Course Detail
Units:
3.0
Course Components:
Laboratory
Lecture
Enrollment Information
Enrollment Requirement:
Prerequisites: 'C' or better in GEOG 3160 AND GEOG 4140
Description
Graduate students should enroll in GEOG 6160 and will be held to higher standards and/or more work. This course is designed to build from GEOG 3160 (Introduction to Spatial Data Science) by covering more advanced topics. These will include greater detail on the algorithms used in machine learning, the use of hyperparameter tuning, deep learning and the use of dashboards to communicate results. Most topics will be introduced as a case study, allowing discussion of the methods, results and choices taken in developing the analysis. Topic will include a mix of video lectures and in-class demonstrations, followed by a hands-on lab where students can walk through the analysis using Python or R. In addition, students will be required to carry out a short spatial data science project and present the results to the class.