Data Science Minor (Non-Teaching)

The Data Science Minor consists of

  • CSCI 127, Joy and Beauty of Data, 4 credits
  • CSCI 132, Basic Data Structures and Algorithms, 4 credits
  • CSCI 232, Data Structures and Algorithms, 4 credits
  • One 3-credit course from
    • CSCI 246, Discrete Structures
    • M 221, Introduction to Linear Algebra
    • M 242, Methods of Proof
  • STAT 216Q, Introduction to Statistics, 3 credits
  • STAT 217Q, Intermediate Statistical Concepts, 3 credits
  • Three upper-division courses (at least 3 computer science credits and at least 3 math or stat credits) from
    • CSCI 432, Advanced Algorithm Topics
    • CSCI 440, Database Systems
    • CSCI 447, Machine Learning
    • CSCI 451, Computational Biology
    • M 386R, Software Applications in Mathematics
    • M 441, Numerical Linear Algebra & Optimization
    • STAT 408, Statistical Computing and Graphical Analysis
    • STAT 411, Methods for Data Analysis I
    • STAT 412, Methods for Data Analysis II
    • STAT 425, Biostatistical Data Analysis
    • STAT 439, Introduction to Categorical Data Analysis
    • STAT 441, Experimental Design
    • STAT 446, Sampling

Note 1: Additional relevant, upper-division courses will be added as options as they become available.  For example, the following courses are under discussion: a Library Science (LSCI) Data Curation course, a Mathematics (M) Discrete Optimization course and a Computer Science (CSCI) Data Mining course.

Note 2: 490R (Undergraduate Research), 491 (Special Topics), 492 (Independent Study) or 494 (Seminar) credits related to data science also count.  These credits must be applied via DegreeWorks Exceptions.