Introduction
The Data Science Designated Emphasis is a non-degree program housed in the Department of Statistics of Baskin Engineering at the University of California, Santa Cruz. The program aims to provide Ph.D. students from a wide swath of graduate degree programs across campus with the training required to apply state-of-the-art methods and tools from data wrangling, data visualization, statistical data analysis, machine learning, and artificial intelligence to their own research.
Requirements
Committee Composition and Departmental Approvals
Students must include one of the Data Science program faculty members that is not a member of their home department in their qualifying exam and dissertation committees. This faculty member is the “DE advisor” for purposes of the designated emphasis. This faculty member must be identified at the time the student applies to the program. A list of current program faculty can be found at the program faculty website.
The student must organize a preliminary meeting with the DE advisor, and agree on a plan for completion of the requirements. Once this plan has been designated, the student and the DE advisor must complete the Application for a Designated Emphasis in Data Science form. The completed application form should be signed by the student's home department advisor, the DE advisor, and the DE executive committee chair, and then filed with the Baskin Engineering Graduate Student Affairs Office (bsoe-ga@rt.ucsc.edu). This must be done before the student's advancement to candidacy.
Course Requirements
Required Courses
All students in the designated emphasis are required to complete 18 credits in mandatory courses covering the following topics:
Data Wrangling and Visualization
| STAT 266A
/CSE 266A
| Data Visualization and Statistical Programming in R | 3 |
| STAT 266C
/CSE 266C
| Introduction to Data Wrangling | 3 |
Statistical Data Analysis
| STAT 204 | Introduction to Statistical Data Analysis | 5 |
Machine Learning
If one of the required courses above is also required by the main program of affiliation of the student, it must be substituted with a valid elective course (see below).
Speaker Series
All students in the designated emphasis are required to register for one-quarter of STAT 280B, which consists of a weekly speaker series featuring external speakers on various topics related to data science methods and applications.
Elective Courses
In addition to completing all required courses, all students must complete at least one five-credit elective from the list below. Students may not double-count a course that is required for their main program as an elective for the Data Science DE.
Applied Mathematics
| AM 229 | Convex Optimization | 5 |
| AM 230 | Numerical Optimization | 5 |
| AM 250 | An Introduction to High Performance Computing | 5 |
Astonomy and Astrophysics
| ASTR 234 | Statistical Techniques in Astronomy | 5 |
Computational Media
| CMPM 243 | Social Computing Research: Design, Algorithms, and Incentives | 5 |
| CMPM 268 | Immersive Analytics | 5 |
Computer Science and Engineering
Economics
Electrical and Computer Engineering
| ECE 237 | Image Processing and Reconstruction | 5 |
| ECE 250 | Digital Signal Processing | 5 |
| ECE 253
/CSE 208
| Introduction to Information Theory | 5 |
| ECE 256 | Statistical Signal Processing | 5 |
Environmental Studies
| ENVS 215A | Geographic Information Systems and Environmental Applications | 5 |
| ENVS 215B | Intermediate Geographic Information Systems | 5 |
Ocean Sciences
| OCEA 260
/EART 260
| Introductory Data Analysis in the Ocean and Earth Sciences | 5 |
| OCEA 267 | Applied Environmental Time Series Analysis | 5 |
Psychology
| PSYC 204 | Quantitative Data Analysis | 5 |
| PSYC 205 | Categorical Data Analysis | 5 |
| PSYC 214A | Multivariate Techniques for Psychology | 5 |
| PSYC 214B | Advanced Multivariate Techniques for Psychology | 5 |
Statistics
| STAT 203 | Introduction to Probability Theory | 5 |
| STAT 205 | Introduction to Classical Statistical Learning | 5 |
| STAT 206 | Applied Bayesian Statistics | 5 |
| STAT 207 | Intermediate Bayesian Statistical Modeling | 5 |
| STAT 208 | Linear Statistical Models | 5 |
| STAT 209 | Generalized Linear Models | 5 |
| STAT 222 | Bayesian Nonparametric Methods | 5 |
| STAT 223 | Time Series Analysis | 5 |
| STAT 224 | Bayesian Survival Analysis and Clinical Design | 5 |
| STAT 225 | Multivariate Statistical Methods | 5 |
| STAT 226 | Spatial Statistics | 5 |
| STAT 227 | Statistical Learning and High Dimensional Data Analysis | 5 |
| STAT 229 | Advanced Bayesian Computation | 5 |
Students might need to take additional electives to substitute for required courses if any of them is also a required course for their main program of affiliation. In addition, to emphasize the interdisciplinary nature of the program, students completing a graduate program housed in the Statistics or Computer Science and Engineering departments must take the elective course outside their home department (however, cross-listed courses that include their home department are acceptable electives for these students). Students in graduate programs housed in other departments are free to take any elective in the list, except for courses that are required by their main program of affiliation (as outlined above).
In rare circumstances, courses not included in the list above could be considered electives with the approval of the Executive Committee.