These are materials for the course Coding for Humanities taught at the University of Groningen. The 7-week course introduces Digital Humanities graduate students to Python programming without assuming any technical background. The course covers applications of text mining and exploratory data analysis. The programming skills involve both the fundamentals of Python as well as the use of common data science libraries (NLTK, Pandas, Seaborn). Everything is done in Jupyter Notebooks, easily installed as part of the Anaconda Distribution. Use of the command line is not needed.
This course was developed by Andreas van Cranenburgh and Leonie Bosveld-de Smet.
https://github.com/andreasvc/codingforhumanities/releases
- http://www.karsdorp.io/python-course/ (Notebook ch. 1-3)
- https://github.com/jivoi/junky/tree/master/python_simple_ex (exercises week 2-4)
- https://mlcourse.ai/ (topic 1)
- https://docs.python.org/3/tutorial/
- https://github.com/vierth/humanitiesTutorial
- http://swcarpentry.github.io/python-novice-gapminder/
- https://github.com/mchesterkadwell/intro-to-text-mining-with-python
These instructions are suitable if you have never installed Jupyter Notebooks or Python on your own computer before.
Install Anaconda (Python 3.8).
Pick the version appropriate for your operating system (Windows, Mac, Linux). Make sure you choose 'Python 3.8' (not 'Python 2.7'). Choose the "64-bit graphical installer".
Once it has installed, open Anaconda Navigator.
Download one of the notebooks from the course website. Save it to a directory where you will keep your work for this course.
In simple terms, an environment is like an isolated box in which to run a notebook safe from interference by other notebooks. Anaconda provides one default environment, called 'root', in which to get up and running quickly.
In Anaconda Navigator > Home there is a card for Jupyter Notebook (not JupyterLab). Click on the 'Launch' button.
This opens a web page at http://localhost:8888/tree that shows your whole file system.
Navigate to where you downloaded the notebooks, and click on one to run it in the usual way.
Credit: these instructions are based on https://github.com/mchesterkadwell/intro-to-text-mining-with-python