GEN 220: COMPUTATIONAL ANALYSIS OF HIGH THROUGHPUT BIOLOGICAL DATA
University of California, Riverside
3 Units, Lecture, 2 hours; discussion, 1 hour. Prerequisite(s): graduate students in a life sciences program or consent of the instructors; previous coursework in genetics/genomics, molecular biology, or cell biology. Enables those with no computer science background to handle high throughout biological data. Covers the Perl programming language; program design, implementation, and testing; relational databases; basic data structures and algorithms; and BioPerl. Includes skill building through analysis of real high throughput biological data. May be taken Satisfactory (S) or No Credit (NC) with consent of instructor and graduate advisor.
Average GPA: 3.82
Grade distribution records: 73 students across 4 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A+ | 20 | 27.4% |
| A | 27 | 37.0% |
| A- | 12 | 16.4% |
| B+ | 10 | 13.7% |
| B | 2 | 2.7% |
| S | 2 | 2.7% |
Based on 73 student grade records across 4 terms and 1 professor.
Instructors
- Jason Stajich 73 students, Average GPA 3.82