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 student-proposed IDM or choose a predefined department-originated IDM. 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 the Department of Computer Science: the IDM in Data Science. See below for more information about this option.  

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 department). 

Note also that some STAT and COMPSCI courses required below need Calculus, Multivariable Calculus, Linear Algebra, and Introduction to Computer Science as prerequisites. More specifically:

  • Introduction to Computer Science: one of COMPSCI 101, 102, 116, or Engineering 103L, or equivalent (check courses for pre-reqs)
  • Calculus 1: MATH 111L, or (combination of 105L and 106L), or MATH 21 or equivalent
  • Calculus 2: MATH 112L, 122L, or MATH 22 or equivalent
  • Multivariable Calculus: one of MATH 202, 212, 219, or 222, taken at Duke or transferred
  • Linear Algebra: one of MATH 218, 221, or 216 taken at Duke or transferred

Note that the math pre-requisites are the same for the Statistical Science major but the major concentrations are more specific.

From Statistics:
  • STA 198L or STA 198CNL - Introduction to Global Health Data Science or STA 199L - Introduction to Data Science and Statistical Thinking
  • STA 221L - Regression Analysis: Theory and Applications
    • Alternatively, STA 210L - Regression Analysis + STA 211 - The Mathematics of Regression (Note: This pathway was only open to students who completed STA 210L before Fall 2024. STA 211 was offered for the last time in Fall 2024.)
  • STA 240L - Probability for Statistics or STA 230 / MATH 230 - Probability or STA 230S / MATH 230S - Probability Inquiry Based Learning or STA 231 / MATH 340 - Advanced Introduction to Probability or MATH 231 - An Algorithmic Introduction to Probability and its Applications
  • STA 332 - Statistical Inference (previously numbered STA 432)
  • STA 402L - Bayesian Statistical Modeling and Data Analysis (previously numbered STA 360L)
  • 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 323L - Statistical Computing (previously numbered STA 323D)
    • STA 325L - Machine Learning and Data Mining
    • STA 344L - Intro to Statistical Modeling of Spatial and Time Series Data
    • STA 440L - Case Studies in the Practice of Statistics
    • STA 344L - Introduction to the Statistical Modeling of Spatial and Time Series Data
    • STA 444L - Statistical Modeling of Spatial and Time Series Data
    • STA 465 - Intro to High-Dimensional Data Analysis
    • STA 561D - Probabilistic Machine Learning
From Computer Science:
  • COMPSCI 201 - Data Structures and Algorithms
  • COMPSCI 210** - Intro to Computer Systems or COMPSCI 250** - Computer Architecture
  • COMPSCI 330 - Design and Analysis of Algorithms
  • One of these courses: COMPSCI 370* - Intro. Artificial Intelligence OR COMPSCI 371 - Elements of Machine Learning OR COMPSCI 372 - Applied Machine Learning OR COMPSCI 570 - Artificial Intelligence OR COMPSCI 671* - Machine Learning
    • *NOTE: COMPSCI 370 was re-numbered from 270 in Fall 2019, and COMPSCI 671 from 571 in Spring 2019.
    • NOTE: COMPSCI 571 (not listed here) is cross-listed as STA 561, and can be used as an elective for the requirement by statistics.
  • 3 Electives from the following (or others approved by the Director of Undergraduate Studies):
    • COMPSCI 216 - Everything Data
    • COMPSCI 226 - User Research Methods in Human-Centered Computing
    • COMPSCI 230 - Discrete Math for Computer Science or 231D - Discrete Math with Functional Programming and Proofs or 232 - Discrete Mathematics and Proofs
    • COMPSCI 260 - Computational Genomics
    • COMPSCI 290 - Special Topics on the following subjects (some may not be offered regularly):
      • Introduction to Applied Machine Learning (Spring 2025)
    • COMPSCI 316 - Introduction to Databases or CompSci 516 - Data-Intensive Systems
    • COMPSCI 321/521 - Graph-Matrix Analysis
    • COMPSCI 333 - Algorithms in the Real World, previously a COMPSCI 290
    • COMPSCI 390 - Special Topics on the following subjects (some may not be offered regularly):
      • Algorithmic Foundations of Data Science (Spring 2025)
    • COMPSCI 474 - Data Science Competition
    • COMPSCI 526 - Data Science
    • COMPSCI 527 - Computer Vision
    • COMPSCI 590 - Special Topics on the following subjects (some may not be offered regularly):
      • Theory of Deep Learning (Spring 2025)
      • Generative Models: Foundations and Applications (Spring 2025)
      • Causal Inference in Data Analysis with Applications to Fairness and Explanations (Spring 2025)
    • COMPSCI 290/590 Special 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
    • **NOTE: For anyone who matriculated before Fall 2022, COMPSCI 316 may be used in lieu of the COMPSCI 210 or COMPSCI 250 requirement. In this case, then COMPSCI 210 or COMPSCI 250D can be used as one of the three electives.

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 an IDM

  • Student-proposed IDM: Students should first read about choosing and declaring an IDM, then discuss it with the Director of Undergraduate Studies of the two departments of interest, and then fill out the IDM forms found on that page.
  • Department-originated IDM: Students declare the department-proposed IDM as part of the major declaration process rather than filling out the IDM forms.