The university library will be closed from December 20, 2025 to January 11, 2026. From January 12, 2026, regular opening hours will apply again. Exception: The main medical library and the central library will be open again from January 5, 2026. Further information
  • An Open Educational Resource t...

Result: An Open Educational Resource to Introduce Data Analysis in Python for the Humanities

Title:
An Open Educational Resource to Introduce Data Analysis in Python for the Humanities
Language:
English
Authors:
Blanke, Tobias, Colavizza, Giovanni, van Hout, Zarah
Source:
Education for Information. 2023 39(2):105-119.
Availability:
IOS Press. Nieuwe Hemweg 6B, Amsterdam, 1013 BG, The Netherlands. Tel: +31-20-688-3355; Fax: +31-20-687-0039; e-mail: info@iospress.nl; Web site: http://www.iospress.nl
Peer Reviewed:
Y
Page Count:
15
Publication Date:
2023
Document Type:
Academic journal Journal Articles<br />Reports - Descriptive
Descriptors:
Open Educational Resources, Data Analysis, Programming Languages, Humanities, Computation, Humanities Instruction, Social Science Research, Artificial Intelligence, Algorithms, Communities of Practice, Networks, Visualization
DOI:
10.3233/EFI-230020
ISSN:
0167-8329
1875-8649
Entry Date:
2023
Accession Number:
EJ1381117
Database:
ERIC

Further information

The article presents an open educational resource (OER) to introduce humanities students to data analysis with Python. The article beings with positioning the OER within wider pedagogical debates in the digital humanities. The OER is built from our research encounters and committed to computational thinking rather than technicalities. Furthermore, we argue that students best learn with the 'whole game' methodology. Learners need to be exposed to meaningful activities as soon and as far as possible. The article presents two examples that implement our approach. The first introduces Python as a data analysis language to students of the humanities. It is different because it concentrates on the principles of the computational thinking behind data analysis rather than programming details. The second example takes the students into the world of machine learning and the whole game of social and cultural research with it. Students learn useful skills such as web scraping but will also run their own machine learning algorithms to follow concrete research questions.

As Provided