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, machine learning) 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 | ||
| Data Science for Beginners | ||
| Criminal Justice Data Analytics (Or) | ||
| Data Science for Studying Language and the Mind | ||
| Introduction to Data Science | ||
Python | ||
| Computational Data Exploration | ||
| Data Science for the Humanities | ||
| Foundations of Data Science | ||
| Introduction to Computational Physics | ||
| Data Analytics and Statistical Computing | ||
| 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 | ||
| Data Analysis for the Natural Sciences I: Fundamentals | ||
| Statistical Methods PSCI | ||
| Social Statistics | ||
| Introductory Statistics | ||
| Applied Data Science | 1 | |
R | ||
| Biological Data Analysis | ||
| Econometric Machine Learning Methods and Models | ||
| Applied Data Science | ||
| Introduction to Bayesian Data Analysis | ||
| Modern Data Mining | ||
Python | ||
| Applied Machine Learning | ||
| Data Analysis for the Natural Sciences II: Machine Learning | ||
| Electives | 3 | |
| Astronomical Techniques | ||
| Introduction to Computational Biology & Biological Modeling | ||
| Computational Text Analysis for Communication Research | ||
| GIS: Mapping Places & Analyzing Spaces | ||
| Artificial Intelligence | ||
| Database and Information Systems | ||
| Big Data, Memory and the Human Brain | ||
| Phonetics II: Data Science | ||
| Computer Analysis and Modeling of Biological Signals and Systems | ||
| Ethics of Artificial Intelligence | ||
| Physical Models of Biological Systems | ||
| Survey Research and Design | ||
| Health of Populations | ||
| Text Analytics | ||
| 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 2023 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.