BS Concentration in Data Science

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:

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 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: MATH 218 or MATH 221
    • 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 (Fain)
      • 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)