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Master of Science – Health Data Science

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Summary

The Master of Science degree in Health Data Science provides students with the rigorous quantitative training and essential computing skills needed to manage and analyze health science data to address today’s most important questions in public health, medicine, and basic biology

Visit the Department of Biostatistics

About

The SM-HDS program is designed to give participants the knowledge and targeted skills required to work in health data science.

As a student, you will:

receive training in quantitative methods, including applied regression, statistical inference, statistical computing, machine learning, statistical consultation and collaboration, and epidemiology;
learn to manage and analyze large-scale health data to reveal patterns, trends, and associations, and respond to the biggest threats to public health;
join a community of leading scientists and educators from around the world dedicated to improving the health of people everywhere through advanced quantitative research;
receive a well-rounded curriculum that will enable you to launch your career in health-related data science or pursue further doctoral studies in biostatistics or other quantitative or computational sciences with an emphasis in data science.

On Campus (Fall start) • Full-time (1.5 year) • Part-time (3 years)

Curriculum

BST 222: Basics of Statistical Inference
BST 260: Introduction to Data Science
BST 261: Data Science II
BST 262: Computing for Big Data
BST 263: Statistical Learning

An additional five ordinal credits must be taken in computer science, from the following list:

BST 221: Applied Data Structures and Algorithms
BST 249: Bayesian Methodology in Biostatistics
or STAT 220: Bayesian Data Analysis
BST 281: Genomic Data Manipulation
BST 282: Introduction to Computational Biology and Bioinformatics
APMTH 120: Applied Linear Algebra and Big Data
APMTH 207: Advanced Scientific Computing: Stochastic Methods for Data Analysis, Inference, and Optimization
BMI 713: Computing Skills for Biomedical Sciences
BMI 715: Computational Statistics for Biomedical Sciences
CS 105: Privacy and Technology
CS 107: Systems Development for Computational Science
CS 124: Data Structures and Algorithms
CS 165: Data Systems
CS 171: Visualization
CS 182: Artificial Intelligence
CS 205: High Performance Computing for Science and Engineering
STAT 171: Introduction to Stochastic Processes
MIT 6.7940: Dynamic Programming and Reinforcement Learning
MIT 6.8300: Advances in Computer Vision

An additional 22.5 ordinal credits of elective courses must be taken. Examples of electives include:

BST 214: Principles of Clinical Trials
BST 267: Introduction to Social and Biological Networks
BST 283: Cancer Genome Data Science
EPI 286: Database Analytics in Pharmacoepidemiology
RDS 282: Economic Evaluation of Health Policy and Program Management
BMI 706: Data Visualization for Biomedical Applications

Competencies

This 60-credit program is designed to provide students with targeted skills and knowledge required for work in health data science. These specific skills and knowledge domains are:

Recognize study design and its scientific and/or societal context
Practice data gathering, preparation, transformation, and exploration
Prepare data visualization, presentation, and communication
Employ appropriate computing paradigms for efficiency and reproducibility
Evaluate modeling context, apply suitable models and methods, and interpret result

The SM in Health Data Science is intended as a terminal professional degree which will enable students to launch their careers in health-related data science. It can also provide the foundation for further doctoral studies in biostatistics or other quantitative or computational sciences with an emphasis in data science.

Students will receive training in quantitative methods, including applied regression, statistical inference, statistical computing, machine learning, statistical consultation and collaboration, and epidemiology.

Our Community

The Health Data Science Program, part of the Department of Biostatistics, offers a wide range of resources including networking opportunities, career services, and the chance to conduct research with the Harvard Chan School’s faculty and staff. The program has also collected resources to help students improve their technical skills in coding and statistics.

Harvard Chan School offers a wide variety of academic support services, including research support through the Countway Library of Medicine and academic coaching and tutoring for students seeking additional help with either an overall transition to graduate school or specific subject matter.

Beyond academics, the school is home to more than 40 official student organizations focusing on public health issues, cultural affinities, and extra-curricular interests. These groups and other offices throughout the school plan events on campus and around Boston.

Career Outcomes

A Master of Science 60-credit degree opens an extraordinary number of pathways to a meaningful career. Graduates of the SM-60 program are trained to pursue careers in a variety of industries:

Biotech/pharma
Health care organizations
National and international government agencies
Non-Profit/NGO
Public and private sector enterprises
Research institutions
University/research

Eligibility Criteria

The Master of Science 60-credit program (SM-60) requires an undergraduate degree in the mathematical sciences or allied fields (statistics, economics, etc.) or computer science, with a strong interest in health science, or a prior bachelor’s degree or non-US equivalent. Some additional coursework and relevant work experience may be required as well.

Application Requirements

All applications must be submitted through SOPHAS – the centralized application service for public health programs. In addition to the application, applicants must submit:

Statement of purpose and objectives
Official test scores
Three letters of reference
Resumé/curriculum vitae
Post-secondary transcripts or mark sheets (World Education Services credential evaluation for applicants with degrees from outside of the United States.)
English language proficiency (TOEFL/IELTS/Duolingo English Test), if applicable

Application Deadline: December 1

Applicants may apply to only one degree program for either full- or part-time status. Applications are reviewed in their entirety and decisions are released via email in late February/early March. Decisions are not released until all application components are received.