Interdepartmental Majors


If your academic interests encompass two academic disciplines in Trinity College, you may wish to consider declaring an interdepartmental major (or IDM). The IDM draws in equal measure upon two Trinity College departments or programs that offer a major.  

You can design your own IDM or choose a predefined IDM path. In recent years, students have designed IDMs that combined Statistical Science with Economics, Public Policy, Computer Science, Biology, or Psychology. Currently, the Department of Statistical Science offers one predefined option with Computer Science: the IDM in Data Science. See below for more information about this option.  

If you would like to explore whether an IDM is right for you, first read about choosing and declaring an IDM, then discuss it with the Director of Undergraduate Studies

If your interests encompass more than two majors, consider designing a Program II plan and attending one of their information sessions. Program  II student Trenton Bricken ’20 designed a degree in Minds and Machines: Biological and Artificial Intelligence; find his story here and his senior thesis experience here.

Interdepartmental Major in Data Science (Statistical Science + Computer Science)

The unprecedented increase in the scale of data and computing is fundamentally transforming the way decisions and discoveries are made in many fields today, with a profound impact on all aspects of work and society. In recent years, there has been a significant demand for “data science” expertise in both academia and industry. This program provides a pathway for students interested in a data science-focused pathway for an interdepartmental major in Statistical Science and Computer Science.

Proposed Course Plan

Recall that an IDM must consist of a minimum of 14 courses, split evenly between the two departments (i.e., seven courses in each). Note that some of the courses listed below have prerequisites not listed here.

From Statistics:
  • STA 199L (Intro to Data Science and Statistical Thinking)
  • STA 210L (Regression Analysis)
  • STA 230, 231, or 240L (Probability)
  • STA 360L (Bayesian Inference and Modern Statistical Methods)
  • STA 432 (Theory and Methods of Statistical Learning and Inference)
  • Two electives from the following (or others approved by the DUS):
    • STA 310 (Generalized Linear Models)
    • STA 313L (Advanced Data Visualization)
    • STA 322 (Study Design: Design of Surveys and Causal Studies)
    • STA 323D (Statistical Computing)
    • STA 325L (Machine Learning and Data Mining)
    • STA 344 (Intro to Statistical Modeling of Spatial and Time Series Data)
    • STA 440L (Case Studies in the Practice of Statistics)
    • STA 444L (Statistical Modeling of Spatial and Time Series Data)
    • STA 450L (Theory and Methods for the Analysis of Social Networks)
    • STA 465 (Intro to High-Dimensional Data Analysis)
    • STA 561D (Probabilistic Machine Learning)
From Computer Science:
  • COMPSCI 201 (Data Structures and Algorithms)
  • One of COMPSCI 316 (Introduction to Databases) or 516 (Data-Intensive Systems)
    • NOTE: CS is in the process of changing this requirement to CompSci 210 or COMPSCI 250, as COMPSCI 210 or 250 is now a prerequisite for COMPSCI 316. COMPSCI 316 will now be one of the elective choices. We will allow COMPSCI 316 or COMPSCI 210 or COMPSCI 250 for this requirement for anyone who matriculated before Fall 2022. 
  • COMPSCI 330 (Design and Analysis of Algorithms)
  • One of COMPSCI 371 (Elements of Machine Learning), 370 (Intro. Artificial Intelligence), 570 (Artificial Intelligence), or 671 (Machine Learning)
  • 3 Electives from the following (or others approved by the Director of Undergraduate Studies):
    • COMPSCI 216 (Everything Data)
    • COMPSCI 230 (Discrete Math for CS)
    • COMPSCI 210 (Intro to Computer Systems) or COMPSCI 250 (Computer Architecture)
    • COMPSCI 260 (Computational Genomics)
    • COMPSCI 316 (Introduction to Databases)
      • NOTE: COMPSCI 316 can be an elective if you take COMPSCI 210 or 250 in place of the COMPSCI 316 requirement above.
    • COMPSCI 321/521 (Graph-Matrix Analysis)
    • COMPSCI 333 (Algorithms in the Real World) - previously a 290
    • COMPSCI 474 (Data Science Competition)
    • COMPSCI 527 (Computer Vision)
    • COMPSCI 290/590 (Topics) on the following subjects (some may not be offered regularly):
      • Algorithmic Aspects of Machine Learning 
      • Algorithms for Big Data 
      • Algorithmic Foundations of Data Science 
      • Reinforcement Learning
    • COMPSCI 390 (Special Topics) on the following subjects (some may not be offered regularly):
      • Computational Approaches to Language Processing (Spring 2023)

Because of the rapid developments in the field, course offerings related to data science will likely continue to evolve in the coming years. This course plan will be revised to reflect any such changes.

Declaring this IDM

Students do not automatically receive an IDM by following the recommended pathway; they still must follow the normal IDM application process, although the approval process will be more consistent and streamlined.