Secondary Field in Data Science

Doctoral students from across the Graduate School of Arts and Sciences can complete a Secondary Field in Data Science

Completion of the Secondary Field will equip students with rigorous statistical and computational methods for managing, analyzing and visualizing data to answer questions from whatever physical, social, or applied science domain they are working in. The Secondary Field offers strong preparation in statistical modeling, machine learning, optimization, management and analysis of massive data sets, and data acquisition.  Students completing the Data Science Secondary Field will be exposed to topics such as reproducible data analysis, collaborative problem solving, visualization and communication, and security and ethical issues that arise in data science.

How to Apply

Students interested in applying to the Data Science Secondary Field should fill out and submit the application form no later than the Fall semester of their third year of study. Applications may be submitted twice a year, in the spring semester (deadline, March 1) and fall semester (deadline, October 1), to the Academic Operations Administrator in the Office of Master's and Professional Programs (SEC 1.312-12). Questions about Secondary Field requirements should be directed to The application, which will include a proposed Plan of Study, must also be approved by a student’s home department Director of Graduate Study and their advisor. 


To earn the Secondary Field in Data Science students must complete a Plan of Study with five courses meeting the requirements below and pass a short oral examination by a faculty committee.

Each student's plan of study for the Secondary Field will include:

  1. Core Courses

At least 3 of the Data Science core courses:

  • AC 209a*         Data Science 1: Introduction to Data Science
  • AC 209b*         Data Science 2: Advanced Topics in Data Science
  • AM 207            Advanced Scientific Computing: Stochastic Methods for Data Analysis, Inference, and Optimization
  • AC 207            Systems Development for Computational Science
  • AC 221            Critical Thinking in Data Science

*In certain circumstances, students can count CS 109a/b in place of AC 209a/b.

  1. Electives

Two electives in Computer Science or Statistics. Students may choose from a wide variety of elective courses offered by the Computer Science and Statistics faculties. A list of suggested electives can be found here.

Alternatively, students may choose to satisfy the elective requirement by taking additional core courses. Students may also choose, as a substitute for one elective, either AC 297r (the Data Science Capstone Project course), or four credits (two semesters) of AC298r (the Interdisciplinary seminar in Computational and Data Science).

Upon completion of required coursework, each candidate for the Data Science Secondary Field will be required to give an oral presentation on a data science research project - typically a small part of the student’s doctoral thesis work or a data-focused side project they have worked on in their lab. Students will be expected to display achievement of the Data Science program’s learning outcomes, including the ability to communicate their work in an interdisciplinary context. 

SEAS will organize a Secondary Field presentation event once each semester. 


Daniel Weinstock, Director of Master's Education, will be responsible for front-line advising of students in the Data Science Secondary Field. Students interested in the secondary field are encouraged to reach out to Dr. Weinstock before submitting an application.

Students enrolled in the Secondary Field are encouraged to participate in the activities of the community, including technical and interdisciplinary colloquia, skill-building workshops, and tech treks to local companies working to apply computation and data science in many different domains.