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 2023

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 201/PHIL 201Ethical Computing Systems (GT-AH3)3
CS 220Discrete Structures and their Applications4
CS 345Machine Learning Foundations and Practice3
or DSCI 445 Statistical Machine Learning
DSCI 235Data Wrangling2
DSCI 369Linear Algebra for Data Science4
STAT 158Introduction to R Programming1
STAT 341Statistical Data Analysis I3
Select one of the following1-3
Introduction to Applied Statistical Methods
Statistics Supplement: General Applications
Introduction to Biostatistics
Intro to Theory and Practice of Statistics
Data Science Minor Electives (select a minimum of 3 credits from the list below) 13-4
Program Total Credits:24-27

Data Science Minor Electives

CS 320Algorithms--Theory and Practice3
CS 435Introduction to Big Data4
CS 440Introduction to Artificial Intelligence4
CS 445Introduction to Machine Learning4
DSCI 320Optimization Methods in Data Science3
DSCI 335Inferential Reasoning in Data Analysis3
DSCI 473Introduction to Geometric Data Analysis2
DSCI 475Topological Data Analysis2
STAT 342Statistical Data Analysis II3
STAT 440Bayesian Data Analysis3
STAT 460Applied Multivariate Analysis3

Courses used to satisfy degree (program) requirements outside this minor cannot count toward completing minor electives. (I.e. If using a course to complete a major, the student must take a different course for the minor elective.)

For example: A CS student using STAT 342 as a technical elective as part of their CS degree, cannot count this course as a minor elective in the DSCI minor.