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Tools & Techniques for Digital Humanities (2019W)

070112 UE Course in Methodology - Tools & Techniques for Digital Humanities (2019W) — University of Vienna, Department of History; Instructor Dr. Maxim G. Romanov

Syllabus

070112 UE Course in Methodology - Tools & Techniques for Digital Humanities (2019W)

  • Instructor: Dr. Maxim Romanov, maxim.romanov@univie.ac.at
  • Language of instruction: English
  • Office hours: We 2–3pm (or by appointment)
  • Office: Hauptgebäude, Room O2.121 (2.Stock, Stiege 9)

Course

Aims, contents and method of the course

The class will introduce the students to a variety of software tools and methods used in the Digital Humanities, primarily using the Python programming language. No prior programming experience is expected.

Course Evaluation

Course evaluation will be a combination of in-class participation (30%), weekly homework assignments (50%), and the final project (20%).

Class Participation

Each class session will consist in large part of practical hands-on exercises led by the instructor. BRING YOUR LAPTOP! We will accommodate whatever operating system you use (Windows, Mac, Linux), but it should be a laptop rather than a tablet. Don’t forget that asking for help counts as participation!

Homework

Just as in research and real life, collaboration is a very good way to learn and is therefore encouraged. If you need help with any assignment, you are welcome to ask a fellow student. If you do work together on homework assignments, then when you submit it please include a brief note (just a sentence or two) to indicate who did what.

NB: On submitting homework, see below.

Final Project

The final project is your website built in Jekyll and hosted on Github. Your website must include blogposts about all homework assignments. You are most welcome to work on this in groups, but everybody is required to submit his/her own course website.

Study materials:

Software, Tools, & Technologies:

The following is the list of software, applications and packages that we will be using in the course. Make sure to have them installed by the class when we are supposed to use them.

Submitting Homework:

  • Homework assignments are to be submitted by the beginning of the next class;
  • For the first few classes you must email them to the instructor (as attachments)
  • Later, you will be publishing your homework assignments on your websites and sending an email to the instructor informing that you have completed your homework and providing a link to the blogpost with the homework report that you created.
    • In the subject of your email, please, use the following format: CourseID-LessonID-HW-Lastname-matriculationNumber, for example, if I were to submit homework for the first lesson, my subject header would look like: 070112-L01-HW-Romanov-12435687.
  • For Codecademy.com assignments:
  • DH is a collaborative field, so you are most welcome to work on your homework assignments in groups, however:
    • You must still submit it. That is, if a groups of three works on one assignment, there must be three separate submissions: either emailed from each member’s email; or, later, published on each member’s website.
    • Codecademy.com assignments must be completed individually.

Schedule

Location: Seminarraum Geschichte 2 Hauptgebäude, 2.Stock, Stiege 9

  • Monday 07.10. 14:15-15:45
  • Monday 14.10. 14:15-15:45
  • Monday 21.10. 14:15-15:45
  • Monday 28.10. 14:15-15:45
  • Monday 04.11. 14:15-15:45
  • Monday 11.11. 14:15-15:45
  • Monday 18.11. 14:15-15:45
  • Monday 25.11. 14:15-15:45
  • Monday 02.12. 14:15-15:45
  • Monday 09.12. 14:15-15:45
  • Monday 16.12. 14:15-15:45
  • Monday 13.01. 14:15-15:45
  • Monday 20.01. 14:15-15:45
  • Monday 27.01. 14:15-15:45

Lesson Topics

  • [ #01 ] Citation Management and Academic Writing I - with Zotero and MS Word or Open Office
  • [ #02 ] “Off with the Interface!” Getting to know the command line
  • [ #03 ] Version Control and Collaboration: Github.com
  • [ #04 ] Citation Management and Academic Writing II - with Pandoc, markdown, Zotero/BibTex
  • [ #05 ] Constructing robust searches with Regular expressions
  • [ #06 ] Webscraping with Wget, preparing URLs with Python and other tools
  • [ #07 ] Text Markup [TEI XML], and how to remove it… - with Python scripts [anaconda]
  • [ #08 ] Structuring data with Python scripts
  • [ #09 ] Georeferencing with QGIS
  • [ #10 ] Text to Map (1/2) - with Python and QGIS
  • [ #11 ] Text to Map (2/2) - with Python and QGIS
  • [ #12 ] Topic modeling - with Python
  • [ #13 ] Social Network Analysis - with Gephi; preparing network data with Python

Note: one of the classes might be canceled; this will be announced separately. Lesson materials will be appearing on the website shortly before each class. Lessons will be accessible via the Lessons link on the left panel.