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, particularly its underpinning statistical techniques, but not necessarily its lower-level computational aspects. Depending on your interests, the other options include:

- The
**Data Science Concentration**within the COMPSCI major, which requires fewer courses on the mathematical and statistical foundations, but focuses more on the computational aspect and practical issues that arise in applying data science. - The IDM in
**MATH+CS on Data Science**, which covers more topics on its mathematical foundations.

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 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) or STA 432 (Stat Learning and Inference)
- 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 465 (High Dimensional Data Analysis)
- STA 561 (Machine Learning)