Helsinki fall 2018

Timetable for fall 2018

Wednesday 31.10. — Introduction

Assignments given

For 2.11.:

  1. Answer the course background questionnaire

  2. Look over the final projects from last year. Select the project that interests you the most. Post a short message on the #introductions channel on the course Slack to introduce yourself and to describe why you chose those that project.

  3. Read up on the history of humanities computing. Be ready to discuss in groups on the next lecture.

(pdf, gd)

Assignments due
Assignments given
  1. Answer the course background questionnaire

  2. Look over the final projects from last year. Select the project that interests you the most. Post a short message on the #introductions channel on the course Slack to introduce yourself and to describe why you chose those that project.

  3. Read up on the history of humanities computing. Be ready to discuss in groups on the next lecture.

Assignments for 7.11.:

  1. Find a dataset that could be of interest to you in your final project. Post a message on #datasets on Slack giving a link to the dataset and a note on why you selected it.

  2. Read Perception deception & Common visualization mistakes as preparation for next week, learning to not trust visualisations blind.

(pdf, gd)

Assignments due
Assignments given
  1. Find a dataset that could be of interest to you in your final project. Post a message on #datasets on Slack giving a link to the dataset and a note on why you selected it.

  2. Read Perception deception & Common visualization mistakes as preparation for next week, learning to not trust visualisations blind.

Two full weeks to do these, need to be done only by 21.11.:

  1. Data cleanup: complete the OpenRefine tutorial.

  2. Visualisation: Experiment with at least one of the following tools:

    • tabular data → chart visualisations: RAW

    • tabular data → chart visualisations: Voyager

    • tabular data → chart visualisations: ​Tableau

    • tabular data → ​interactive map/network/timeline/list/facet visualisations: Palladio

      • Palladio has help pages. There are also multiple tutorials on using Palladio, for example this one, or this one which is particularly on network analysis.

    • tabular data → map(+timeline) visualisations: ​Carto

    • ​text →​ interactive explorative interface for linguistic study: Voyant tools

    • ​big, preselected collections of text → interface for linguistic study: Korp / corpus.byu.edu

    • If you're feeling explorative, feel free to also dig for more tools in TAPoR.

    If you're short on inspiration, feel free to go through this hands-on tutorial covering OpenRefine, RAW and Palladio.

    Afterwards, post a message on #tools on Slack detailing:

    1. What is the tool good for?

    2. What kind of data do you need for the tool to be useful?

      1. What information does the data need to contain?

      2. What format does it have to be in?

    3. Your experience with the tool.

    If someone has already posted on the tool you tested, don't repeat them. Instead, add to what they've said in a thread. (also be prepared to discuss the tools in class)

  3. Programming: Go through the fundamental concepts of programming for humanists and complete the assignments there.

  4. Regular expressions: Read the section on regular expressions and go through the assignments there.

  5. In preparation for the lecture on 21.11., read this research article.

Friday 9.11. — No lecture

Wednesday 14.11. — Clinic for support in the assignments

Friday 16.11. — No lecture

Assignments due
Assignments given
  1. Data cleanup: complete the OpenRefine tutorial.

  2. Visualisation: Experiment with at least one of the following tools:

    • tabular data → chart visualisations: RAW

    • tabular data → chart visualisations: Voyager

    • tabular data → chart visualisations: ​Tableau

    • tabular data → ​interactive map/network/timeline/list/facet visualisations: Palladio

      • Palladio has help pages. There are also multiple tutorials on using Palladio, for example this one, or this one which is particularly on network analysis.

    • tabular data → map(+timeline) visualisations: ​Carto

    • ​text →​ interactive explorative interface for linguistic study: Voyant tools

    • ​big, preselected collections of text → interface for linguistic study: Korp / corpus.byu.edu

    • If you're feeling explorative, feel free to also dig for more tools in TAPoR.

    If you're short on inspiration, feel free to go through this hands-on tutorial covering OpenRefine, RAW and Palladio.

    Afterwards, post a message on #tools on Slack detailing:

    1. What is the tool good for?

    2. What kind of data do you need for the tool to be useful?

      1. What information does the data need to contain?

      2. What format does it have to be in?

    3. Your experience with the tool.

    If someone has already posted on the tool you tested, don't repeat them. Instead, add to what they've said in a thread. (also be prepared to discuss the tools in class)

  3. Programming: Go through the fundamental concepts of programming for humanists and complete the assignments there.

  4. Regular expressions: Read the section on regular expressions and go through the assignments there.

  5. In preparation for the lecture on 21.11., read this research article.

In preparation for next Wednesday's (28.11.) lecture, select (at least) one of the following sets of paired articles based on your own interests:

Form a group with all the other people who selected the same articles. For class, prepare a presentation on them, detailing:

  1. How do the two articles relate to each other?

  2. Research questions - What are the humanities research questions? Do the projects also target computer science research questions? If so, what? What is the relationship between the CS and humanities research questions?

  3. Data - How has the data used been gathered? What are the data sources used? How has the data been processed? Is the data available for others to use?

  4. Methods - What methods do the projects apply? How do the methods support answering the research questions?

  5. Partners - What is the make-up of the projects? Which disciplines are represented by the participants?

Assignments given
  1. Check out the Explained Visually site, and especially PCA explained visually

  2. Read on some small, actual work:

    1. The presentation of the DHH15 key concepts of socialism group

    2. The presentation of the DHH15 Finnair Blue Wings multimodality group

    3. If you understand Finnish, the election questionnaire analysis and visualisation

  3. For 5.12., explore this topic model of CEEC and read the explanation on topic modelling

Wednesday 28.11. — Computational data analysis literacy, part 2

Assignments due
Presentations
  1. Check out the Explained Visually site, and especially PCA explained visually

  2. Read on some small, actual work:

    1. The presentation of the DHH15 key concepts of socialism group

    2. The presentation of the DHH15 Finnair Blue Wings multimodality group

    3. If you understand Finnish, the election questionnaire analysis and visualisation

Select (at least) one of the following sets of paired articles based on your own interests:

Form a group with all the other people who selected the same articles. For class, prepare a presentation on them, detailing:

  1. How do the two articles relate to each other?

  2. Research questions - What are the humanities research questions? Do the projects also target computer science research questions? If so, what? What is the relationship between the CS and humanities research questions?

  3. Data - How has the data used been gathered? What are the data sources used? How has the data been processed? Is the data available for others to use?

  4. Methods - What methods do the projects apply? How do the methods support answering the research questions?

  5. Partners - What is the make-up of the projects? Which disciplines are represented by the participants?

Friday 30.11. — No lecture

Wednesday 5.12. — Computational data analysis literacy, part 3

Assignments due
Assignments given
  1. Find a computational humanities research paper that interests you. Post a message on #research on Slack shortly describing why you picked the paper.

  2. Prepare to shortly (max 2 minutes) present your current idea for your final project on the lecture on

Wednesday 12.12. — No lecture, remote support for final project

Friday 14.12. — No lecture, remote support for final project

Friday 21.12. Deadline for returning final project