Teaching materials for the Applied Data Analysis (ADA) activity strand at the Digital Humanities Oxford Summer School. The authors of these materials are Giovanni Colavizza and Matteo Romanello.
Convenors:
- Matteo Romanello (University of Lausanne)
- Nilo Pedrazzini (The Alan Turing Institute/University of Oxford)
| Day.session | Topic | Materials |
|---|---|---|
| 1.1 | Introduction | slides |
| 1.2 | Data carpentry | slides + notebook |
| 1.3 | Python refresher | notebook |
| 1.4 | The Python data analysis stack, pt. I | notebook |
| 2.1 | Tidy data pt. I-II | slides + notebook |
| 2.2 | Tidy data pt. III | notebook |
| 2.3 | Data transformation with Pandas | notebook |
| 3.1 | Applied data analysis, pt. I: The basics | slides + notebook |
| 3.2 | Applied data analysis, pt. II: Advanced | notebook |
| 4.1 | Applied data analysis, pt. III: Visualization | slides + notebook |
| 4.2 | Applied data analysis, pt. IV: Mapping | From Melanie Walsh's ICAP |
| 5.1 | Social network analysis | From Melanie Walsh's ICAP |
| 5.2 | Communicating results and epilogue | slides |
- 2022: Giovanni and Matteo Romanello (archived teaching materials)
- 2020-2021: hiatus due to global pandemic
- 2019: Giovanni Colavizza and Matteo Romanello (archived teaching materials)
Inspiration for ADA was given by the following courses/teachers:
- [ICAP] Melanie Walsh's Introduction to Cultural Analytics & Python https://melaniewalsh.github.io/Intro-Cultural-Analytics/welcome.html
- Ryan Cordell's Humanities Data Analysis http://s17hda.ryancordell.org/schedule/
- Massimo Franceschet's Dear Data Science http://users.dimi.uniud.it/~massimo.franceschet/ds/syllabus/syllabus.html
- Bob West's Applied Data Analysis (ADA) https://dlab.epfl.ch/teaching/fall2018/cs401
- Garrett Grolemund and Hadley Wickham, R for data science, https://r4ds.had.co.nz/index.html
- Julia Silge and David Robinson, Text mining with R: A tidy approach, https://www.tidytextmining.com
- Folgert Karsdorp, Mike Kestemont and Allen Riddell, Humanities Data Analysis: Case Studies with Python, https://www.humanitiesdataanalysis.org