Computational literacy
  • Computational literacy for the humanities and social sciences
  • Three approaches to computational methods
  • History of humanities computing
  • Data processing: fundamental concepts of programming for humanists and social scientists
  • Data processing: regular expressions
  • Data analysis: fundamental concepts of statistics
    • Understanding and describing groups
    • What is average?
    • Uncertainty in describing groups
    • What is a sensible group to describe?
    • Comparing groups
    • Understanding relationships
  • Digging into a method: topic modeling
  • Final project
  • Where to continue?
  • Course instances
    • Helsinki fall 2021
    • Helsinki fall 2020
    • Helsinki fall 2019
    • Helsinki fall 2018
  • Holding area for unfinished content
    • Data
    • Easy tools for acquiring, processing and exploring data
    • Computational data analysis method literacy
    • Open, reproducible research and publishing
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Last updated 5 months ago

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Here I'll gather some relevant links to further resources. I think these are good, but they're also somewhat of a random selection.

  • Term glossaries:

  • General course lists:

  • Tools and data processing:

  • The programming humanist:

    • , the best introduction to programming for humanists that I could find

    • , lessons and tutorials for doing various DH things

    • , a nicely built general, interactive introduction to programming

  • On visualisation:

    • (a good introductory book on choosing suitable visualisations for highlighting different aspects in data, and avoiding pitfalls in tuning them)

    • (starts with a good chapter organised around general principles, but then continues with very down to earth practical instructions on how to plot stuff using ggplot2 in R. This is very useful, but doesn't cover the general view on different graph types and their usefulness. Thus, a very nice complement to the book before)

  • On statistics and computational data science:

    • , a very good and clear resource introducing both statistical concepts, as well as how to apply them in practice in R and Tidyverse. An excellent follow-up to the introduction in this course.

    • , an excellent alternative simple introduction to core statistical concepts

    • at the University of Helsinki

    • , an excellent introduction to statistical analysis with interactive Python notebooks

    • book

    • , a good general-purpose book introducing different types of humanities data analysis

  • Further resources to go through that may cover bits and bobs missed by the above:

Data science
Statistics
Elective theory and practice courses available as part of the Helsinki DH module
Elective method courses as part of the Helsinki DH module
Online courses at dariahTeach
Data Carpentry tutorials
Library Carpentry tutorials
Python Programming for the Humanities
Software Carpentry tutorials
CodeRefinery tutorials
The programming historian
Eloquent Javascript
Fundamentals of Data Visualization
Data Visualization - a practical introduction
Statistical Inference via Data Science: A ModernDive into R and the Tidyverse!
Online Statistics Education: An Interactive Multimedia Course of Study
Introduction to Open Data Science MOOC
Computational and Inferential Thinking - The Foundations of Data Science
R for Data Science
The historian’s macroscope
Statistics for the Humanities
Explained Visually
Kristoffer Magnusson's Visualizations of statistical concepts
Natural Language Processing for Historical Texts
Six Septembers: Mathematics for the Humanist
Text Analysis with R for Students of Literature
Teaching yourself to code in DH list
Technical Foundations of Informatics
Computational Historical Thinking With Applications in R
Humanities Data in R
Text Mining with R