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.