Upon joining the Ph.D. program, each student is assigned an initial advisor who is on the DCDS faculty. This advisor meets with the student to assess their background and advise them on course selection.

All students complete a common core curriculum, and also a domain depth requirement in a social science area. The focus of the first year is on acquiring a common set of tools and an understanding of the ranges and types of problems students may work on as they progress in the program. The entire incoming cohort takes a unique two semester seminar sequence solely for DCDS students, which includes both general topics and a series of data-driven dives into the types of research questions that may be encountered in each of the domain areas.

In addition, students will be exposed to research in different areas through “rotations”, starting in the fall semester of their first year. By the end of the summer following their first year, each student will put together an advisory committee (of at least 2 DCDS faculty, preferably from different tracks) and identify the specific track in which they plan to do research and pursue their Ph.D.


Required Core Courses

  • DCDS 499: Introduction to Graduate Research in Computational and DataSciences. Students must take this course during their first fall semester. The course presents topics and ideas that do not need detailed specific computational or substantive backgrounds, primarily by DCDS faculty about the work they do. An important function of this course is to help you narrow your choice of potential research areas and identify potential rotation mentors (see section on Matching Process for more details). Additionally, the course will also include discussions and assignments to help develop critical research skills, such as selecting good problems, conducting literature reviews, effective time management, and cognizance of ethical implications in research.
  • DCDS 500: Computational and Data Sciences Research Explorations. Students must take this course during their first spring semester. The course lays the foundation for future study and success in transdisciplinary research involving computational and data sciences. Opportunities exist to engage with the conceptual and technical challenges emerging from the increasingly ubiquitous availability of extensive datasets capturing many aspects of human life, social behavior, and scientific discovery. The course emphasizes technical and ethical issues of knowledge development, causal inference, and justice in the context of complex data. Students work in diverse teams to apply methods to case studies.
  • DCDS 510: Introduction to Data Wrangling. Students must take this course during their first spring semester. The course introduces students to tools and techniques for how to collect, maintain, and process large-scale datasets of the kind generated when studying people and social systems. Students learn methods for generating information from multiple sources (e.g., static survey data, dynamic data accessed via API), as well as evaluating information for flaws (e.g., missing or erroneous entries, redundant entries, bias stemming from data collection). The course provides opportunities to ingest data, perform analyses, and document findings using an electronic notebook for reproducibility.
  • Pol Sci 581: Quantitative Political Methodology I or Psych 5066: Quantitative Methods I. The course introduces students to scientific inquiry and basic statistical tools, primarily covering the linear regression model and focusing on how to collect, manage, and analyze data using computer software, and how to effectively communicate to others results from statistical analyses.
  • Pol Sci 582: Quantitative Political Methodology II or Psych 5067: Quantitative Methods II. The course covers advanced methods of statistical analysis in computational and social sciences. Topics include maximum likelihood estimation for various cross-sectional, time series, and measurement models.
  • CSE 502: Fundamentals of Computer Science. This is a fundamental course studying key algorithms, data structures, and their effective use in a variety of applications. It emphasizes the importance of data structure choice and implementation for obtaining the most efficient algorithm for solving a given problem. A key component of this course is worst-case asymptotic analysis, which provides a quick and simple method for determining the scalability and effectiveness of an algorithm. We expect many students will already have this background – it is intended as a pathway for students with little computational training.
  • CSE 417T: Introduction to Machine Learning or ESE 417: Introduction to Machine Learning and Pattern Classification. The course covers the fundamental principles of supervised learning, including generalization, overfitting, regularization, cross-validation, and model selection, and also the basics of core ML techniques and algorithms, including linear models like logistic regression, gradient descent, tree-based and ensemble methods, kernel methods, and artificial neural networks.
  • CSE 517A: Machine Learning. This is an advanced machine learning course that includes models like kernel methods, Gaussian processes, deep neural networks, PCA, and SVD, in addition to discussion of general techniques to deal with problems like unsupervised learning, semi-supervised learning and graph learning.

Domain Depth

Depending on students’ choice of academic track, they must complete the following domain depth requirements:

  1. Political Science: Students must complete three substantive classes in one subfield (American politics, comparative politics, international relations) from a specified list for each subfield.
  2. Psychological and Brain Sciences: Students must complete three substantive graduate-level classes in one subfield (brain, behavior and cognition, clinical science, social/personality, development and aging). With permission, students may substitute the PBS Research Methods course (PBS 5011) for one of those substantive classes depending on their background in psychological science.
  3. Social Work and Public Health: Students must complete a doctoral seminar series, including conceptual foundations of social science, advanced research methods, and a theory seminar, plus an advanced substantive course from an approved list in their area of interest. With permission from the co-directors, students may substitute core courses for substantive classes.
  4. Computational Methodologies: Students must take Advanced Algorithms (CSE 541T) and either Introduction to Artificial Intelligence (CSE 412A) or Bayesian Methods in Machine Learning (CSE 515T). In addition, students must take two substantive classes in their area of interest (one of the other three tracks) from among the classes acceptable for students in that track as noted above.

A typical progression of classes is described below:

Fall of 1st yearSpring of 1st yearFall of 2nd yearSpring of 2nd year
Algorithms (CSE 502)Machine Learning I (CSE 417T or ESE 417)Quantitative Methods II (PolSci 582 or Psych 5067)Machine Learning II (CSE 517A)
Quantitative Methods I (PolSci 581 or Psych 5066)Data Wrangling (DCDS 510)Domain CourseDomain Course
Intro to CDS (DCDS 499)Explorations in CDS (DCDS 500)Domain Course or ElectiveDomain Course or Elective

Further Requirements

  • A minimum of 72 credit hours beyond the bachelor’s level, with a minimum of 36 being course credits (including the core curriculum)
  • A minimum of 24 credit hours of doctoral dissertation research
  • Students must maintain an average grade of B (GPA 3.0) for all 72 credit hours
  • Required courses must be completed with no more than one grade below a B-
  • Up to 24 graduate credit hours may be transferred with the approval of the Graduate Studies Committee, chaired by the Director of Graduate Studies
  • In addition to fulfilling the course and research credit requirements students must
    • complete at least two three-month-long research rotations
    • pass a qualifying exam
    • successfully defend a thesis proposal
    • present and successfully defend a dissertation
    • complete a teaching requirement consisting of two semesters of mentored teaching experience