BS Concentration in Data Science

Concentration in Data Science - Academic Requirements

We have revised our BS degree requirements for the Data Science Concentration to include AI as part of the core, including two AI courses--COMPSCI 270 and an AI core course. First-year students who matriculate at Duke in Fall 2026 will be subject to these major changes.

NOTE: 2 of the 3 electives MUST be a COMPSCI course.

Students who matriculated at Duke prior to Fall 2026 are grandfathered into the old concentration requirements HERE.

Students must successfully complete at least 15 required units to finish the Computer Science major. Degree candidates must successfully complete at least 34 units total to earn the Bachelor of Science degree.

Prerequisite Courses (4 units) from the following:

  1. Introductory Course - COMPSCI 101L* OR COMPSCI 102L OR COMPSCI 116
  2. Math Requirement - MATH 111L & MATH 112L OR MATH 21 & MATH 22 / Other equivalents may also count.
  3. Math/Stats Requirement - one of the following:
    • STA 230/MATH 230
    • STA 230S/MATH 230S
    • STA231/MATH 340
    • MATH 231
    • STA 240L

*NOTE: We will waive the COMPSCI 101L requirement, if you successfully take and complete COMPSCI 201.

Core Courses (5 units) from the following:

  1. COMPSCI 201
  2. COMPSCI 230 OR COMPSCI 231D OR COMPSCI 232 see substitutions
  3. COMPSCI 210D OR COMPSCI 250D
  4. COMPSCI 270 OR two courses:
    • one of MATH 202, MATH 212 or MATH 219
    • AND one of MATH 216, MATH 218 or MATH 221
  5. COMPSCI 330

Statistics Course (1 unit) - one of the following:

  • STA 332
  • STA 402L

Systems Course (1 unit) - one of the following:

  • COMPSCI 316
  • COMPSCI 516

Artificial Intelligence Course (1 unit) - one of the following:

  • COMPSCI 370 OR COMPSCI 570
  • COMPSCI 371 OR COMPSCI 671
  • COMPSCI 526
  • COMPSCI 527
  • COMPSCI 575

Data Science Course (1 unit) - the following course:

  • COMPSCI 216

Concentration Electives (3 units)

3 COMPSCI courses - 200-level or higher, with QC code from the Artificial Intelligence Core Course list above AND/OR the list below. 2 of the 3 courses MUST be COMPSCI courses.

  • STA 325 - Machine Learning and Data Mining
  • STA 402L - Bayesian Statistical Modeling and Data Analysis
  • 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
  • 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
    • Algorithmic Foundations of Data Science
  • COMPSCI 445 - Intro to High Dimensional Data Analysis (cross MATH 465)
  • 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)
    • Causality and Fairness for Data Analysis
    • Data Science Concepts and Applications
    • Elements of Deep Learning
    • Theory of Deep Learning
    • Generative Models: Foundations and Applications
    • Causal Inference in Data Analysis with Applications to Fairness and Explanations
  • one independent study course which has a substantial emphasis related to data science within COMPSCI, MATH, STA, ECE, or a related area approved by the Director of Undergraduate Studies

A comprehensive list of elective courses is available to students in Stellic, the degree audit system.

Free Elective (1 unit)

1 COMPSCI course - 200-level or higher and MAY be an independent study course which has a substantial emphasis on computer science topics within MATH, STA, ECE, or a related area approved by the Director of Undergraduate Studies (1 unit)

A comprehensive list of elective courses is available to students in Stellic(link opens in a new window/tab)(link opens in a new window/tab), the degree audit system.

To add, drop, or change a concentration, complete the Academic Plan Change form.

Academic Plan Change(link opens in a new window/tab)(link opens in a new window/tab)(link opens in a new window/tab)

The 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 which balance out with more statistical science or mathematics:

Eligibility for specific substitutions may vary by academic plan. A consultation with the Director of Undergraduate Studies is required before final approval can be granted to receive credit for a substitution. 

See the CS Course Substitutions Guide for possible course substitutions which have been pre-approved by the DUS.

Course Substitutions Guide

Departmental Graduation with Distinction

A program for Graduation with Distinction (GWD) in Computer Science is available. Candidates for a degree with Distinction, High Distinction, or Highest Distinction must apply to the Director of Undergraduate Studies (DUS) and meet certain criteria.

See the CS Graduation with Distinction webpage for additional information, resources, and how to apply.

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