How to contact me:
email: via canvas or to unmil-At-ischool.utexas.edu (please include the text "INF385T" in your subject line)
with prior appointment in-person meeting or, videoconference
skype: unmil.karadkar, Google hangouts: unmilk-aT-gmail.com (please don't send email to this address)
drop by my office without an appointment–I will try to make time for you
Classroom: UTA 1.204
Course unique id: 28300
Canvas page: https://utexas.instructure.com/courses/1204378
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.
- Graduate standing
- Basic knowledge of a programming language (for example: variables, constants, statements, conditionals, loops, function calling, and return values in a modern programming language such as Python, PHP, Java, or C++)
- Basic understanding of data modeling (examples: Entity-Relationship diagrams, data models, Dublin Core, METS, or data structures)
- A motivation to learn more as necessary for your project
No textbook. Assigned readings will be available online or made available via Canvas. Some online readings are only available to UT Austin students and you may access these off-campus via UT-VPN or via the libraries' web site.
Large-scale digitization projects such as the National Digital Newspaper as well as increasing quantities of born-digital materials have put enormous collections of documents and data within our reach. This is a studio-style course designed to explore techniques that will make these massive quantities of data (although, not necessarily not BIGDATA) useful to targeted demographics, for specific goals via the use of programmatic techniques. This course both draws upon and will enable you to contribute to the areas of digital libraries and archives (collections, digitization), computational techniques (database management, data mining), and user experience (interaction design, HCI). Participants in this course will work in small teams, crafting small projects that demonstrate the viability of the proposed solutions. Typical projects will involve the development and/or evaluation of parts of data pipelines--ingestion, transformation, storage, manipulation, and presentation.
Pedagogy and Organization
Class time will be split between short content-based lectures, reading discussions & debates, and group activities. Lectures will highlight content from assigned readings. The goal is to create a learning environment in the classroom where we raise significant questions, discuss concepts, and develop skills collaboratively. This format requires participation of all class members. Students are expected to:
At the end of this course, you will be able to:
My Personal Goals
In addition to the content-specific objectives, I will do my best to: