Data Science and Analytics, Minor

Data science is the study of methods for extracting knowledge from data, combining programming, statistical, and communication skills. The Data Science & Analytics minor is intended for students who wish to complement their major field of study with data science skills. Students will learn the foundational data and programming tools, fundamental statistical inference methods, and modern machine learning approaches – with a focus on application in the social and natural sciences. The minor consists of six courses, three of which are foundational and must fall into specific components (data and programming, statistics, applied data science) and the remaining three are electives that must have a strong link to data science. The minor is not exclusive to a single department, but rather recognizes the wide range of data science courses available in SAS and helps students organize their coursework into a focused data science minor.

Curriculum

Introductory Data Science and Programming1
R
Criminal Justice Data Analytics (Or)
Data Science for Studying Language and the Mind
Introduction to Data Science
Python
Computational Data Exploration
Stories From Data: Programming for Data Journalism
Foundations in Data Science for Communication
Data Science for the Humanities
Foundations of Data Science
Introduction to Computational Physics
Math and Statistics1
Statistics for Biologists
Statistics for the Social Sciences I
Statistics for Economists
Introduction to Data-driven Modeling
Biological Data Science I - Fundamentals of Biostatistics
Engineering Probability
Statistics for Public Policy
Linear Algebra
Data Analysis for the Natural Sciences I: Fundamentals
Statistical Methods PSCI
Advanced Statistical Methods for Political Science
Social Statistics
Introductory Statistics
Introductory Business Statistics
Probability
Statistical Inference
Applied Data Science1
R
Biological Data Analysis
Machine Learning for Social Science
Econometric Machine Learning Methods and Models
Data Science for Public Policy
Applied Data Science
Applied Machine Learning in Business
Introduction to Bayesian Data Analysis
Modern Data Mining
Data Analytics and Statistical Computing
Data Science Using ChatGPT
Python
Applied Machine Learning
Big Data Analytics
Artificial Intelligence Lab: Data, Systems, and Decisions
TinyML: Tiny Machine Learning for Embedded Systems
Applied Data Science - Deep Learning and Artificial Intelligence
Data Analysis for the Natural Sciences II: Machine Learning
Electives3
Forensic Analytics
GIS for the Digital Humanities and Social Sciences
Intro to Digital Archaeology
Astronomical Techniques
Introduction to Computational Biology & Biological Modeling
Algorithmic Ethics
Artificial Intelligence
Ethical Algorithm Design
Database and Information Systems
Big Data, Memory and the Human Brain
Computational Text Analysis for Communication Research
Talking with AI: Computational and Communication Approaches
Econometric Methods and Models
Climate and Big Data
GIS: Mapping Places & Analyzing Spaces
Phonetics II: Data Science
Computer Analysis and Modeling of Biological Signals and Systems
Data and Analysis for Marketing Decisions
Experiments for Business Decision Making (Center Special Topic)
Physical Models of Biological Systems
Political Polling
Health of Populations
Text Analytics
Sample Survey Design
GIS Applications in Social Science
Total Course Units6

The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2025 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.