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.
Updated form forthcoming, please check back. 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.
| # | Number | Title | Prerequisites |
|---|---|---|---|
| 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, or | 01:198:142/ 01:198:111 |
| 01:960:295 | Data management and wrangling with R, or | 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 (select one from list below) | Departmental prerequisites |
| IV+ | 01:198:310 | 1 credit Data Science Capstone Project - default option, or | I, II, and III |
| 01:220:323 | 1 credit capstone Data Science and Econometrics | I, II, and III |
IV. 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 |
