Undergraduate Certificate

Open to ALL students! The goals of the Data Science Certificate program are achieved by completing three foundational courses followed by an advanced domain-specific course and a mini-capstone course.

Please use this form to track your progress. Upon completion, please submit completed form to datascience@sas.rutgers.edu

Students must take 4 courses (14 to 16 credits) which include a 1-credit recitation (mini-capstone) for skills demonstration for the successful completion of the Certificate in Data Science.

Students must maintain a G.P.A. of 2.0 in the courses applied to the certificate. No courses with grade D can be counted toward the certificate.

Please note:

Data101 MUST be taken (no waivers).

Remaining classes have to be completed BEFORE Capstone project. No class (except domain course) can be taken in parallel with Capstone.

For remaining classes - they may be taken in parallel if prereqs are satisfied and/or with permission of program directors.

 

#NumberTitlePrerequisites
I 01:198:142/ 01:960:142 Data 101: Data Literacy Some math knowledge
II 01:960:291 Statistical inference for data science 01:198:142/ 01:960:142
III 01:198:210 Data management for data science 01:198:142/ 01:198:111
01:960:295 Data management and wrangling with R 01:198:142/ 01:960:142
04:547:221 Fundamentals of data curation & management 01:198:142/ 01:960:142
IV xx:xxx:xxx Domain Course Departmental prerequisites
IV+ 01:198:310 1 credit Data Science Capstone Project - default option I, II, and III
01:220:323 1 credit capstone Data Science and Econometrics I, II, and III

 

IV. Domain classes (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 Computing and Graphics in Applied Statistics 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