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Introduction to methods for digital humanities

By Eetu Mäkelä, professor (tenure track) in Humanities–Computing Interaction at the University of Helsinki.

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Target audience

People of all levels in the humanities interested in whether computational methods might help them in their own work.

Prerequisites: Absolutely none.

Aside: Why should you be interested in computational methods? Two reasons:

  1. they may allow you yourself to do your work more efficiently, and
  2. they may lead to completely new and powerful ways of addressing questions in your field

The probability of either of these happening very much depends on what you are interested in, but not in any way that can be shortly enumerated. Instead, that is what this course aims at enabling you to discover yourself.

Course concept and learning goals

This course is an introductory signposting course on applying modern data processing to complex social and historical data. As such, it doesn't target the wide world of all different digital humanities. Instead, it hems closely to our local focus in digital humanities, which itself aligns with the long tradition of humanities computing. On the other hand, with regard to subfields of the humanities, the course makes no delineations, on the contrary arguing that by taking examples from different fields, a deeper understanding of the possibilities afforded by computation can be attained. For more details, see the introduction.

As a signposting course, the course provides students with the knowledge they need to choose their own focus within computational humanities, also manifesting in the ability to choose where to go for further knowledge.

After this course the student understands the multiple ways in which methods benefit work within the computational humanities. She herself is able to use ready tools to work with data. In addition, she has attained knowledge of the fundamental concepts of programming, through which she can start to expand her capabilities, should she so choose. She also learns how open, reproducible research and publishing is done in practice. Further, the student gains a general literacy on advanced statistical and computer science methods applicable to computational humanities, and when to apply them. Finally, she learns to apply all of the above in practice in a small concrete computational humanities project.

Yet most importantly, after this course and utilising all of the above, the student is able to:

  1. make informed decisions on which computational approaches will be of use to herself, and
  2. understand and follow the development of computational approaches within her field in general.

Format

This course is meant for both independent self-study (reading up on only certain sections of the course), as well as for completing as either a contact learning or MOOC course with a group of likeminded students. For material relating to particular instances of the latter mode of study, see here.

Course contents

  1. Introduction: three approaches to methods for digital humanists
    • Easy, ready-made tools for data cleanup, visualisation and exploration
    • Fundamentals of programming for data processing
    • Data analysis method literacy
  2. Different types of data, data quality, available open datasets
  3. Easy tools for processing and exploring data
  4. Data processing: fundamental concepts of programming for humanists
  5. Data processing: regular expressions
  6. Data analysis: fundamental concepts of statistics
  7. Computational data analysis method literacy
  8. Open, reproducible research and publishing
  9. Digital humanities project

Practical matters

  • The course has a Slack workspace at meth4dh.slack.com used for both returning some assignments as well as peer and teacher support. Please join it, starting by entering your email here.
  • For linking to quotes in their original context, the course uses hypothes.is. To be able to use this, you must join the METH4DH group (as well as register in general if you don't already have an account). You also naturally need access to the sources (most commonly through accessing them from a university network / VPN).

Licensing

The text of this course is licensed under a Creative Commons Attribution 4.0 International License. This means that you are free to use, embed, remix and further develop any part of this course for use in your own course or other material. The only requirement is that you give appropriate credit for this material, provide a link to the license, and indicate if changes were made (see the license for more details).

If you do make use of this material, I'd naturally also appreciate a ping, as well as the possibility to merge any improvements to this version, even if neither of those is actually required by the license.

For access to the source code of this GitBook, please see this GitHub repository.