PSTAT 135: BIG DATA ANALYTICS
University of California, Santa Barbara
Basics in distributed data storage, retrieval, processing and cloud computi ng. Overview of methods for analyzing big data from both high dimensional s tatistics and machine learning - topics chosen from penalized regression, c lassification/clustering, dimension reduction, random projections, kernel m ethods, network clustering, graph analytics, supervised and unsupervised le arning among others.
Average GPA: 3.76
Grade distribution records: 405 students across 7 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A+ | 5 | 1.2% |
| A | 277 | 68.4% |
| A- | 51 | 12.6% |
| B+ | 23 | 5.7% |
| B | 29 | 7.2% |
| B- | 6 | 1.5% |
| C+ | 3 | 0.7% |
| C | 8 | 2.0% |
| C- | 2 | 0.5% |
| F | 1 | 0.2% |
Based on 405 student grade records across 7 terms and 3 professors.
Instructors
- Oh Sang-Yun 289 students, Average GPA 3.75
- Tashman A P 92 students, Average GPA 3.80
- Kloke J 24 students, Average GPA 3.75