The Bachelor of Arts in Data Science at Rutgers provides students with a strong foundation in data literacy, statistical inference, data management, and information and data management principles. The program has two tracks, Statistics and Societal Impact, each with unique requirements. The Statistics track includes calculus, computer science, and advanced statistics courses. The Societal Impact track includes courses in regression methods, computing, IT and informatics, and courses that are built to equip students to take on the role of a data scientist. The program prepares students for a career in data science with a focus on statistical analysis, data management, and information and data management principles.
Students must complete the Foundational courses and choose from one of the BA degree options, along with completing the SAS CORE requirements.
There is no precedence order except for the normal prerequisites for the courses. Information is subject to change, please consult advisors.
Foundational Courses
- 01:198:142/01:960:142 Data 101: Data Literacy
- 01:960:291 Statistical Inference for Data Science [The 01:960:291. Statistical Inference for Data Science requirement has been expanded to include any of the following: 01:960:212 Statistics II, OR 01:960:384 Intermediate Statistical Analysis (Formerly 960:380), OR 33:136:385 Statistical Methods in Business.]
- Data Management Courses (Choose one):
- 01:640:135/01:640:151 Calculus I
- 01:640:250 Introductory Linear Algebra
- 04:189:220 Data in Context*
*Update to Data in Context Course Code (30.3.2026)
With the release of the Schedule of Classes, Data in Context is now officially listed under the course code 04:547:225. If you previously completed this requirement under the former course code 04:189:220, you do NOT need to retake the course, your requirement has already been fulfilled. We also anticipate that this course will be offered in the summer. At this time, the course code for the summer offering has not yet been confirmed. Additional updates will be shared as soon as more information becomes available. If you have any questions, please contact the Data Science team at .
- New Data in Context Course Code (30.3.2026): With the release of the Schedule of Classes, Data in Context is now officially listed under the course code 04:547:225. If you previously completed this requirement under the former course code 04:189:220, you do NOT need to retake the course, your requirement has already been fulfilled. We also anticipate that this course will be offered in the summer. At this time, the course code for the summer offering has not yet been confirmed. Additional updates will be shared as soon as more information becomes available. If you have any questions, please contact the Data Science team at .
- Important Update: Data Science Major Declaration Change (as of Feb. 26, 2025)
We are excited to announce an important update regarding the prerequisites for declaring the Data Science major! Effective immediately, students can declare the Data Science Major after the successful completion of Data101 and Statistical Inference courses only. The Data Management course is no longer required to declare the Data Science major, but it is still a requirement for the DS degree completion. We understand that the previous requirement may have posed challenges for some students, and we believe this update will encourage more students to pursue their academic goals without unnecessary barriers. We are committed to supporting your educational journey and ensuring you have the best opportunities to succeed. - As of December 2023, the 01:960:291. Statistical Inference for Data Science requirement has been expanded to include any of the following: 01:960:212 Statistics II, OR 01:960:384 Intermediate Statistical Analysis (Formerly 960:380), OR 33:136:385 Statistical Methods in Business.
- The 01:198:336 Principles of Information and Data Management has been dropped for the Statistics, Economics, and Societal Impact tracks of the Data Science major but retained for the Computer Science track.
BA in Data Science - Statistics Track (Code: NB219TJ)
BA in Data Science - Societal Impact Track (Code: NB219IJ)
- 01:960:463 Regression Methods
- 01:960:486 Applied Statistical Learning
- 04:189:103 IT and Informatics
- 04:547:201 Information Technology Fundamentals
- 04:547:321 Information Visualization
- Choose one:
- 01:960:365 Bayesian Data Analysis OR
- 01:960:467 Applied Multivariate Analysis OR
- 01:960:490 Intro to Experimental Design OR
-
Approved Domain Course (from the table below)
IMPORTANT! Students cannot use the same Data Science course to fulfill two separate requirements. If a domain course is also listed as a foundational or a track course requirement, students must select a different approved domain course.
| 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 |
| SEBS (11) | BIOTECHNOLOGY (126) | 485 Functional Genomics | default, 198:310 |
| SOE (14) | ELECTRICAL AND COMPUTER ENGINEERING (332) | 443 Machine Learning for Engineers | default, 198:310 |
Curriculum Sheets
Declaration of Major
Requirements: To declare the Data Science Major, students must successfully complete these two foundational courses: Data 101 and Statistical Inference (or one of its equivalents).
School of Arts and Science (SAS) & School of Communication and Information (SC&I) students can add 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 Science (SEBS): Students must complete the SEBS form to request a non-SEBS second major: https://sebs.wufoo.com/forms/declaration-for-2nd-nonsebs-major/.
- School of Engineering (SOE): https://soe.rutgers.edu/academic-advising-and-policies/forms
- Rutgers Business School (RBS): https://forms.office.com/r/90QXFCd1UB