Rutgers University School of Arts and Sciences (New Brunswick) 33 courses
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 01:830:202 Data Science & Addiction Research Methods 1 (DSARM 1) 01:830:203 Data Science & Addiction Research Methods II (DSARM II) 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...
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
| Code | Course Title | Credits |
|---|---|---|
| 01:198:461 | Machine Learning | 4 |
| 01:198:462 | Introduction to Deep Learning | 4 |
| 01:960:463 | Regression Methods | 3 |
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 One of the following: 04:547:321 Information Visualization 04:189:220 Data in context 01:960:463 Regression Methods
| Code | Course Title | Credits |
|---|---|---|
| 01:960:486 | Applied Statistical Learning | 3 |
| 04:547:321 | Information Visualization | 3 |
| 04:189:220 | Data in Context | 4 |
| 01:960:463 | Regression Methods | 3 |
Minor in Data Science-Economics Track
01:220:424 Machine Learning for Economics One of the following: 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. The following two courses are required for the Societal Impact track (R3) but CANNOT be counted as domain courses. Students pursuing this track must choose a different course to fulfill the domain course requirement (R2). 04:547:321 Information Visualization 04:189:220 Data in Context
| Code | Course Title | Credits |
|---|---|---|
| 04:547:321 | Information Visualization | 3 |
| 04:189:220 | Data in Context | 4 |
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