CS 235: DATA MINING TECHNIQUES
University of California, Riverside
4 Units, Lecture, 3 hours; term paper, 1.5 hours; activity, 1.5 hours. Prerequisite(s): CS 141; CS 170 is recommended. CS 235 online section: enrollment in the online Master of Science in Engineering program; graduate standing. Provides a broad background in the design and use of data mining algorithms and tools. Includes clustering, classification, association rules mining, time series clustering, and Web mining. May be taken Satisfactory (S) or No Credit (NC) with consent of instructor and graduate advisor.
Average GPA: 3.65
Grade distribution records: 1,020 students across 13 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A+ | 136 | 13.3% |
| A | 387 | 37.9% |
| A- | 227 | 22.3% |
| B+ | 127 | 12.5% |
| B | 63 | 6.2% |
| B- | 36 | 3.5% |
| C+ | 16 | 1.6% |
| C | 4 | 0.4% |
| C- | 7 | 0.7% |
| D+ | 4 | 0.4% |
| D | 1 | 0.1% |
| F | 5 | 0.5% |
| NP | 2 | 0.2% |
| S | 5 | 0.5% |
Based on 1,020 student grade records across 13 terms and 2 professors.
Instructors
- Evangelos Papalexakis 883 students, Average GPA 3.63
- Mariam Salloum 137 students, Average GPA 3.77