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Course materials for 'Critical Perspectives in Cultural Data Analysis' at UT Austin's iSchool

Syllabus: Critical Perspectives in Cultural Data Analysis

University of Texas at Austin School of Information

Fall 2017, Mondays 3–6 p.m.

Instructor: Tanya Clement

TA: Steve McLaughlin

Office hours: Mondays 1–3 p.m., UTA 5.558

Course Schedule
Week 1 Week 2 Week 3 Week 4
Week 5 Week 6 Week 7 Week 8
Week 9 Week 10 Week 11 Week 12
  Week 13 Week 14  

Course Objectives

Prerequsites: advanced-level undergraduate or graduate coursework in the humanities; no or very little programming experience preferred;

In the data, information, knowledge, wisdom (DIKW) hierarchy that circulates through Knowledge Management (KM) and Information Science (IS) discussions, data appears at the base of a pyramid of which wisdom is the pinnacle. In this schematic, data is “raw” and lacking in meaning, while information, the next higher level of the pyramid—just below knowledge and then wisdom—represents the presence of added links and relationships; information is higher up on the wisdom chain because it is data made meaningful. In the humanities, students are taught that data is not found in the “raw” but has rather been cooked all along, taken and constructed and seasoned according to our situated contexts including access issues (Where is the data?); media, format, and technology constraints (How is the data?); and perspectives (What is the data? Who is involved in and impacted by its creation and use?).

Learning to think critically about data as information means rejecting common illusions about data more generally, including its objectivity, impersonality, atemporality, and authorlessness. To teach students to think about information from this more critical perspective means first understanding how a culture tends to understand what is informative.

Towards these ends, this course takes on “data wrangling” in the context of humanist perspectives.

Learning goals:

Course Principles

Assignments

Final Project: Critical Data Analysis (50%)

For your final project, you will use a dataset drawn from online sources and analyze those data in a critical essay. You may either present an argument about the data (e.g., describing bias in the way the data were chosen and arranged) or you may use your dataset as the basis for an argument about culture (e.g., tracing a stylistic shift in a literary community). You should conceive and execute your project with a specific audience in mind, such as literary scholars, newspaper readers, or policy advocates.

Your dataset should comprise at least 200 texts or other media files, or at least 2000 metadata records. The size of your collection should be appropriate to your technical skills and the complexity of each record. Rather than using an entire pre-existing dataset, you may choose to extend or limit the dataset in some way. This might mean curating material from multiple sources, mashing up two or more datasets, augmenting records using machine learning or natural language processing, or using a creative technique to organize messy data.

Your final project will include the following elements:

Weekly Assignments (WA) (50%)

Except when indicated, there will be required readings each week. The required readings will either be available online and linked below or posted on Canvas, so there are no books to buy or papers to acquire for the class.

Assignments should be posted on Canvas by midnight the day before class.


Week 1 (9/11): Introductions & Command Line Basics

Readings

Canvas

To start for next week:

▸ In-class outline

Week 2 (9/18): The Operating System in Context

Readings

Readings in Canvas

Optional

Read pages 1–28 of Shieber’s Python tutorial and work through the code examples.

Work through Chris Albon’s tutorial on Python string operations.

Assignment

WA #1

▸ In-class outline

Week 3 (9/25): Collections as Data: Meaning making

Readings

Canvas

Optional

Assignment

WA #2

▸ In-class outline

Week 4 (10/2): Collections as Data: Data Models

Readings

Canvas

Optional Readings

Assignment

WA #3

▸ In-class outline

Week 5 (10/9): An Algorithmic Criticism: Word-Level Text Analysis

<! – Note: assign Text II this week – have them turn in their Jupyter notebook. –>

Readings

Canvas

Assignment

WA #4

▸ In-class outline

Week 6 (10/16): The Rise of Free Culture: Web Scraping & APIs

Readings

Canvas

Optional Readings

Assignment

WA #5

▸ In-class outline

Week 7 (10/23) The Politics of Open Data

Readings

Canvas

Optional Readings

Assignment

WA #6

▸ In-class outline

Week 8 (10/30): Statistics and Visualization

Readings

Canvas

Optional Readings

Assignment

▸ In-class outline

Week 9 (11/6): Your Data, Your culture

No Readings

Assignment

Due: Proposal

▸ In-class outline

Week 10 (11/13): Machine Learning

Readings

Canvas

Optional Readings

Assignment

WA #7

▸ In-class outline

Week 11 (11/20): Critical Text Analysis

Readings

Canvas

Optional Reading

Assignment

WA #8

▸ In-class outline

Week 12 (11/27): Peer Production & Crowdsourcing

Readings

Canvas

Optional Readings

WA #9

▸ In-class outline

Week 13 (12/4): Copyright and the Information Commons

Readings

Canvas

Optional Readings

Assignment

WA #10

▸ In-class outline

Week 14 (12/11): Final Presentations

Final Presentation due

12/18: Final Project due


Additional resources:

Installation Tutorials Jeroen Janssens Seven Command Line Tools for Data Science (2013) workbench. Juola, P. and Ramsay, S. Six Septembers: Mathematics for the Humanist. Zea E-Books. Seaver, Nick “Algorithms as culture: Some tactics for the ethnography of algorithmic systems” Big Data and Society. 9 Nov. 2017