GEOG 588 - Analytical Approaches for Spatial Data Science
This is a sample syllabus.
This sample syllabus is a representative example of the information and materials included in this course. Information about course assignments, materials, and dates listed here is subject to change at any time. Definitive course details and materials will be available in the official course syllabus, in Canvas, when the course begins.
Overview
This course focuses on theoretical discussions in spatial data science, as well as on applying a range of spatial data science skills and tools to solve real-world problems and model geographic phenomena. This includes reading, writing, and working with novel types of geographic data, and making static and interactive maps. Students will read, discuss, and synthesize research articles and develop coding solutions for data science tasks through a series of lab exercises. Discussion forums will provide a platform for students to discuss the readings and address coding challenges and problems in a collaborative framework. Exercises using the tools introduced in the course will provide students an opportunity to troubleshoot and debug their code while creating reproducible programming/coding workflows. Students will apply concepts presented in the readings to compare existing spatial data science methods in order to select the appropriate methods to complete their term project.
Objectives
Students who excel in this course are able to:
- Evaluate and justify the selection of the appropriate methods to complete Spatial Data Science tasks.
- Develop key scientific programming skills that integrate state-of-the-art coding practices essential for spatial data science.
- Synthesize, analyze, and visualize multiple types of spatial data for real-world problem-solving.
- Create troubleshooting and debugging strategies (tools, mindsets, reproducible examples, resources) to address coding problems.
Required Materials
Typically, there are no required materials for this course. If this changes, students will find a definitive list in the course syllabus, in Canvas, when the course begins.
Prerequisites
GEOG 485: GIS Programming and Software Development or GEOG 487: Environmental Challenges in Spatial Data Science or equivalent experience.
Expectations
We have worked hard to make this the most effective and convenient educational experience possible. How much and how well you learn is dependent on your attitude, diligence, and willingness to ask for clarifications or help when you need them. We are here to help you succeed. Please keep up with the class schedule and take advantage of opportunities to communicate with us and with your fellow students. You can expect to spend an average of 12 – 15 hours per week on class work.
Major Assignments
- 1 Personal Introduction Video (2.5%)
- 7 Reading Discussions: (20%)
- 6 Technical Discussions: (12.5%)
- 5 Lab Assignments: (22.5%)
- 2 Reflective Practices: (5%)
- 1 Lightning Talk: (15%)
- 1 Final Project: (22.5%). This includes your topic selection and discussion, term-project proposal, peer review, rough draft, and final draft.
Course Schedule
| Week | Topic | Assignment |
|---|---|---|
| 1 | Introduction to Spatial Data Science |
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| 2 | Good Habits and Practices in Spatial Data Computing |
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| 3 | Data Visualization |
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| 4 | Working with Geographic Data |
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| 5 | Spatial Data Science Applications |
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| 6 | Working with US Census Data |
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| 7 | Common Methods in Spatial Data Science, Part 1 |
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| 8 | Common Methods in Spatial Data Science, Part 2 |
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| 9 | Final Project, Part 1 |
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| 10 | Final Project, Part 2 |
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