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. Immediately prior to each fall semester (starting in 2019), DCDS faculty conduct a “boot camp” in mathematics, statistics, and programming to help bring incoming students up to the level they need to succeed in initial coursework and the program.

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 November 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. 

Curriculum

Required Core Courses (24 credit hours)

  1. CSE 502 (3 credits): This is an existing fundamental course in algorithms and data structures, including significant implementation in an object-oriented programming language (currently Java). We expect many students will already have this background – it is intended as a pathway for students with very little computational training.
  2. Quantitative Methods (QM) I and II (6 credits): A two-semester sequence covering essential probability and statistics, including hypothesis testing, inference, and experimental methodology, using a modern statistical computing language like R. The introductory courses offered by the departments of Psychological and Brain Sciences (PBS 5066) and Political Science (PS 581) will be cross-listed and count for QM I credit. QM II is a course including maximum-likelihood methods, Bayesian and nonparametric models, generalized linear models, and sampling techniques. The course is currently taught as Political Science 582 and will be cross-listed across participating departments.
  3. CSE 5XX: “Data Wrangling”: We are in a new era in terms of the volume and modalities of data generated by, and in trying to measure, human behavior. This will be a new, cross-listed course that introduces students to tools and techniques for how to collect, maintain, and process large-scale datasets of the kind generated in the course of studying people and social systems.
  4. Machine Learning I and II: CSE 417T and 517A (3 credits) This is a two semester sequence in machine learning. Together the two courses cover the fundamental principles of supervised learning, including generalization, overfitting, regularization, cross-validation, and model selection, core ML techniques and algorithms, including linear models like logistic regression, gradient descent, tree-based and ensemble methods, kernel methods, and deep neural networks, and topics in unsupervised learning.
  5. Computational and Data Sciences (CDS) Seminar I and II: A two-semester seminar sequence cross-listed across participating departments (6 credits) and team taught by participating faculty.
    1. CDS Seminar I will be structured around topics and ideas that do not need detailed specific content background. The topics covered will include ethics, the nature of research, robustness and reproducibility of research, and presentations across the different areas of interest to give students an understanding of research in human and social data analytics across the university.
    2. CDS Seminar II will be structured as a series of deep dives into data-driven approaches in each of the domain areas, including a  module on computational methodologies. In each of these modules, the students will either be given a specific dataset to investigate, or a specific hands-on task to complete (e.g., developing a visualization, or assessing how easy a computational tool is for social scientists to use). Students will work in teams on the projects.

Domain Depth

Students will choose one of four focus “tracks” (Political Science, Psychological and Brain Sciences, Social Work and Public Health, or Computational Methodologies). Depending on the track, students must complete the following domain depth requirements:

  1. Political Science track: Students must complete three substantive classes in one subfield (American politics, comparative politics, international relations) from a specified list for each subfield as well as a research design course (PS 540).
  2. Psychological and Brain Sciences track: Students must complete three substantive classes in one subfield (brain, behavior and cognition, clinical science, social/personality, development & aging). With permission, students may substitute the Psychological & Brain Sciences Research Methods Course (PBS 5011) for one of those substantive classes depending on their background in psychological science.  
  3. Social Work & Public Health track: Students must complete a three-course core doctoral seminar series, including conceptual foundations of social science, advanced research methods, and a theory seminar, either in public health or in social work. Students will also be required to take an advanced substantive course from an approved list in their area of interest.
  4. Computational Methodologies track: Students must take CSE 541T: Advanced Algorithms and either CSE 511A: Artificial Intelligence or CSE 515T: Bayesian Methods in Machine Learning. In addition, students must take two substantive classes in their area of interest (social work & public health, political science, or psychological and brain sciences) from among the classes acceptable for students in that track as noted above.

A typical progression of classes is described below, separately for students who enter with and without more extensive computational backgrounds

 

Students without much Computer Science background:

Fall of 1st year

Spring of 1st year

Fall of 2nd year

Spring of 2nd year

CSE 502

CSE 417T: ML I

Quant Methods II

CSE 517A: ML II

Quant Methods I

Data Wrangling

Domain Course

Domain Course

CDS Seminar I

CDS Seminar II

Domain Course or Elective

Domain Course or Elective

 

Students with more Computer Science background:

Fall of 1st year

Spring of 1st year

Fall of 2nd year

Spring of 2nd year

CSE 417T: ML I OR

Domain Course

CSE 417T: ML I OR Domain Course

Quant Methods II

CSE 517A: ML II

Quant Methods I

Data Wrangling

Domain Course

Domain Course

CDS Seminar I

CDS Seminar II

Domain Course or Elective

Domain Course or Elective

Further Requirements

  • A minimum of 72 credit hours beyond the bachelor’s level, with a minimum of 37 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