Information Course Schedule spring 2017
This course provides an introduction to critical and ethical issues surrounding data and society. It blends social and historical perspectives on data with ethics, policy, and case examples to help students develop a workable understanding of current ethical issues in data science. Ethical and policy-related concepts addressed include: research ethics; privacy and surveillance; data and discrimination; and the “black box” of algorithms. Importantly, these issues will be addressed throughout the lifecycle of data — from collection to storage to analysis and application. Course assignments will emphasize researcher and practitioner reflexivity, allowing students to explore their own social and ethical commitments.
Surveying history through the lens of information and information through the lens of history, this course looks across time to consider what might distinguish ours as “the information age” and what that description implies about the role of “information technology” across time. We will select moments in societies’ development of information production, circulation, consumption, and storage from the earliest writing and numbering systems to the world of Social Media. In every instance, we’ll be concerned with what and when, but also with how and why. Throughout we will keep returning to questions about how information-technological developments affect society and vice versa?
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.
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.
This course is designed to be an introduction to the topics and issues associated with information and information technology and its role in society. Throughout the semester we will consider both the consequence and impact of technologies on social groups and on social interaction and how society defines and shapes the technologies that are produced. Students will be exposed to a broad range of applied and practical problems, theoretical issues, as well as methods used in social scientific analysis. The four sections of the course are: 1) theories of technology in society, 2) information technology in workplaces 3) automation vs. humans, and 4) networked sociability.
This course uses examples from various commercial domains — retail, health, credit, entertainment, social media, and biosensing/quantified self — to explore legal and ethical issues including freedom of expression, privacy, research ethics, consumer protection, information and cybersecurity, and copyright. The class emphasizes how existing legal and policy frameworks constrain, inform, and enable the architecture, interfaces, data practices, and consumer facing policies and documentation of such offerings; and, fosters reflection on the ethical impact of information and communication technologies and the role of information professionals in legal and ethical work.
This course addresses concepts and methods of user experience research, from understanding and identifying needs, to evaluating concepts and designs, to assessing the usability of products and solutions. We emphasize methods of collecting and interpreting qualitative data about user activities, working both individually and in teams, and translating them into design decisions. Students gain hands-on practice with observation, interview, survey, focus groups, and expert review. Team activities and group work are required during class and for most assignments. Additional topics include research in enterprise, consulting, and startup organizations, lean/agile techniques, mobile research approaches, and strategies for communicating findings.
Three hours of lecture per week. This course applies economic tools and principles, including game theory, industrial organization, information economics, and behavioral economics, to analyze business strategies and public policy issues surrounding information technologies and IT industries. Topics include: economics of information goods, services, and platforms; economics of information and asymmetric information; economics of artificial intelligence, cybersecurity, data privacy, and peer production; strategic pricing; strategic complements and substitutes; competition and antitrust; Internet industry structure and regulation; network cascades, network formation, and network structure.
The design and presentation of digital information. Use of graphics, animation, sound, visualization software, and hypermedia in presenting information to the user. Methods of presenting complex information to enhance comprehension and analysis. Incorporation of visualization techniques into human-computer interfaces. Three hours of lecture and one hour of laboratory per week.
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.
This course will cover new interface metaphors beyond desktops (e.g., for mobile devices, computationally enhanced environments, tangible user interfaces) but will also cover visual design basics (e.g., color, layout, typography, iconography) so that we have systematic and critical understanding of aesthetically engaging interfaces. Students will get a hands-on learning experience on these topics through course projects, design critiques, and discussion, in addition to lectures and readings. Two hours of lecture per week.
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 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.
Many of us are interested in looking forward towards future challenges and opportunities (near, medium, and occasionally long term) of the information economy and society. But technology prognostication has a terrible track-record. And keying on worst-case and best-case possibilities is an unrealistic, inefficient, and sometimes dangerous way to generate insight. Scenario thinking is an alternate methodology, developed first by Royal Dutch Shell for use in the energy sector after the oil shocks of the 1970s and later extended more broadly to business, government, and non-profit sectors. Scenario thinking starts from the proposition that the future is unpredictable in any meaningful sense… and that it is possible instead to systematically develop a landscape of possible futures from which useful insights can be drawn, and against which strategic action can be planned. In this seminar we will learn, practice, and develop scenario thinking for the information economy and society. We’ll explore the scientific limits of prediction; decision biases in that setting; and alternative methods for gaining and communicating insight that changes what people think and what they do. We’ll develop our own scenarios and use them to explore systematically challenges and opportunities ahead for the things we care about — business ideas, governance challenges, social change, etc. This seminar will call on a high level of energy, creativity, and open-mindedness as well as great teamwork.
