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.
Students must take 4 courses (14 to 16 credits) which include a 1-credit 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.
Certificate Completion Form
Congratulations on completing the Data Science Certificate! We are thrilled to recognize your hard work and dedication. To finalize the certification process, please complete this form which is the final step in confirming your accomplishment. We celebrate your success and look forward to acknowledging your journey in data science (note: please turn the mobile-friendly feature off when accessing the form below on your phone):
Please note that The Certificate Completion Forms will only be processed in January and May. January graduates can submit their forms starting in December, while May graduates can submit them starting in April.
The processing of the Undergraduate Certificate Completion form is contingent upon the student fulfilling all requirements, namely achieving a grade of C or higher. Kindly submit the form only after completing the requirements and grades have been released.
Course Requirements
Students must take 4 courses (14 to 16 credits) in addition to a 1-credit recitation (mini-capstone) for skills demonstration to successfully complete 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 a grade of D can be counted toward the certificate.
Please note:
The remaining classes have to be completed BEFORE the Capstone project. No class (except the domain course) can be taken in parallel with the Capstone.
For remaining classes - they may be taken in parallel if prereqs are satisfied and/or with permission of program directors.
- Data 101: Data Literacy (01:198:142/01:960:142) must be taken, (no waivers)
- Statistical Inference for Data Science (01:960:291)
- [ As of Dec. 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, OR 33:136:385 Statistical Methods in Business.]
- 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)
| 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 |
- 1 credit Data Science Capstone Project (01:198:310)
- 1 credit Data Science and Econometrics (01:220:323)
View Data Science Certificate Pathway
| # | Number | Title | Prerequisites |
|---|---|---|---|
| I | 01:198:142/ 01:960:142 | Data 101: Data Literacy | Some math knowledge |
| II | 01:960:291 or 01:960:212 | Statistical inference for data science or Statistics II | 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 |
