Weekly Course Content
The course will be broken up into 13 weeks of content,and each week will cover that week’s unique topic. Each week will have a recommended set of readings that will help reinforce the content. As we progress through the semester, you will be able to access the weekly content on the sidebar of this page. For each week, you’ll find information about readings and topics, as well as links to the lecture slides.
Lecture slides
The lecture slides are Jupyter notebook files, a mix of executable Python cells, text, and images. Students can access the slide materials using Jupyter notebooks.
The Jupyter notebook files for each week are stored in a repository that is available on Github. On the content page for each week, you will see a link to this repository. Once you navigate to the repository on GitHub, you can download the contents of the repository to your computer and work locally with the notebook files on your laptop. To download the repository contents, look for the green “Code” button and select “Download ZIP”.
Lecture topics
Sep 5/ Week 2: Data Visualization Fundamentals; HW #1 assigned
Sep 12/ Week 3: Data visualization (Week 2, con’t). More Interactive Data Viz.
Sep 19/ Week 4:
- Continue on Interactive Data Viz
- Introduction to Vector Data & GeoPandas; Geospatial Analysis & Mapping
- HW #1 due
Sep 26 / Week 5:
- Continue Vector Data & GeoPandas;
- Choropleth and hexagon maps; interactive maps with folium (lecture 4)
- HW #2 assigned
Oct 3/ no classes
Oct 10/ Week 6:
Oct 17/ Week 7:
- Raster Analytics
- HW #3 assigned
Oct 24/ Week 8:
Oct 31/ Week 9:
- API, Text Analytics
- HW #3 due
Nov 7/ Week 10:
- Census API
- Visualizing Large Datasets
- HW #4 assigned
Nov 14/ Week 11:
Nov 21/ Week 12:
- predictive modeling
- HW #4 due
- HW #5 assigned
- HW #6 assigned
Nov 26/ Week 13:
Week 14: More on dashboards & large raster analytics