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, student have designed IDMs which 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 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 199 - Intro to Data Science
  • STA 210 - Regression
  • STA 240L Probability for Statistics (recommended) OR STA 230 - Probability
  • STA 360 - Bayesian Modeling
  • STA 432 - Theory and Methods of Statistical Inference and Learning (STA 250 - Statistical Inference - counts if taken Spring 2020 or earlier)
  • 2 electives from the following (or others with DUS approval):
    • STA 310 - Generalized Linear Models
    • STA 313 - Advanced Data Visualization
    • STA 323 - Statistical Computing
    • STA 325 - Machine Learning and Data Mining
    • STA 440 - Capstone
    • STA 444 - Spatio-Temporal Modeling
    • STA 450 - Social Network Analysis
    • STA 561 - Machine Learning
From Computer Science:
  • COMPSCI 201 - Data Structures and Algorithms​​​
  • COMPSCI 316 - Introduction to Databases or COMPSCI 516 - Data-Intensive Systems
  • COMPSCI 330 - Design and Analysis of Algorithms​​
  • COMPSCI 371 - Elements of Machine Learning or COMPSCI 571 - Machine Learning or COMPSCI - 270 Intro. Artificial Intelligence or COMPSCI - 570 Artificial Intelligence
  • 3 electives from the following or with DUS approval:
    • COMPSCI 216 - Everything Data
    • COMPSCI 230 - Discrete Math for CS
    • COMPSCI 250D - Computer Architecture OR COMPSCI 210D – Introduction to Computer Systems
    • COMPSCI 290 - Topics offerings such as Data Science Competition​​​​​​​
    • COMPSCI​​​​​​​ 527 - Computer Vision​​​​​​​
    • COMPSCI​​​​​​​ 590 - Topics offerings such as
      • Algorithmic Aspects of Machine Learning
      • Algorithms for Big Data
      • Algorithmic Foundations of Data Science
      • Algorithms in the Real World
      • Reinforcement Learning

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.