Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
InteractiveResource . 2026
License: CC BY
Data sources: ZENODO
ZENODO
InteractiveResource . 2026
License: CC BY
Data sources: Datacite
ZENODO
InteractiveResource . 2026
License: CC BY
Data sources: Datacite
versions View all 2 versions
addClaim

Introduction to Data Analysis Basics with Python for the Humanities

Authors: Sarkar Farshi, Golnaz;

Introduction to Data Analysis Basics with Python for the Humanities

Abstract

The main goal of this lesson is to familiarize humanities students and scholars with quantitative research methods and data analysis basics. To make the concepts and methods of digital data analysis more concrete and demonstrate their use case, this lesson also introduces the learners to the programming language Python. Using Python on different datasets consisting of tables, text, and network data, learners explore the possibilities of quantitative data analysis for the purpose of humanities research. 

Please cite this lesson using the information in this file when you refer to it in publications, and/or if you re-use, adapt, or expand on the content in your own training material. type: lessonauthors: - given-names: Golnaz - family-names: Sarkar Farshilicense: CC-BY-4.0DOI: https://doi.org/10.5281/zenodo.18837668

Keywords

python, Data analysis, Digital humanities

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Related to Research communities