostics, variable selection, prediction, and model interpretation. Throughout the course, students will develop practical programming skills for data analysis, visualization, reproducible research, and effective communication of statistical results.

Course Director: Wodan Ling, PhD, MA
3 credits

Must receive the instructor’s approval and/or pass a screening test.

This course provides an introduction to the fundamentals of Python programming with an emphasis on core techniques and tools used in AI and data science. Students will learn essential programming concepts, including data structures, control flow, functions, and classes. The course will cover data processing using libraries such as NumPy and Pandas, data visualization using Matplotlib and Seaborn, and basic statistical analysis with SciPy. Foundational AI-related methods such as the Monte Carlo method (including random number generation, simulation, and numerical integration) and numerical optimization will be introduced, with applications to real-world problems in AI and data science.

Course Director: Shoshana Rosenberg, ScD, MPH
3 credits

Students have the option of taking PHSC 9001 in Fall Term 1 OR Fall Term 2. Those who want to take PHSC 9002 in Spring must take PHSC 9001 in Fall Term I.

The goal of this course is to provide students with a foundation of epidemiologic methods. This course will introduce students to key epidemiologic concepts including measures of disease frequency, study designs, bias, and causal inference. Students will also learn how to critically evaluate epidemiological research papers.

Course Director: Yushu Shi, PhD
3 credits

This course aims to introduce some common statistical methods and computational tools for predictive modeling, specifically regression analysis. Topics covered in this course include:

• Multivariable linear regression, variable selection, and model diagnosis
• Linear regression with variable transformation
• Generalized linear models, including Logistic regression model, Poisson regression model, variations of the Poisson model for zero-inflated data, multinomial Logistic regression model, and relevant model diagnoses
• Survival analysis: censoring mechanism, log-rank test, Kaplan-Meier curve, parametric survival models, including Cox model (and Fine and Gray model for competing risks data.)

Materials in parentheses are subject to students’ background, performance of past exams, and class progress.

Course Director: Samprit Banerjee, PhD, MStat
3 credits

In the last decade, biomedical and health sciences have seen an explosion of “Big Data” problems. Such problems are commonly associated with general business analytics and marketing. Many statistical and machine learning methods are required to solve such problems. This course is going to provide the basic know-how to tackle such problems and is going to teach what is statistical learning, how to assess model accuracy, supervised and unsupervised classification techniques, tree-based methods, random forests, regularized regression techniques, resampling methods, and support vector machines. The aim of this course is to enable students to identify an appropriate statistical learning algorithm for a real-world application and be able to apply the algorithm to the data using R while being cognizant of the advantages and disadvantages of the chosen algorithm.

Course Director: Faculty
3 credits

This is the culminating capstone course of all masters-level graduate education programs. It has two aims: (1) helping students to discover and develop new and effective ways of managing and working together with all the stakeholders within the healthcare field and (2) helping accelerate a student's development of the context awareness, integrative management, and industry skills that are needed to lead in a rapidly changing healthcare sector. This capstone course puts students in a new organization, one they don’t already know well, and gives them the chance to practice hitting the ground running. This culminating course provides a deeper preparation for the next stages of a student's career. The capstone project will last the entire year: the first term involves matching students with the right project, the second term has students working with their client, and the third term consists of a detailed report and final presentation.

Course Director: Faculty
3 credits

Course Director: Samprit Banerjee, PhD, MStat 
3 credits

There has been an explosion of big data in medicine and healthcare. There are four main sources of such big data – 1) administrative databases in healthcare such as electronic health records and health insurance claims, 2) biomedical imaging (e.g. MRI, CT-Scan, X-ray etc.) 3) sensors in smartphones, wearable and implantable devices and 4) genetics and genomics. It is difficult to navigate and critically assess the statistical methods and analytic tools that are needed to conduct analytics and research with such big biomedical data. This course will introduce the four above-mentioned important sources of big data in medical studies, discuss the nuances and intricacies of how such data are generated and introduce tools to navigate such databases visualize and describe them.

