Information Course Schedule Fall 2014

How can we critically think about emergent phenomena of the Internet? Is the Internet a democratic medium for political action (a "networked public sphere") or a surveillance apparatus of centralized control? Who has access to digital information and what techniques are used to make information artificially scarce? How do trade group lawsuits against digital "piracy" affect a generation's perception of the law? Should we look at the growing sphere of copyright as a public interest problem, or celebrate the expansion of creators' rights? Can free software thrive independently from ideological backing? Why are peer production communities like Wikipedia and Linux affected by extreme gender disparity?

In this course, we will examine the societal implications of computer networks from critical and technical perspectives. We will collectively engage with issues of intellectual property, access to information, privacy, freedom of speech, representation, and peer production. We will be discussing provocative texts and media, doing hands-on exploration of emerging technologies, and practicing ethnographic fieldwork in online communities. We will also offer opportunities for field trips and guest speakers to provide us with different perspectives. Additionally, students will engage in a semester-long collaborative project in a flexible format.

This is a student-initiated group study course (DE-Cal). Please contact the student coordinator(s) for specific questions.

Must be taken on a passed/not passed basis.

With the advent of virtual communities and online social networks, old questions about the meaning of human social behavior have taken on renewed significance. Using a variety of online social media simultaneously, and drawing upon theoretical literature in a variety of disciplines, this course delves into discourse about community across disciplines. This course will enable students to establish both theoretical and experiential foundations for making decisions and judgments regarding the relations between mediated communication and human community. Also listed as Sociology C167.

How can we critically think about emergent phenomena of the Internet? Is the Internet a democratic medium for political action (a "networked public sphere") or a surveillance apparatus of centralized control? Who has access to digital information and what techniques are used to make information artificially scarce? How do trade group lawsuits against digital "piracy" affect a generation's perception of the law? Should we look at the growing sphere of copyright as a public interest problem, or celebrate the expansion of creators' rights? Can free software thrive independently from ideological backing? Why are peer production communities like Wikipedia and Linux affected by extreme gender disparity?

In this course, we will examine the societal implications of computer networks from critical and technical perspectives. We will collectively engage with issues of intellectual property, access to information, privacy, freedom of speech, representation, and peer production. We will be discussing provocative texts and media, doing hands-on exploration of emerging technologies, and practicing ethnographic fieldwork in online communities. We will also offer opportunities for field trips and guest speakers to provide us with different perspectives. Additionally, students will engage in a semester-long collaborative project in a flexible format.

This is a student-initiated group study course (DE-Cal). Please contact the student coordinator(s) for specific questions.

Must be taken on a passed/not passed basis.

15 weeks; 3 hours of lecture per week. This course introduces the intellectual foundations of information organization and retrieval: conceptual modeling, semantic representation, vocabulary and metadata design, classification, and standardization, as well as information retrieval practices, technology, and applications, including computational processes for analyzing information in both textual and non-textual formats.

7 weeks - 4 hours of laboratory per week. This course introduces software skills used in building prototype scripts for applications in data science and information management. The course gives an overview of procedural programming, object-oriented programming, and functional programming techniques in the Python scripting language, together with an overview of fundamental data structures, associated algorithms, and asymptotic performance analysis. Students will watch a set of instructional videos covering material and will have four hours of laboratory-style course contact each week.

This course will provide an introduction to the field of human-computer interaction (HCI). Students will learn to apply design thinking to user experience (UX) design, prototyping, & evaluation. The course will also cover special topic areas within HCI.

This course focuses on the practice of leadership, collaboration, and people management in contemporary, distributed, information and technology-rich organizations. Not just for potential people managers, we start with the premise that a foundation in leadership, management, and collaboration is essential for individuals in all roles, at any stage of their career. To build this foundation we will take a hybrid approach, engaging literature from disciplines such as social psychology, management, and organizational behavior, as well as leveraging case studies and practical exercises. The course will place a special emphasis on understanding and reacting to social dynamics in workplace hierarchies and teams.

