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.

These goals are achieved by students completing 6 courses plus a 1-credit mini-capstone. Students must maintain a G.P.A. of 2.0 in the courses applied to the minor. No courses with grade 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.

Updated form to track progress forthcoming, please check back.

To add the Data Science in MyMajor, please complete the forms according to your specific school affiliation. 
School of Arts and Sciences:
School of Environmental and Biological Sciences:
School of Engineering:
Rutgers Business School:


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. 

Foundation Courses (3 courses):

  • Data 101: Data Literacy (01:198:142/01:960:142)
  • Statistical Inference for Data Science (01:960:291)
  • Data Management: choose one of the following:
    • Data Management for data science (01:198:210), or
    • Data Management and wrangling with R (01:960:295), or
    • Fundamentals of data curation and management (04:547:221)

View Domain Courses

Track 1.

This track targets students with existing programming experience. It requires courses in statistics, data-centric programming, data management, and data analysis. Introduction to Discrete Structures II (CS206) is a prerequisite.

  • Regression Methods 01:960:463 (3) and
  • Choose from one of the following Machine Learning courses

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. Statistics II (01:960:212) is a prerequisite.

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. 

  • Advanced Analytics for Economics 01:220:424 (3) and
  • Choose from one of the following

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.

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) 485 Functional Genomics default, 198:310
SOE (14) ELECTRICAL AND COMPUTER ENGINEERING (332) 443 Machine Learning for Engineers default, 198:310