Curriculum for students who matriculated at Duke prior to Fall 2026...
Students who matriculated at Duke in Fall 2026 or later, please see our new BS degree requirements for the Data Science Concentration HERE.
This concentration in data science is intended for COMPSCI majors interested in studying data science in depth, with a distinctively computational focus. If you are interested in data science but not necessarily in becoming a COMPSCI major, there are other options that are less concerned with the lower-level computational aspects:
- The IDM (interdepartmental major) in Stat+CS on Data Science covers more topics on statistical data analysis, while
- The IDM in Math+CS on Data Science focuses more on the mathematical foundations of data science.
Prerequisites
- One of the following introductory COMPSCI courses or equivalent:
- COMPSCI 101L - Introduction to Computer Science
- COMPSCI 102 - Interdisciplinary Introduction to Computer Science
- COMPSCI 116 - Foundations of Data Science
- MATH 111L - Introductory Calculus I or equivalent
- MATH 112L - Introductory Calculus II or equivalent
- Probability: STA 230, STA 231, STA 240
Requirements
- COMPSCI 201 - Data Structures and Algorithms
- COMPSCI 216 - Everything Data
- COMPSCI 230 - Discrete Math for Computer Science or 231D - Discrete Math with Functional Programming and Proofs or 232 - Discrete Mathematics and Proofs see substitutions
- COMPSCI 210D - Introduction to Computer Systems or COMPSCI 250D - Computer Architecture
- COMPSCI 316 - Introduction to Databases or COMPSCI 516 Database Systems
- COMPSCI 330 - Design & Analysis of Algorithms
- Two courses in MATH/STA:
- Linear Algebra: One of MATH 218, 221 OR COMPSCI 270L
- Statistics: STA 250*, STA 360**, STA 432, OR MATH 342
*NOTE: ECE 480 is an approved substitution for STA 250 [NOTE: As of Fall 2020, STA 250 is no longer offered.]
**NOTE: You cannot use STA 360 as an elective if you are using it as the requirement here.
- One of the following courses:
- COMPSCI 370 - Intro. Artificial Intelligence
- COMPSCI 371 - Elements of Machine Learning
- COMPSCI 570 - Artificial Intelligence
- COMPSCI 571 - Probabilistic Machine Learning
- COMPSCI 671 - Machine Learning
- Three Electives at 200-level or higher. One out of the three electives must be a COMPSCI course.
- One elective in COMPSCI (independent Study possible), MATH, STA, ECE, or a related area approved by the Director of Undergraduate Studies.
- Two additional courses must be drawn from either the above list (COMPSCI 370, 371, 570, 571, 671) or the list below.
- STA 325 - Machine Learning and Data Mining
- STA 360 - Bayesian Inference - You cannot count STA 360 as an elective if you are using it for the Stats requirement above
- COMPSCI 226 - User Research Methods in Human-Centered Computing
- COMPSCI 260 - Computational Genomics
- COMPSCI 290 - Special Topics on the following subjects (some may not be offered regularly):
- Intro to Applied Machine Learning (Spring 2025)
- COMPSCI 321/521 - Graph-Matrix Analysis
- COMPSCI 333 - Algorithms in the Real World
- COMPSCI 390 - Special Topics on the following subjects (some may not be offered regularly):
- Computational Approaches to Language Processing (Spring 2023)
- Algorithmic Foundations of Data Science (Spring 2025)
- COMPSCI 445/MATH 465 - Intro to High Dimensional Data Analysis
- 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):
- Reinforcement Learning
- Algorithmic Foundations of Data Science
- Focus on SARS-Cov-2 and COVID-19, cross CBB 590-01 (Spring 2021)
- Causality and Fairness for Data Analysis (Spring 2023)
- Data Science Concepts and Applications (Spring 2023)
- Elements of Deep Learning (Spring 2023)
- 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)