Master of Science in Data Science

Under the joint academic leadership of the Computer Science and Statistics faculties and administered through the Institute for Applied Computational Science (IACS), this three semester long master's program will train students in the rapidly growing field of data science.

Data Science lies at the intersection of statistical methodology, computational science, and a wide range of application domains.  The program offers strong preparation in statistical modeling, machine learning, optimization, management and analysis of massive data sets, and data acquisition.  The program focuses on topics such as reproducible data analysis, collaborative problem solving, visualization and communication, and security and ethical issues that arise in data science.

IACS hosted an on-campus information session for prospective students.  Watch the video of the program overview here

Read more about our new program in AMSTATNEWS, The Membership Magazine of the American Statistical Association.

ADMISSIONS

Students are admitted to the program through the Graduate School of Arts and Sciences (GSAS). GSAS requires online submission of applications for graduate study. In general, applicants must hold the BA or equivalent degree. GSAS considers students for admission to the fall term only.  

Applications to the Master of Data Science degree program for entry into the program in Fall 2019 will be accepted beginning September 2018. 

LEARNING OUTCOMES

The design of the program was developed through discussions between the computer science and statistics faculty and other Harvard departments and schools, including the IACS Advisory Board and the Data Science Education Committee. Each student's plan of study should address a set of learning outcomes developed from these discussions.  The learning outcomes answer the question: "What should a graduate of our data science program be able to do?"

  • Build statistical models and understand their power and limitations
  • Design an experiment
  • Use machine learning and optimization to make decisions
  • Acquire, clean, and manage data
  • Visualize data for exploration, analysis, and communication
  • Collaborate within teams
  • Deliver reproducible data analysis
  • Manage and analyze massive data sets
  • Assemble computational pipelines to support data science from widely available tools
  • Conduct data science activities aware of and according to policy, privacy, security and ethical considerations
  • Apply problem-solving strategies to open-ended questions

DEGREE REQUIREMENTS

Requirements for the SM degree in Data Science address these learning outcomes.  A total of 12 courses are required, typically completed over 3 semesters.  

Each student's plan of study for the SM degree will include:

  • ​The four technical core courses:
    • AC 209a Data Science I
    • AC 209b Data Science II
    • AM 207 Advanced Scientific Computing: Stochastic Methods for Data Analysis, Inference, and Optimization
    • CS 207 Systems Development for Computational Science
  • One semester of the new Critical Thinking and Data Science course
  • At least one research experience.  This requirement can be satisfied by the AC 297r Capstone project course or a semester-length independent study project
  • At least one Computer Science elective and one Statistics elective chosen from the suggested electives list
  • Up to one seminar course - AC 298r or similar
  • Up to four other data science electives (from other FAS departments or other schools at Harvard)
  • As a final requirement, the presentation of a poster on a data science project at the annual IACS Project Showcase
SM Course Requirements at a Glance
SM Requirements Number Required
Technical core 4
Critical Thinking and Data Science 1
Research Experience (AC 297r or AC 299r) 1
Computer Science elective 1
Statistical elective 1

Additional courses:

Computer Science electives (up to 4)
Statistical electives (up to 4)
Other Data Science electives (up to 4)
Research course (up to 1)
Seminar course (up to 1)

4
TOTAL 12

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