wk1.md

HIST3907B

Data Mining & Visualization for Historians

Why this course, and Why Now?

• The discipline is changing
• Tens of millions of dollars (probably more) have been spent on digitizing historical documents
• Wouldn't it be good to figure out what they can tell us?

Note: Rationale behind this course.

Here be dragons.

Today's agenda

1. A potted history of visualization
2. Nuts and bolts of how this course will work
3. Technology and collaboration
4. Homework.

Some Examples from the History of Visualization

Keeping in mind that there's more to dataviz than the visual...

Sumerian cuneiform tablet, listing deities, ca 2400 BC

Note: writing & visualization often are the same thing. Even Excel is a visualization

Peutinger Table

Joseph Minard

Note: maps and what kinds of knowledge they privilege

What do those two maps do?

• what kinds of knowledge do they privilege?

• what kinds of things can be known via a map?

• what are some lies built into maps? (think in terms of rhetoric)

Harry Beck and the Tube

Note: Graphs of the Maps-Graphs-Trees triad

What kinds of stories are implied by a network graph?

I did say this was a woefully incomplete survey.

• Read the article by Bailey & Pregill (look under 'syllabus' > 'wk 1 readings')
• Read the basic principles of information visualization in The Macroscope

Visualization is a kind of storytelling.

It has its own rhetoric, tropes, modes and idioms. Part of this course is about telling the compelling story using the compelling visualization. The other part is about finding that signal in the noise of historical data in the first place. We'll talk more about that on Wednesday.

Visualization & Exploration are in tension

What you will learn in this course:

1. Identify and define the limitations of useful sources of historical data online

2. Compare and employ appropriate tools to clean and manipulate this data with a critical eye to how the tools themselves are theory-laden

3. Analyze data using various tools with an awareness of the tendency of tools to push towards various historiographic or epistemic perspectives (ie, the 'procedural rhetorics' of various tools)

4. Visualize meaningful patterns in the data to write 'good history' across multiple platforms, with critical evaluation of the limitations

5. Model best practices in open access data management as mandated by SSHRC and other research agencies

6. Develop an online scholarly voice to contribute data and reflection to the wider digital history community

Structure of the course

The modules in the course are built around the progressive steps of working with big data:

1. principles of open access research and your digital identity (learning outcomes 5,6)
2. Finding Data (learning outcomes 1)
3. 'Wrangling' Data, or getting it into useable shape (learning outcome 2)
4. First Questions, or where are the holes and assumptions in your data? (learning outcomes 1,3)
5. Analyzing data, or matching the appropriate tool to the question (learning outcomes 2,3)
6. Visualizing (graphing, writing, plotting, mapping) data patterns, or communicating the compelling story (learning outcome 4)

Assessment

• The key to earning a good grade is simply to do the work.

• Another component is to actually come and talk to me outside of class.

• And finally: learn from one another. I expect you to help each other.

• Exercises: 30 % (You will select your six best. See the repo)

• Final project: 30%

• Paradata document: 20%

• Active, value-added community building by participation in the discussion(s), across mutliple media and platforms: 20%

Due dates

also available in the repo ###Exercises:

• Principles of open access research: January 14th
• Finding Data: January 28th
• Wrangling Data: February 11th
• Where are the holes? February 25th
• Analyzing Data: March 11th
• Visualizing Data: March 25th

Select your six best for formal grading consideration: April 1st

###Final Project:

• Checkpoint 1: February 14th
• Checkpoint 2: February 25th
• DRAFT for feedback: March 25th
• Final version due April 8th

###Paradata document:

• April 8th

Final Project

We'll talk more about this on Wednesday.

Know that it will not be something that can be done at the last minute.

Contact details

I'm Dr. Graham.

On Twitter I'm @electricarchaeo

On Email I'm at [email protected]

In person I hide in PA406 and you can find me probably on Mondays or Wednesdays.

Homework:

1. Explore the course repo.
2. Read through the two articles.
3. Fasten your seatbelts!

This course will not be easy. On the other hand, 50% of the grade is up for grabs just for doing the homework and being a decent classmate.

Digital work requires community - we learn and build together. It takes a lot of practice, experimentation, and productive failures.

Welcome to the future of history!