You can run all the notebooks for this course using Colab (link above). If you prefer to work locally, I recommend using Jupyter notebooks which are easily accessible using the Anaconda interface.
| Week | Topic | Materials |
|---|---|---|
| 1 | No-class (preparation) | notebook |
| 2 | Introduction | slides + notebook |
| 3 | Tidy data I | slides + notebook |
| 4 | Tidy data II | slides + notebooks 1, 2 |
| 5 | Exploring data | slides + notebooks 1, 2 |
| 6 | Enough data? | slides + notebook |
| 7 | Visualizing data | slides + notebook |
| Extra | Modelling data | notebook |
We will mostly use these datasets, from the Applied Data Analysis course.
See the assignments folder.
A good companion for this course is John Canning, Statistics for the Humanities, 2014. Also recommended are Melanie Walsh, Introduction to Cultural Analytics & Python, 2021 and Karsdorp, Kestemont, Riddell, Humanities Data Analysis: Case Studies with Python, 2021.
Everything in this repository which is not already attributed to someone else is released under CC BY 4.0.
The contents of this course are in part based on the following courses:
- Applied Data Analysis (with Matteo Romanello).
- Coding the Humanities.