Master's in Data Science

Master’s in Data Science Program Overview

The Data Science master's program, jointly led by the Computer Science and Statistics faculties, trains 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.

To earn the Master of Science in Data Science, students must complete 12 courses. This requires students to be on campus for at least 3 semesters (one and a half academic years). Some students will choose to extend their studies for a fourth semester to take additional courses or complete a master’s thesis research project.

SEAS will be hosting virtual information sessions this Fall for students interested in the Data Science program. Registration for these sessions is available on the Admissions Events page for prospective graduate students.

Why pursue a master’s degree in Data Science?

With companies and organizations better able to capture data in a multitude of ways, data-driven decision making is changing the way businesses operate. Powerful analytics tools can model and predict how consumers will behave or markets will respond. Consequently, an understanding of data science is a 21st century job skill that can be beneficial in many different careers.

Data Science Degree Career Paths

Data Science career paths are flexible. There are different pathways to use data science skills.

  • Data science professional - data analyst, database developer, or data scientist.
  • Analytics-enabled jobs - functional business analyst or data-driven manager.

Data science professionals like data analysts can become qualified for a data science or data system developer role depending on where they deepen their expertise. By expanding knowledge in Artificial Intelligence, statistics, data management, and big data analytics, a data analyst can transition into a data scientist role. By building on existing technical skills in Python, relational databases, and machine learning, a data analyst can become a data system developer. 


There are no formal prerequisites for applicants to this master’s program. However, successful applicants do need to have sufficient background knowledge of calculus, linear algebra and differential equations; familiarity with probability and statistical inference; fluency in at least one programming language such as python or R, and an understanding of basic computer science concepts. As Data Science is an interdisciplinary field, SEAS welcomes applicants with undergraduate training in a wide range of academic disciplines. 

How to Apply

Learn more about how to apply to the Data Science degree program or apply now.

What should a graduate of the Data Science program be able to do?

The design of the program is based on eleven learning outcomes developed through discussions between the computer science and statistics faculty:

  • 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

Financing Your Degree

Students typically finance their master’s degree program with a combination of loans, savings, family support, grants (from governments, foundations and companies), fellowships and scholarships. We recommend you visit the Harvard Kenneth C. Griffin Graduate School of Arts and Sciences (Harvard Griffin GSAS) Funding and Financial Aid website prior to your application to learn more about your options.

Teaching Fellowships

Approximately 15% of our students are paid Teaching Fellows, usually in the second year. TFing in the first semester is highly unusual. Teaching compensation is paid out at Harvard graduate student rates.

Faculty Director, Master's in Data Science Program and Co-Chair, FAS Standing Committee on Higher Degrees in Data Science
Scientific Program Director and Lecturer
Assistant Director of Graduate Studies in Data Science
Director of Master's Education