Special Topics in Information
Specific topics, hours and credit may vary from section to section, year to year. May be repeated for credit with change in content.
What can computation allow us to discover about literature and how might machine learning transform literary theory? Can we use algorithms to challenge gender norms and complicate ideas about race and authorship in novels and other narratives? And what can quantification tell us about the markers of poetic form, stylistic innovation, or literary prestige? This course will study these and other issues in the field of digital humanities or cultural analytics to teach humanities scholars computational methods to develop new insights in their disciplines and instruct students of data science in literary and cultural theories to deepen and nuance their approach to data.
Open to both undergraduate and graduate students, our course will pursue three ends: first, we will explore the landscape of methods currently used by scholars at the intersection of computation and cultural theory (including methods in natural language processing such as named entity recognition, coreference resolution and parsing; computer vision, including image recognition; classification; social network analysis; and hypothesis testing), and discuss others that are at the leading edge of adoption. Second, we will cast a critical eye on those methods and discuss the assumptions they make about their objects of study in order to determine when they can be appropriately applied. And third, we will lead students through the act of operationalizing a research question in the digital humanities.
The course will be structured in two parts: the first will focus on reading articles in the field and detailing the technical knowledge needed to execute that work. We will focus on research that touches several core themes — theories of gender, race, and class, narratology (including free indirect discourse and focalization, or representations of consciousness), poetic theory and meter, and structuralist philosophy — and explore several modalities, including text, images and sound. The second part of the course will be structured as a lab/studio/critique, where students lead discussion about their specific research projects and receive feedback from the class and professors. Our hope is that you will leave the class with the knowledge to carry forward your own projects in computation and critique.
A seminar focusing on topics of current interest. Topics will vary. A seminar paper will be required. Open to students from other departments.
This course introduces students to data visualization: the use of the visual channel for gaining insight with data, exploring data, and as a way to communicate insights, observations, and results with other people.
The field of information visualization is flourishing today, with beautiful designs and applications ranging from journalism to marketing to data science. This course will introduce foundational principles and relevant perceptual properties to help students become discerning judges of data displayed visually. The course will also introduce key practical techniques and include extensive hands-on exercises to enable students to become skilled at telling stories with data using modern information visualization tools.
Students will be asked to complete assignments before class, work together in small groups in class, and provide peer assessments. Grades will be based on assignments, quizzes, in class participation, peer assessment quality, 2 midterms, and a final project. The assignments for the course will together work towards building a coherent visualization that tells a story and is visible on the web.
This course is designed for upper division undergraduates who have an interest in design and in data. It is intended to accommodate students who have only a limited programming background, as well as those who are skilled with programming. For this reason, the only prerequisite is CS/Stat/Info 8 or equivalent. This course assumes students already have familiarity with basic data analysis and manipulation, and basic statistics.
Students are encouraged but not required to have taken other courses from the introductory design sequence (one of DES INV 10- Discovering Design DES INV 15- Design Methodology, DES INV 21- Visual Communications & Sketching, CS 160 User Interface Design and Development), as well as other introductory data science and statistics courses.
Graduate students will be accommodated only as space permits.
This studio course introduces students to design thinking and the basic practices of interaction design. Following a human-centered design process that includes research, concept generation, prototyping, and refinement, students will work as individuals and in small teams to design mobile information systems and other interactive experiences. Assignments approach design on three levels: specific user interactions, contexts of use, and larger systems. Becoming familiar with design methodologies such as sketching, storyboarding, wire framing, and prototyping, students will learn core skills for understanding the rich contexts of stakeholders and their interactions with technology, for researching competing products and services, for modeling the current and preferred state of the world, and for prototyping and communicating possible solutions.
No coding is required.