Spring 2018

INF 385T Special Topics in Information Science : Critical Perspectives On Cultural Data Analysis

Unique ID: 27345

DESCRIPTION

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COURSE NOTES

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: Exploration of cultural implications of large-scale preservation of cultural materials. Writing using perspectives in critical data studies; Familiarity with scripting-style programming in Python and Unix-like systems, emphasizing literacy in finding and using free and open source software; techniques for collecting, transforming, and analyzing media and metadata available on the Web; with commonly used data models and their standard formats, including CSV, JSON, and XML; with text analysis techniques such as natural language processing (NLP), sentiment analysis, and machine learning classification; and with tools for analyzing cultural data via visualization and statistical tests, emphasizing critical reflection on the limitations of these approaches. Course Principles Writing critically about data requires both a level of knowldege about data and data wrangling as it requires a level of knowledge about thinking and writing from critical perspectives learned in cultural studies. While this course does not teach cultural studies, an understanding of and experience in humanities theory and research and the principles of cultural studies are essential. https://pcda17.github.io/

PREREQUISITES

Graduate standing.

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

RESTRICTIONS

Students from the Department of Computer Science will not be permitted into this class without special permission from the instructor.