EDS 223: GEOSPATIAL ANALYSIS
University of California, Santa Barbara
Introduces the spatial modeling and analytic techniques of geographic infor mation science to data science students. The emphasis is on deep understand ing of spatial data models and the analytic operations they enable. Recogni zing remotely sensed data as a key data type within environmental data scie nce, this course also introduces fundamental concepts and applications of r emote sensing. In addition to this theoretical background, students become familiar with libraries, packages, and APIs that support spatial analysis i n R.
Average GPA: 3.85
Grade distribution records: 167 students across 5 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A+ | 66 | 39.5% |
| A | 56 | 33.5% |
| A- | 29 | 17.4% |
| B+ | 10 | 6.0% |
| B | 1 | 0.6% |
| B- | 2 | 1.2% |
| C+ | 3 | 1.8% |
Based on 167 student grade records across 5 terms and 3 professors.
Instructors
- Oliver R Y 109 students, Average GPA 3.83
- Adams A R 32 students, Average GPA 3.81
- Frew J E 26 students, Average GPA 4.00