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
Code | Title | Course Units |
---|---|---|
Introductory Data Science and Programming | 1 | |
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 Statistics | 1 | |
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 Science | 1 | |
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 | ||
Electives | 3 | |
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 Units | 6 |
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.