GEOG 215: Introduction to Spatial Data Science: Summer 2024 Session I
Abbreviated Syllabus Download full syllabus here
Course Description
This course introduces students to data science with a focus on spatial (geographic) data. We will explore various facets of data science practice including data collection, management and integration, descriptive modeling, exploratory spatial data analysis, and communication via visualization and mapping using real-world datasets.
Course Texts
- Required Texts: None
- Readings: Will be provided weekly on the course site.
Course Goals and Learning Outcomes
Students will:
- Use R for exploring spatial data.
- Develop spatially oriented research questions.
- Identify and utilize publicly available spatial data.
- Clean, organize, and analyze spatial data.
- Perform and interpret exploratory spatial data analysis techniques.
IDEAs in Action - Quantitative Reasoning
- Summarize and present quantitative data in various mathematical forms.
- Develop and compute representations of data using mathematical models.
- Make and evaluate assumptions in data analysis and understand result limitations.
- Apply concepts to make judgments and draw conclusions.
- Synthesize and present data to support a position.
Grade Breakdown
Class Participation (20%): The participation component of the course grade will be made up of in-class participation, as well as effort on in-class exercises.
Assignments (35%): There will be 5 assignments that will be completed throughout the course and will be graded on accuracy. These assignments are designed to have students independently implement skills learned through course material and in-class exercises.
Tests (20%): There will be two closed-book tests on course concepts during the semester.
Project Tasks (10%): Project tasks will allow students to iteratively develop their final projects throughout the course of the semester. Each task will allow students to complete a portion of their final project and allow the instructor to give feedback throughout the semester.
Final Project (10%): The final project will be a polished version of the completed project tasks that also implement any instructor feedback given on individual project tasks.
Final Presentation (5%): Students will give a 6–8-minute presentation on their final project during the course exam period.
Grade Scale
| Grade | Percentage |
|---|---|
| A | 93.5% + |
| A- | 89.5-93.4% |
| B+ | 86.5-89.4% |
| B | 82.5-86.4% |
| B- | 79.5-82.4% |
| C+ | 76.5-79.4% |
| C | 72.5-76.4% |
| C- | 69.5-72.4% |
| D+ | 66.5-69.4% |
| D | 59.5-66.4% |
| F | < 59.4% |