To declare the Data Science minor, students must successfully complete the Data Literacy course, Data 101 (198:142/960:142), with a grade of C or better.
Required Core Courses
The following courses are required for all Data Science minors:
01:198:142/01:960:142 Data 101: Data Literacy
01:960:291 Statistical Inference for Data Science or one of the following equivalents:
01:960:212 Statistics II
01:960:384 Intermediate Statistical Analysis
33:136:385 Statistical Methods in Business
One of the following Data Management courses:
01:198:210 Data Management for Data Science
01:960:295 Data Management and Wrangling with R
04:547:221 Fundamentals of Data Curation and Management
One of the following domain courses:
01:198:439 Introduction to Data Science
01:220:322 Econometrics
01:359:207 Data and Culture
01:447:303 Computational Genetics of Big Data
01:450:320 Spatial Data Analysis
01:450:321 Geographic Information Systems
01:450:330 Geographical Research Method
01:750:345 Computational Astrophysics
01:790:391 Data Science for Political Science
01:920:360 Computational Social Science
01:960:365 Introduction to Bayesian Data Analysis
01:960:463 Regression Methods
01:960:486 Applied Statistical Learning
04:189:220 Data in Context
04:547:321 Information Visualization
11:126:486. Functional Genomics for Research
14:332:443 Machine Learning for Engineers
One of the following Capstone Course:
Data Science Capstone Project (01:198:310) - default, or
Data Science and Econometrics (01:220:323)
Minor in Data Science-Computer Science Track:
This track targets students with existing programming experience. It requires courses in statistics, data-centric programming, data management, and data analysis. Note that the courses 01:198:461 and 01:198:462 have prerequisites that include courses in addition to those required for the minor.
- 01:960:463 Regression Methods
- One of the following Machine Learning courses:
01:198:461 Machine Learning Principles
01:198:462 Introduction to Deep Learning
Minor in Data Science-Statistics Track:
This track targets students with a quantitative background but perhaps little programming experience. It can be pursued without any additional prerequisite courses beyond those in requirements I, II, and III.
- 01:960:486 Applied Statistical Learning
04:547:321 Information Visualization
04:189:220 Data in context
01:960:463 Regression Methods
Minor in Data Science-Economics Track:
This track is intended mainly for Economics majors or Quantitative Economics minors. In any case, completion of the intermediate economics core courses (01:220:320, 321, and 322) is required, as these courses are prerequisites to Advanced Analytics for Economics, 01:220:424. Calculus II (01:640:152) is a prerequisite.
- 01:220:424 Machine Learning for Economics
04:547:321 Information Visualization
04:189:220 Data in Context
Minor in Data Science-Societal Impact Track:
This track will allow students to develop skills in human-centered aspects of data science.
- 04:547:321 Information Visualization
- 04:189:220 Data in Context
