Integrated Data Science Certificate
The following requirements are in addition to, or further elaborate upon, those requirements outlined in The Graduate School Policy Guide.
Certificate
Total Units Required: 5
Course Requirements:
To complete the Integrated Data Science (IDS) Certificate requirements, students will take five courses including at least one course from group A, at least two courses from group B, at least one course from group C, and a fifth course from any group. The courses currently available in each curriculum group are described below, developed in connection to the NSF IDEAS traineeship.
Group A. Data Challenges in Domain Disciplines
| Course | Title |
|---|---|
| DATA_SCI 401-1 | Data-Driven Research in Physics, Geophysics, and Astronomy |
| DATA_SCI 401-2 | Data-Driven Research in Physics, Geophysics, and Astronomy |
| BIOL_SCI 354-0 | Systems Biology |
Group B. Core Data Analytics
| Course | Title |
|---|---|
| DATA_SCI 421-0 | Integrated Data Analytics I |
| DATA_SCI 422-0/EARTH 353-0 | Mathematical Inverse Methods in Earth and Environmental Sciences |
| DATA_SCI 423-0 | Machine Learning: Foundations, Applications, and Algorithms |
Group C. Electives in Data Analytics
| Course | Title |
|---|---|
| CHEM_ENG 379-0 | Computational Biology: Analysis and Design of Living Systems |
| COMP_ENG 495-0 | Special Topics in Computer Engineering |
| COMP_ENG 510-0 | Seminar |
| COMP_SCI 336-0 | Design & Analysis of Algorithms |
| COMP_SCI 496-0 | Special Topics in Computer Science |
| EARTH 327-0 | Geophysical Time Series Analysis |
| ELEC_ENG 420-0 | Digital Image Processing |
| ELEC_ENG 435-0 | Deep Learning: Foundations, Applications, and Algorithms |
| ELEC_ENG 495-0 | Special Topics in Electrical Engineering |
| ELEC_ENG 433-0 | Statistical Pattern Recognition |
| ELEC_ENG 473-0 | Deep Reinforcement Learning |
| ES_APPM 421-1 | Models in Applied Mathematics |
| ES_APPM 448-0 | Numerical Methods for Random Processes |
| IEMS 304-0 | Statistical Learning for Data Analysis |
| MAT_SCI 458-0 | Atomic Scale Computational Materials Science |
| STAT 320-1 | Statistical Theory & Methods 1 |
| STAT 350-0 | Regression Analysis |
| STAT 365-0 | Introduction to the Analysis of Financial Data |
| STAT 457-0 | Applied Bayesian Inference |
| STAT 461-0 | Advanced Topics in Statistics |
Please note Machine Learning (COMP_SCI 349-0) will still count as an elective if taken before spring 2020. Data Management and Information Processing (COMP_SCI 317-0) is no longer offered; however it will still count as an elective if taken before Fall 2019.