U-IDATA-BS - Interdepartmental: Data Science (STA+CS)

An IDM must consist of a minimum of 14 courses, split evenly between the two departments (seven courses in each). Some of the courses listed below have prerequisites not listed here.

From Statistics

• STA 199L - Intro to Data Science

• STA 210L - Regression

• STA 240L Probability for Statistics (recommended) OR STA 230 - Probability

• STA 360L - Bayesian Modeling

• STA 432 - Theory and Methods of Statistical Inference and Learning (STA 250 - Statistical Inference - counts if taken Spring 2020 or earlier)

• 2 electives from the following (or others with DUS approval):

  • STA 310 - Generalized Linear Models

  • STA 313L - Advanced Data Visualization

  • STA 323D - Statistical Computing

  • STA 325L - Machine Learning and Data Mining

  • STA 440L - Capstone

  • STA 444L - Spatio-Temporal Modeling

  • STA 450L - Social Network Analysis

  • STA 561D - Machine Learning

From Computer Science

• COMPSCI 201 - Data Structures and Algorithms​​​

• COMPSCI 316 - Introduction to Databases or COMPSCI 516 - Data-Intensive Systems

• COMPSCI 330 - Design and Analysis of Algorithms​​

• COMPSCI 371 - Elements of Machine Learning or COMPSCI 571 - Machine Learning or COMPSCI - 270 Intro. Artificial Intelligence or COMPSCI - 570 Artificial Intelligence

• 3 electives from the following or with DUS approval:

  • COMPSCI 216 - Everything Data

  • COMPSCI 230 - Discrete Math for CS

  • COMPSCI 250D - Computer Architecture OR COMPSCI 210D – Introduction to Computer Systems

  • COMPSCI 290 - Topics offerings such as Data Science Competition

  • COMPSCI​​​​​​​ 527 - Computer Vision​​​​​​​

  • COMPSCI​​​​​​​ 590 - Topics offerings such as

    • Algorithmic Aspects of Machine Learning

    • Algorithms for Big Data

    • Algorithmic Foundations of Data Science

    • Algorithms in the Real World

    • Reinforcement Learning