"Behavioral Economics" is one important perspective on how information impacts human behavior. The goal of this class is to deploy a few important theories about the relationship between information and behavior, into practical settings — emphasizing the design of experiments that can now be incorporated into many 'applications' in day-to-day life. Truly 'smart systems' will have built into them precise, testable propositions about how human behavior can be modified by what the systems tell us and do for us. So let's design these experiments into our systems from the ground up! This class develops a theoretically informed, practical point of view on how to do that more effectively and with greater impact.

Three hours of lecture per week. The philosophical, legal, historical, and economic analysis of the need for and uses of laws protecting intellectual property. Topics include types of intellectual property (copyright, patent, trade secrecy), the interaction between law and technology, various approaches (including compulsory licensing), and the relationship between intellectual property and compatibility standards.

This course is a survey of technologies that power the user interfaces of web applications on a variety of devices today, including desktop, mobile, and tablet devices. This course will delve into some of the core front-end languages and frameworks (HTML/CSS/JavaScript/React/Redux), as well as the underlying technologies that enable web applications (HTTP, URI, JSON). The goal of this course is to provide an overview of the technical issues surrounding user interfaces powered by the web today, and to provide a solid and comprehensive perspective of the web’s constantly evolving landscape.

Three hours of lecture per week. Letter grade to fulfill degree requirements. Prerequisites: Proficient programming in Python (programs of at least 200 lines of code), proficient with basic statistics and probabilities. This course examines the state-of-the-art in applied Natural Language Processing (also known as content analysis and language engineering), with an emphasis on how well existing algorithms perform and how they can be used (or not) in applications. Topics include part-of-speech tagging, shallow parsing, text classification, information extraction, incorporation of lexicons and ontologies into text analysis, and question answering. Students will apply and extend existing software tools to text-processing problems.

Three hours of lecture per week. Introduction to relational, hierarchical, network, and object-oriented database management systems. Database design concepts, query languages for database applications (such as SQL), concurrency control, recovery techniques, database security. Issues in the management of databases. Use of report writers, application generators, high level interface generators.

Students will receive no credit for C262 after taking 290 section 4. Three hours of lecture and one hour of laboratory per week. This course explores the theory and practice of Tangible User Interfaces, a new approach to Human Computer Interaction that focuses on the physical interaction with computational media. The topics covered in the course include theoretical framework, design examples, enabling technologies, and evaluation of Tangible User Interfaces. Students will design and develop experimental Tangible User Interfaces using physical computing prototyping tools and write a final project report. Also listed as New Media C262.

Three hours of lecture per week. Introduction to many different types of quantitative research methods, with an emphasis on linking quantitative statistical techniques to real-world research methods. Introductory and intermediate topics include: defining research problems, theory testing, causal inference, probability and univariate statistics. Research design and methodology topics include: primary/secondary survey data analysis, experimental designs, and coding qualitative data for quantitative analysis. No prerequisites, though an introductory course in statistics is recommended.

Three hours of lecture per week. Theory and practice of naturalistic inquiry. Grounded theory. Ethnographic methods including interviews, focus groups, naturalistic observation. Case studies. Analysis of qualitative data. Issues of validity and generalizability in qualitative research.

Three hours of seminar per week.  This seminar reviews current literature and debates regarding Information and Communication Technologies and Development (ICTD). This is an interdisciplinary and practice-oriented field that draws on insights from economics, sociology, engineering, computer science, management, public health, etc.

Network studies have been described as “a terminological jungle in which any newcomer may plant a tree.” Since J.A. Barnes wrote that in 1972, newcomers have proliferated, the jungle flourished, and the ecosystem diversified dramatically. This growth is particularly evident in the region of “social networks” — though it can sometimes be hard to envisage anything social that could not be called a network. The aim of this course, then, is to try to understand what has been described as the “modern obsession” with networks, to try to decide what might be valuable and what ephemeral, and to see if we can justify such decisions. For this, we will attempt to set some recent accounts in both disciplinary and historical context. Consequently, we will look at contributions from different fields and different periods. In particular, this seminar will seek to encourage dialogue among its participants by examining the implicit dialogue among the texts we shall be reading and the fields they represent, while keeping an eye on cases where, despite the shared terminology, the works seem to have nothing to say to each other.

