Instructor(s): Karla Lindquist

This is an introduction to the opportunities and challenges of using large datasets for biomedical research. Topics to be covered include: What makes big data different? What big data can and cannot do. Phases of data science: getting data, merging and cleaning data, storing and accessing data, visualizing or telling stories with data, drawing conclusions from data. Introduction to supervised and unsupervised machine learning including detailed discussion of algorithms and model fitting.

Instructor(s): Stathis Gennatas

Vast amounts of health-related data are being generated daily and at an increasing rate. Our ability to extract insights and make the most of these resources depends on the effective and efficient use of computational tools to preprocess, visualize, and analyze different types of data. This course is an introductory programming course which aims to provide hands-on experience in the R language and enable further work in biostatistics, epidemiology, and machine learning/health data science.

Instructor(s): Stathis Gennatas, John Kornak

R programming course to enable work in any field including biostatistics, epidemiology, data science/machine learning. This course builds on students prerequisite core R language knowledge to cover skills in advanced data transformations, visualization, working with big (in-memory) data, report-writing, and core statistic testing.

Instructor(s): Adam Olshen

This is a course that covers machine learning methods as they apply to areas of biomedical research and will teach how to implement the methods in R. Topics to be covered include: What is Machine learning? Prediction techniques (including classification) and methods for assessing them, Cross-validation, penalized regression methods such as lasso, boosting, bagging and ensemble methods, pattern recognition, deep learning, and data reduction methods, and machine learning meta packages in R.

Instructor(s): John Kornak

This course provides an introduction to essential tools and skills for data science, focusing on Python programming and industry-relevant tools. Students will learn command line basics, version control with Git, documentation with Markdown, remote execution, and high-performance computing (HPC). Integrated throughout the course, the Python component covers syntax, flow control, data management, visualization, libraries for data science, and algorithms and data structures common in interviews.

Instructor(s): John Kornak

This seminar series covers topics in data science algorithms, ethics, biases, and applications. Students will be exposed to current topics on Data Science and Machine Learning/Biostatistics and Health Data applications, discuss issues in data science, present their work, and learn how to critically evaluate research literature. External speakers will be invited to give presentations on potential careers in health data science across the biotech industry, government and academia.

Instructor(s): John Kornak

This course covers advanced topics of data science methods, ethics and biases. The focus in this second year of the seminar program will be on students presenting their research work progress from their Capstone projects. Additionally, students will also learn how to critically evaluate research literature.

Instructor(s): John Kornak

Capstone project requirement for students in the Masters in Health Data Science program. Students will write a first author paper researching a problem in health data science and analyzing data using appropriate data science methodology; present their work at a scientific conference; generate a portfolio of code, analyses and data products; and write a detailed report on the background methodology and technical issues that were considered as well as implemented for the submitted publication.

Instructor(s): John Kornak

Survey of Data Science methods in Python, starting with common data science tools and processes and spending one week per topics learning to build common ML/AI solutions.

Instructor(s): Jean Feng

This course teaches the mathematical foundations of machine learning (ML). Each week, the course surveys a different algorithm to examine its underlying machinery, covering topics such as linear algebra, calculus, and optimization. ML algorithms range from linear models to gradient boosting and deep learning. The course also discusses newer concepts such as model fairness and ML for causal inference. Upon course completion, students should be able to learn new ML algorithms independently.

Instructor(s): John Kornak

This course provides an introduction to Bayesian statistics, Markov Chain Monte Carlo (MCMC) sampling, and Gaussian Processes. The first two units cover the fundamentals of Bayesian methods and MCMC, and the final optional unit explores Gaussian processes. Students will gain practical skills in applying these techniques to real-world problems using R, STAN, and JAGS.

Instructor(s): Staff

Masters in Health Data Science students are expected to act as an educational apprentice (EA). This experience involves leading a weekly small-group discussion section of 10-15 students, holding office hours and grading homework assignments and projects. This requirement will provide students with valuable teaching experience without having a significant time impact on their Capstone project work. In all cases, students will have taken the course they are asked to EA during their first year.