"Teaching the Teacher" resources for colleagues that want to get started using computational methods in their teaching (using Python)
This repository contains materials for colleagues who are new to teaching computational methods but want to do so in the future. In particular, this holds true for the following courses, but the tips and resources also apply to future yet-to-be-developed courses:
| Course name | Resource link 1 |
|---|---|
| Gesis Course Introduction to Machine Learning | https://github.com/annekroon/gesis-machine-learning |
| Big Data and Automated Content Analysis | https://github.com/uvacw/teaching-bdaca |
| Computational Communication Science I | https://github.com/uva-cw-ccs1/2223s2/ |
| Computational Communication Science II | https://github.com/uva-cw-ccs2/2223s2/ |
| Data Journalism | https://github.com/uvacw/datajournalism |
| Digital Analytics* | https://github.com/uva-cw-digitalanalytics/2021s2 |
*Ask Joanna or Theo for access
You need to have a working Python environment and you need to be able to install Python packages on your system. There are several ways of achieving this, and it is important to note that not all of your students may have the same type of environment. In particular, one can either opt for the so-called Anaconda distribution or a native Python installation. There are pro's and con's for both approaches. Currently, students in Data Journalism as well as in Digital Analytics are advised to install Anaconda; students in Big Data and Automated Content Analysis are explicitly given the choise. Please read the our Installation Guide for detailed instructions.
As a pilot, we are holding a five-day course in which we combine
- teaching the necessary Python skills
- teaching how to teach these skills
- exercising and reflecting on best teaching practices.
A list of additional resources that could be of interest:
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A 5-day workshop by Anne and Damian on Machine Learning in Python (for social scientists with no or minimal previous Python knowledge): https://github.com/annekroon/gesis-ml-learning/
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"The new book" (forthcoming open-access on https://cssbook.net and in print with Wiley): Van Atteveldt, W., Trilling, D., Arcila, C. (in press): Computational Analysis of Communication: A practical introduction to the analysis of texts, networks, and images with code examples in Python and R
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"The old book" (the book used between 2015 and 2020 in the Big Data courses). Less focus on Pandas than in more modern approaches, slightly outdated coding style in some examples, and less depth than the "new" book. The Twitter API chapter is outdated and sentiment anaysis as described in Chapter 6 should not be tought like this any more. Apart from that, it still can be a good resource to get started and/or to look things up. Trilling, D. (2020): Doing Computational Social Science with Python: An Introduction. Version 1.3.2. https://github.com/damian0604/bdaca/blob/master/book/bd-aca_book.pdf