If your academic interests encompass two academic disciplines in Trinity College, you may wish to consider declaring an interdepartmental major (IDM). The IDM draws in equal measure upon two Trinity College departments or programs that offer a major, each requiring 7 courses for a total of 14 courses.

Computer Science is working with several partners to create “predefined” IDMs, listed below, whose programs of study are pre-approved by the respective Directors of Undergraduate Studies. If your interests happen to align with one of these predefined IDMs, we highly encourage to follow it because it makes planning ahead and optimizing your program of study easier, and it allows our advisors to provide more informed advice and make more consistent decisions. There is currently one such predefined IDM with Statistics on Data Science.

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 Directors of Undergraduate Studies in Computer Science and the other discipline. If you would like to pursue an IDM involving some other discipline for which nothing has been predefined, please follow these guidelines:

- If you have taken COMPSCI 101, 102, or 116, we can count one towards the 7 COMPSCI courses required.
- Include in your 7 COMPSCI courses 201, 250, and one of 230 and 330.
- Note that 230 is a prerequisite for 330 but could be skipped if you have probability and other math courses.

- COMPSCI electives should make sense for the other disciplinary you are considering.
- For example, 260 would make sense Biology, 223 would make sense with Economics, and 342 would make sense with Public Policy.

(These general guidelines do not apply to specific, predefined IDMs.)

# IDM in STA+CS on Data Science

The Departments of Statistical Science and Computer Science have collaboratively mapped out a data science pathway for an interdepartmental major (IDM) between the two departments. This pathway makes it easier for you to identify courses relevant to a career in data science, and to plan and optimize your program of study accordingly.

Note that this IDM is intended for students interested in data science but not necessarily its lower-level computational aspects. If you would like to pursue a COMPSCI major but are still interested in data science, you may consider the Data Science Concentration within the COMPSCI major; that option requires fewer courses on statistical data analysis, but focuses more on the computational aspects of data science.

Note also that some STAT and COMPSCI and 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 their AP or IB or pre-college equivalents
- Calculus: MATH 111L and MATH 112L, or their AP or IB or pre-college equivalents
- Multivariable Calculus: one of MATH 202, 212, or 222, taken at Duke or transferred
- Linear Algebra: one of MATH 216, 218, or 221, taken at Duke or transferred

### From Computer Science:

- COMPSCI 201 (Data Structures and Algorithms)
- One of COMPSCI 316 (Introduction to Databases) or 516 (Data-Intensive Systems)
- 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)
- *Note: 370 was renumbered from 270 in Fall 2019, and 671 from 571 in Spring 2019.
- Note that 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 230 (Discrete Math for CS)
- COMPSCI 250 (Computer Organization and Programming)
- COMPSCI 527 (Computer Vision)
- COMPSCI 290/590 (Topics) on the following subjects (some may not be offered regularly):
- Algorithmic Aspects of Machine Learning (Ge)
- Algorithms for Big Data (Machanavajjhala)
- Algorithmic Foundations of Data Science (Munagala)
- Algorithms in the Real World (Maggs)
- Data Science Competition (Rudin)
- Reinforcement Learning (Parr)

### 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 approved by the Director of Undergraduate Studies):
- 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)