INF 385T - Special Topics in Information Science: Programming for Cultural Data Analysis
Graduate standing. Additional prerequisites may vary with the topic.
Study of the properties and behavior of information. Technology for information processing and management.
Three lecture hours a week for one semester.
May be repeated for credit when the topics vary.
Programming for Cultural Data Analysis
Prerequsites: advanced-level undergraduate or graduate coursework
in the humanities; no or very little programming experience
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.
- Teach scripting-style programming in Python and Unix-like
systems, emphasizing literacy in finding and using free and open
- Familiarize students with techniques for collecting,
transforming, and analyzing media and metadata available on the
- Introduce commonly used data models and their standard
formats, including CSV, JSON, and XML.
- Explore computational text analysis techniques such as
natural language processing (NLP), sentiment analysis, and
machine learning classification.
- Introduce tools for analyzing cultural data via visualization
and statistical tests, emphasizing critical reflection on the
limitations of these approaches.
- Familiarize students with Web archiving and data curation
- Explore cultural implications of large-scale preservation of
Most assignments include beginning programming skills and medium
to long-form, critical, academic writing.