This course takes a multi-disciplinary approach to explore the possibilities and limitations of ubiquitous sensing technologies for physiological and contextual data. We will survey the intellectual foundations and research advances in ubiquitous computing, biosensory computing, and affective computing, with applications ranging from brain-computer interfaces to health and wellness, social computing to cybersecurity. We will cover temporal and spectral analysis techniques for sensor data. We will examine data stewardship issues such as data ownership, privacy, and research ethics. Students signing up for the 3-unit option will continue in the second half of the semester with a student-led research project.

What insights about student learning can be revealed from data, and how can those insights be used to improve the efficacy of educational technology? This course will cover computational approaches to the task of modeling learning and improving outcomes in Intelligent Tutoring Systems (ITS) and Massive Open Online Courses (MOOCs). We will cover theories and methodologies underpinning current approaches to knowledge discovery and data mining in education and survey the latest developments in the broad field of human learning research.

This course will be project based, where teams will be introduced to online learning platforms and their datasets with the objective of pairing data analysis with theory or implementation. Literature review will serve to add context and grounding to projects.

Suggested background includes one programming course and familiarity with one statistical/computational software package.

The study of learning in online environments is an interdisciplinary pursuit, and therefore all majors are welcomed and encouraged to bring complimentary backgrounds.

Undergraduates with the appropriate background and motivation are encouraged to enroll but must contact Associate Director of Student Affairs Catherine Cronquist Browning for enrollment permissions.
 

Students in this course will expand on their knowledge of techniques for exploratory data analysis (EDA) and collaborate on and contribute to a research project whose goal is to create a new framework for the EDA process.

Specific topics, hours and credit may vary from section to section, year to year. May be repeated for credit with change in content.

Course may be repeated for credit. One and one-half to two hours of lecture per week for eight weeks. Two hours of lecture per week for six weeks. Three hours of lecture per week for five weeks.

Course may be repeated for credit. Three hours of lecture per week for five weeks.

Course may be repeated for credit. Three hours of lecture per week for five weeks.

Every business depends on information — about customers, competitors, trends, performance, etc. Entire curricula have been focused on the technological, systems, strategic, and management challenges associated with that dependency. This course, however, looks at a different intersection between information and business. Specifically, it will explore how entrepreneurs across the world are developing ventures fundamentally centered on new and emerging information technologies and the business models and strategies they make possible. These include not only the Googles, Amazons, and Facebooks of the world, but also ventures like Comat and Samasource. In some cases, these are efforts on the proverbial cutting edge of technology; more often they involve creative application and/or integration of existing information technologies in innovative ways.

We will first examine the key elements of business models and the entrepreneurial process, before looking in more detail at a variety of ventures leveraging information-based technologies and strategies in an array of markets. Using of mix of case-study discussion, short lectures, and focused conversations with active entrepreneurs, this will be a highly interactive and collaborative course — not a sit-listen-take-notes type of class.

Expect to be actively involved in a series of in-class and outside assignments, both individual- and team-based, that will help you develop an understanding of how entrepreneurs are using information-centric technologies to create new markets and redefine old ones, and the lessons learned along the way. You may also explore your own ideas for new ventures along the way.

Students will build tools to explore and apply theories of information organization and retrieval. Students will implement various concepts covered in the concurrent 202 course through small projects on topics like controlled vocabularies, the semantic web, and corpus analysis. We will also experiment with topics suggested by students during the course. Students will develop skills in rapid prototyping of web-based projects using Python, XML, and jQuery.

One hour colloquium per week. Must be taken on a satisfactory/unsatisfactory basis. Prerequisites: Ph.D. standing in the School of Information. Colloquia, discussion, and readings designed to introduce students to the range of interests of the school.

Topics in information management and systems and related fields. Specific topics vary from year to year. May be repeated for credit, with change of content. May be offered as a two semester sequence.

Discussion, reading, preparation, and practical experience under faculty supervision in the teaching of specific topics within information management and systems. Does not count toward a degree.