Geospatial Data Science in Python
Syllabus
Schedule
Section 401
Section 402
Content
Assignments
Overview
Section 401
Section 402
Resources
GitHub
Canvas
Ed Discussion
11. Clustering Analysis in Python
Weekly Course Content
1. Exploratory Data Science in Python
2. Data Visualization Fundamentals
3. More Interactive Data Viz, Intro to Vector Data & GeoPandas
4. Geospatial Analysis & Mapping
5. More Geospatial Analysis: Street Networks and Raster Data
6. Web Scraping
7. Working with APIs
8. Analyzing and Visualizing Large Datasets
9. From Notebooks to the Web: Part 1
10. From Notebooks to the Web: Part 2
11. Clustering Analysis in Python
12. Predictive Modeling with Scikit-Learn, Part 1
13. Predictive Modeling with Scikit-Learn, Part 2
14. Advanced Raster Analysis
On this page
Recommended Readings/Videos
References
Week 11: Clustering in Python
Content for lectures 11A and 11B
View materials:
MUSA-550-Fall-2023/week-11
HTML slides:
Lecture 11A
Lecture 11B
Executable slides:
Lecture 11A
Lecture 11B
Recommended Readings/Videos
Andrew Ng’s K-means Clustering Lecture
Using DBSCAN to identify neighborhoods
Discriminatory ads on Facebook
NY Times exposé on the dangers of location data
References
scikit-learn
Documentation
Clustering home page
K-Means
DBSCAN
Clustering comparison chart
Gapminder Data
Airbnb
Tom Slee
Inside Airbnb
How Airbnb’s Data Hid the Facts in New York City
10. From Notebooks to the Web: Part 2
12. Predictive Modeling with Scikit-Learn, Part 1
