Application Requirements:
- An undergraduate degree in mathematical sciences or allied fields (statistics, economics, etc.) or computer science, with a strong interest in health science,
- practical knowledge of computer scripting and programming, as well as experience with a statistical computing package such as R or Python
- calculus through multivariable integration,
- one semester of linear algebra or matrix methods, and
- excellent written and spoken English.
Additional research or work experience would be considered beneficial, but not required. Evidence that these requirements have been fulfilled should form part of the application. Online courses that offer certificates upon successful completion can fulfill the above requirements.
Course Requirements:
Core Curriculum
A total of 60 credits of coursework are required for the SM in Health Data Science, with a minimum of 55 ordinal credits. This includes a 20 credit ordinally graded core curriculum consisting of:
BST 222 Basics of Statistical Inference (5 credits)
BST 260 Introduction to Data Science (5 credits)
BST 261 Data Science II (2.5 credits)
BST 262 Computing for Big Data (2.5 credits)
BST 263 Applied Machine Learning (5 credits)
Epidemiology Requirement
The Harvard T.H. Chan School of Public Health requires that Master’s students successfully pass one epidemiology course. The program requires that EPI 201 Introduction to Epidemiology: Methods I (2.5 credits) be taken to satisfy this requirement.
Computing Requirement
The program is designed to produce strong programmers. Students will also be required to take an additional 5 credits of coursework in computer science, choosing from the following:
BST 234.1 Introduction to Data Structures and Algorithms (Part 1) (2.5 credits)
BST 234.2 Introduction to Data Structures and Algorithms (Part 2) (2.5 credits)
BST 281 Genomic Data Manipulation (5 credits)
BMI 713 Computational Statistics for Biomedical Science (5 credits)
CS 105 Privacy and Technology (5 credits)
CS 164 Software Engineering Computer Science (5 credits)
CS 165 Data Systems (5 credits)
CS 171 Visualization (5 credits)
CS 187 Computational Linguistics (5 credits)
STAT 171 Introduction to Stochastic Processes (5 credits)
Project-Based Research Course
The program will provide a culminating research experience that tests all competencies through a hands-on semester-long project-based research course (7.5 credits). This course will allow students to immerse themselves in multiple health data science projects in public health and biomedical science.
HDS 325 Health Data Science Practice (7.5 credits)
Elective Courses
Twenty-five additional credits must be taken. Courses that would satisfy these requirements may come from the following list of elective courses. These are in addition to the computer science courses listed under the computing requirement, which could also be counted as electives once the 5 credit requirement has been met.
BST 210 Applied Regression Analysis (5 credits)
BST 223 Applied Survival Analysis (5 credits)
BST 226 Applied Longitudinal Analysis (5 credits)
BST 228 Applied Bayesian Analysis (5 credits)
BST 267 Introduction to Social and Biological Networks (2.5 credits)
BST 270 Reproducible Data Science (2.5 credits)
BST 282 Introduction to Computational Biology and Bioinformatics (5 credits)
BST 283 Cancer Genome Analysis (5 credits)
EPI 202 Elements of Epidemiologic Research: Methods 2 (2.5 credits)
EPI 203 Study Design in Epidemiologic Research (2.5 credits)
EPI 204 Analysis of Case-Control and Cohort Studies (2.5 credits)
EPI 271 Propensity Score Analysis (1.25 credits)
EPI 288 Data Mining and Prediction (2.5 credits)
ID 271 Advanced Regression for Environmental Epidemiology (2.5 credits)
BMI 701 Introduction to Biomedical Informatics (5 credits)
BMI 702 Foundation of Biomedical Informatics II (2.5 credits)
BMI 703 Precision Medicine I: Genomic Medicine (2.5 credits)
BMI 705 Precision Medicine II: Integrating Clinical and Genomic Data (2.5 credits)
BMI 706 Data Visualization for Biomedical Applications (2.5 credits)
CI 722.0 Clinical Data Science: Comparative Effectiveness Research I (2.5 credits)
ME 530M.1 Clinical Informatics (5 credits)
Sample Programs
Students enrolled in the Health Data Science Masters program have the flexibility to choose elective courses more suitable for their desired career path. Listed below are sample programs with different elective course concentrations: biostatistics, computer science, and bioinformatics/biomedical. Note that these are samples and courses are subject to availability.
Biostatistics Track
First Semester (Fall)
BST 222 (Core)
BST 260 (Core)
BST 262 (Core)
EPI 201 (Core)
BST 210 (Elective)
Second Semester (Spring)
BST 261 (Core)
BST 263 (Core)
BST 223 (Elective)
BST 226 (Elective)
BST 214 (Elective)
Third Semester (Fall)
HDS 325 (Capstone Course)
CS 171 (Elective)
CS 105 (Elective)
BST 228 (Elective)
Computer Science Track
First Semester (Fall)
BST 222 (Core)
BST 260 (Core)
BST 262 (Core)
EPI 201 (Core)
BST 267 (Elective)
BST 270 (Elective)
Second Semester (Spring)
BST 261 (Core)
BST 263 (Core)
BST 234 (Computer Science Requirement)
CS 161 (Elective)
EPI 288 (Elective)
Third Semester (Fall)
HDS 325 (Capstone Course)
CS 171 (Elective)
CS 164 (Elective)
BMI 713 (Elective)
Bioinformatics/Biomedical Track
First Semester (Fall)
BST 222 (Core)
BST 260 (Core)
BST 262 (Core)
EPI 201 (Core)
EPI 202 (Elective)
Second Semester (Spring)
BST 261 (Core)
BST 263 (Core)
BST 281 (Computer Science Requirement)
BST 282 (Elective)
EPI 203 (Elective)
EPI 204 (Elective)
Third Semester (Fall)
HDS 325 (Capstone Course)
BMI 701 (Elective)
BMI 703 (Elective)
BMI 705 (Elective)
ME 530M.1 (Elective)
