CS 133: Introduction to Data Visualization
San Jose State University
Topics in data analysis and visualization. Covers tools and techniques to efficiently analyze and visualize large volumes of data in meaningful ways to help solve complex problems in fields such as life sciences, business, and social sciences. Prerequisite(s): CS 146 with a grade of C- or better, or [CS 22B and graduate standing]. Computer Science, Data Science, Computer Science and Linguistics, or Software Engineering majors only.
Average GPA: 3.46
Grade distribution records: 399 students across 7 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A+ | 30 | 7.5% |
| A | 167 | 41.9% |
| A- | 42 | 10.5% |
| B+ | 26 | 6.5% |
| B | 85 | 21.3% |
| B- | 9 | 2.3% |
| C+ | 6 | 1.5% |
| C | 25 | 6.3% |
| C- | 2 | 0.5% |
| D | 4 | 1.0% |
| F | 3 | 0.8% |
Based on 399 student grade records across 7 terms and 7 professors.
Instructors
- Jessica Vy Thuy Huynh-Westfall 171 students, Average GPA 3.41
- Hannah E Debaets 54 students, Average GPA 3.70
- Mei-Chong Wendy Lee 48 students, Average GPA 3.43
- Jelena Gligorijevic 43 students, Average GPA 3.49
- Jelena Segan 36 students, Average GPA 3.78
- Daniel Isaac Quintana 34 students, Average GPA 3.02
- Mohammad Parsa Hosseini 13 students, Average GPA 3.33