The Minor in Data Science is open to ALL students! The Data Science Minor is designed to equip students to become proficient in the principles of computation, statistical inference, and data management and their applications in a specific domain/field.
Its interdisciplinary and visionary curricula allow flexibility and accessibility for any student who wants to enhance their academic competency and employability in data-informed careers.
The minor consists of 6 courses plus a 1-credit capstone. After completing the three data science foundational courses the minor offers a choice of four tracks to allow students to differentiate and complement their career pathway, thus accommodating a broad range of student goals and backgrounds.
Students must maintain a GPA of 2.0 in the courses applied to the minor. No courses with a grade of D can be counted toward the minor.
Students are advised to check with their major department for any restrictions on counting courses for both the Data Science minor and their major.
Course Requirements
Students are required to complete six courses and a mini capstone. Students must maintain a GPA of 2.0 in the courses applied to the minor. No courses with a D can be counted toward the minor.
- Data 101: Data Literacy (01:198:142/01:960:142) must be taken, (no waivers)
- Statistical Inference for Data Science (01:960:291)
- {The 01:960:291. Statistical Inference for Data Science requirement has been expanded to include any of the following: Statistics II (01:960:212), OR 01:960:384 Intermediate Statistical Analysis (Formerly 960:380), OR 33:136:385 Statistical Methods in Business.}
- Data Management: choose one of the following:
Domain classes (select one and check department for prerequisites)
| RU-NB School | Department | Course # Title | Capstone |
|---|---|---|---|
| SAS (01) | COMPUTER SCIENCE (198) | 439 Introduction to Data Science | default, 198:310 |
| SAS (01) | ECONOMICS (220) | 322 Econometrics | 01:220:323, to be taken after, not concurrent with 322 |
| SAS (01) | ENGLISH (359) | 207 Data and Culture | default, 198:310 |
| SAS (01) | GENETICS (447) | 303 Computational Genetics for Big Data | default, 198:310 |
| SAS (01) | GEOGRAPHY (450) | 320 Spatial Data Analysis | default, 198:310 |
| SAS (01) | GEOGRAPHY (450) | 321 Geographic Information Systems | default, 198:310 |
| SAS (01) | GEOGRAPHY (450) | 330 Geographical Research Methods | default, 198:310 |
| SAS (01) | PHYSICS (750) | 345 Computational Astrophysics | default, 198:310 |
| SAS (01) | POLITICAL SCIENCE (790) | 391 Data Science for Political Science | default, 198:310 |
| SAS (01) | SOCIOLOGY (920) | 360 Computational Social Science | default, 198:310 |
| SAS (01) | STATISTICS (960) | 365 Bayesian Data Analysis | default, 198:310 |
| SAS (01) | STATISTICS (960) | 463 Regression Methods | default, 198:310 |
| SAS (01) | STATISTICS (960) | 486 Applied Statistical Learning | default, 198:310 |
| SCI (04) | DIGITAL COMMUNICATION, INFORMATION, AND MEDIA (189) | 220 Data in Context | default, 198:310 |
| SCI (04) | INFORMATION TECHNOLOGY AND INFORMATICS (547) | 321 Information Visualization |
default, 198:310 |
| SEBS (11) | BIOTECHNOLOGY (126) | 486 Functional Genomics | default, 198:310 |
| SOE (14) | ELECTRICAL AND COMPUTER ENGINEERING (332) | 443 Machine Learning for Engineers | default, 198:310 |
Capstone Courses (1 course):
Choose from one of the following four tracks.
Track 1.
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.
- Regression Methods 01:960:463 (3) and
- Choose from one of the following Machine Learning courses
- Machine Learning Principles 01:198:461 or
- Introduction to Deep Learning 01:198:462
Track 2.
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.
- Applied Statistical Learning 01:960:486 (3) and
- Choose from one of the following
- Information Visualization 04:547:321 (3) or
- Data in context 04:189:220 (3) or
- Regression Methods 01:960:463 (3)
Track 3.
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.
- Machine Learning for Economics 01:220:424 (3) and
- Choose from one of the following
- Information Visualization 04:547:321 (3) or
- Data in context 04:189:220 (3)
Track 4.
This track will allow students to develop skills in human-centered aspects of data science. Introduction to computer concepts (04:547:201) is a prerequisite for the following courses.
- Information Visualization 04:547:321 (3) and
- Data in context 04:189:220 (3)
View Data Science Minor Pathway
Data Science Minor Declaration
Requirements: 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.
School of Arts and Science (SAS) students can add the Data Science to MyMajor https://mymajor.sas.rutgers.edu
Other Rutgers- New Brunswick Students (non-SAS students): For students in other schools, it is essential to complete the required forms corresponding to your school to include the Data Science major in your academic journey officially. Here are the links to the respective forms:
- School of Environmental and Biological Sciences: https://mymajor.sebs.rutgers.edu
- School of Engineering: https://soe.rutgers.edu/academic-advising-and-policies/academic-policies/minors-second-majors-and-dual-degrees.
- Rutgers Business School (RBS): https://forms.office.com/r/ZtZf0BGr3E