Course Director: Kevin Kensler, ScD
3 credits

Prerequisite: Epidemiology I (PHSC 9001)

The goal of the course is to provide students with more advanced epidemiologic methods and statistical analyses appropriate for specific study designs. This course will expand students’ knowledge of epidemiologic concepts related to the design, conduct and interpretation of epidemiologic studies.

Course Director: Faculty
3 credits

This is the culminating capstone course of all masters-level graduate education programs. It has two aims: (1) helping students to discover and develop new and effective ways of managing and working together with all the stakeholders within the healthcare field and (2) helping accelerate a student's development of the context awareness, integrative management, and industry skills that are needed to lead in a rapidly changing healthcare sector. This capstone course puts students in a new organization, one they don’t already know well, and gives them the chance to practice hitting the ground running. This culminating course provides a deeper preparation for the next stages of a student's career. The capstone project will last the entire year: the first term involves matching students with the right project, the second term has students working with their client, and the third term consists of a detailed report and final presentation in front of the client as well as faculty and fellow classmates.

1-3 credits

Course Director: Linda Gerber, PhD
3 credits

The course will deliver an advanced topic in data analysis that is extremely important for an independent biostatistician: Hierarchical (Mixed Effect) Models with Applications in Longitudinal Data Analysis. This course will give students necessary tools to analyze clustered data, data with hierarchical structure, previously analyzed data from multiple sources (meta-analysis), repeated measure and longitudinal data. The course will cover how to model and analyze such data, and how to remedy missing values in such data. The course will also incorporate instruction in the statistical software R. Every statistical technique will be subsequently followed by instruction and demonstration in R, analyzing real data.

Course Director: Shoshana Rosenberg, ScD, MPH
3 credits

Students have the option of taking PHSC 9001 in Fall Term 1 OR Fall Term 2. Those who want to take PHSC 9002 in Spring must take PHSC 9001 in Fall Term I.

The goal of this course is to provide students with a foundation of epidemiologic methods. This course will introduce students to key epidemiologic concepts including measures of disease frequency, study designs, bias, and causal inference. Students will also learn how to critically evaluate epidemiological research papers.

Course Director: Wodan Ling, PhD, MA
3 credits

Available to those who have not taken HBDS 5022 before, and receive the instructor’s approval and/or pass a screening test.

This course provides an introduction to the fundamentals of Python programming with an emphasis on core techniques and tools used in AI and data science. Students will learn essential programming concepts, including data structures, control flow, functions, and classes. The course will cover data processing using libraries such as NumPy and Pandas, data visualization using Matplotlib and Seaborn, and basic statistical analysis with SciPy. Foundational AI-related methods such as the Monte Carlo method (including random number generation, simulation, and numerical integration) and numerical optimization will be introduced, with applications to real-world problems in AI and data science.

Course Director: Himel Mallick, PhD
3 credits

The goal of this course is to introduce a core set of modern statistical and AI-based concepts and techniques to students, and to demonstrate how to use them to answer complex research questions in healthcare. Students will acquire knowledge of causal inference methods that integrate statistical modeling with machine learning and artificial intelligence, including potential outcomes, counterfactuals, directed acyclic graphs, non-parametric structural equation models, inverse probability weighting, g-computation, causal mediation analysis, causal multimodal AI, and precision medicine.

Course Director: Arindam RoyChoudhury, PhD
3 credits

Pharmaceutical studies use many statistical methods that are not routinely taught as part of conventional biostatistics courses. In this course, the students will learn the statistical methods specifically used in pharmaceutical studies. The course is divided into three modules. (1) “Statistical Aspects of Phase I Clinical Trial” will include 3+3 Design, accelerated titration; up and down designs; continual reassessment method (CRM), Modified CRM, TITE CRM, Bayesian Logistic Regression Model (BLRM), escalation with overdose control (EWOC), toxicity probability interval (TPI) and modified TPI (mTPI). (2) “Statistical Aspects of Phase II Clinical Trial” will include design and analyses for One stage and Simon’s Two Stage Designs, Multi-arm Phase II design. (3) “Statistical Aspects of Phase III Clinical Trial” will include randomization, design and analysis for parallel, crossover, factorial, seamless Phase II/III, Adaptive and SMART designs.

1-3 credits