Students with a minor in Data Science will receive foundational training in modern data science to complement and enhance their major field of study.
Effective Fall 2020
Additional coursework may be required due to prerequisites.
Students must satisfactorily complete the total credits required for the minor. Minors and interdisciplinary minors require 12 or more upper-division (300- to 400-level) credits.
|CS 163||CS1---No Prior Programming Experience||4|
|CS 165||CS2--Data Structures||4|
|DSCI 235||Data Wrangling||2|
|DSCI 320||Optimization Methods in Data Science||3|
|DSCI 335||Inferential Reasoning in Data Analysis||3|
|DSCI 369||Linear Algebra for Data Science||4|
|Data Science Minor Electives (select a minimum of 3 credits from the list below) 1||3-4|
|Program Total Credits:||23-24|
Data Science Minor Electives
|CS 345||Machine Learning Foundations and Practice||3|
|CS 425||Introduction to Bioinformatics Algorithms||4|
|CS 435||Introduction to Big Data||4|
|CS 440||Introduction to Artificial Intelligence||4|
|DSCI 445||Statistical Machine Learning||3|
|DSCI 473||Introduction to Geometric Data Analysis||2|
|DSCI 475||Topological Data Analysis||2|
|STAT 341||Statistical Data Analysis I||3|
Courses used to satisfy requirements outside this minor cannot count toward completing this minor. If using a course toward major requirements, the student must take a different course for this minor.