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 chose a predefined IDM path. Currently, the Department of Statistical Science offers one prefedined option with Computer Science: 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 here, and and then discuss it with the Director of Undergraduate studies (dus@stat.duke.edu). 

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 230 - Probability
  • Sta 250 - Mathematical Statistics
  • Sta 360 - Bayesian Modeling
  • 2 electives from the following (or others with DUS approval):
    • Sta 323 - Stat 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:
  • CS 201 - Data Structures and Algorithms​​​
  • CS 316 - Introduction to Databases or CS 516 - Data-Intensive Systems​​​​​​​
  • CS​​​​​​​ 330 - Design and Analysis of Algorithms​​
  • CS​​​​​​​ 371 - Elements of Machine Learning or CS 571 - Machine Learning or CS - 270 Intro. Artificial Intelligence or CS - 570 Artificial Intelligence
  • 3 electives from the following or with DUS approval:​​​​​​​
    • CS​​​​​​​ 216 - Everything Data
    • CS​​​​​​​ 230 - Discrete Math for CS
    • CS​​​​​​​ 250 - Computer Organization and Programming
    • CS​​​​​​​ 290 - Topics offerings such as Data Science Competition​​​​​​​
    • CS​​​​​​​ 527 - Computer Vision​​​​​​​
    • CS​​​​​​​ 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. 

See here for more information on declaring an IDM.