This is an introductory course on design, problem solving and innovation. While the principles generalize to any context, this course focuses on solutions that take the form of digital goods and services.
This is a team-based, experiential learning course. Students who take this course should expect to:
Work with a team that includes different backgrounds, interests, and personal motivations. As a cross-listed course, teams may or may not include students from different schools across the University (depending upon enrollment).
Experience a process for identifying and prioritizing opportunities to innovate. The process scales from an entrepreneur working alone to Fortune 500 firms managing an innovation portfolio.
Practice applying qualitative processes (including customer interviews, paper prototyping, and remote user-testing) to characterize the "job to be done," isolate a "minimum viable problem," and iterate your design prototypes.
Practice applying quantitative processes (including analysis of keyword searches, digital ad campaigns, and funnel analysis) to characterize the "job to be done," isolate a "minimum viable problem," and iterate your design prototypes.
Formulate hypotheses and then design and execute experiments in a Lean cycle of build, measure and learn.
Teams will learn general principles of product/service design in the context of tools, methods, and concepts specific to the Web-based environment. Both desktop and mobile products and services are prototyped in the Web context to leverage common development and testing resources. For purposes of the course, the product or service should be aimed at consumers in the range 25 - 45. We define this target audience so that we can use classmates as preliminary subjects of interviews, testing, and surveys. For the purposes of this course, the product or service need not have a compelling business model. The focus is on creating a product or service that solves a real problem, not necessarily creating a new business.
This course teaches a process-oriented approach to product and service design with heavy emphasis on user experience design. Students interested in design aesthetics, semiotics and cognitive psychology should look elsewhere. Neither is this a class about technology. The course syllabus does not include tutorials on specific software packages. Students interested in technical questions such as platform selection and scaling should look elsewhere.
Many products of human invention — political speeches, product reviews, status updates on Twitter and Facebook, literary texts, music and paintings — have been analyzed, not uncontroversially, as “data”. In this graduate-level course (open to all departments, especially those in the humanities and social sciences), we will pursue two ends: we will investigate the landscape of modern quantitative methods for treating data as a lens onto the world, surveying a range of methods in machine learning and data analysis that leverage information produced by people in order to draw inferences (such as discerning the authorship of documents and the political position of social media users, charting the reuse of language in legislative bills, tagging the genres of songs, and extracting social networks from literary texts). Second, we will cast a critical eye on those methods, and investigate the assumptions those algorithms make about the world and the data through which we see it, in order to understand their limitations and when to apply them. How and when can empirical methods support other forms of argumentation, and what are their limits? Many of these techniques are shared among the nascent communities of practice known as “computational social science”, “computational journalism” and the “digital humanities”; this course provides foundational skills for students to conduct their own research in these areas. No computational background is required.
Specific topics, hours and credit may vary from section to section, year to year. May be repeated for credit with change in content.
As new sources of digital data proliferate in developing economies, there is the exciting possibility that such data could be used to benefit the world’s poor. Recent examples from the research literature show how satellite imagery and deep learning can be used to identify and target pockets of extreme poverty; how mobile phone metadata can help track and stop the spread of malaria and Ebola; how social media analytics can improve disaster response; and how machine learning algorithms can help smallholder farmers optimize planting and harvesting decisions – to name just a few examples.
Through a careful reading of recent research papers and through hands-on analysis of large-scale datasets, this course introduces students to the opportunities and challenges for data-intensive approaches to international development. Students should be prepared to dissect, discuss, and replicate academic publications from several fields including development economics, machine learning, information science, and computational social science. Students will also conduct original statistical and computational analysis of real-world data, and are expected to have prior graduate training in machine learning, econometrics, or a related field.
There is a burgeoning market for technologists and lawyers who can understand the application and implementation of privacy and security rules to network connected services. Privacy and Security Lab is a new course designed to promote the development of such “privacy technologists.” Students will meet twice a week, once in lecture, and the second time in a computer lab to gain hands-on skills in privacy and security analysis. The course will explore the concepts, regulations, technologies, and business practices in privacy and security, including how different definitions of “privacy” may shape technical implementation of information-intensive services; the nature of privacy and security enhancing services; and how one might technically evaluate the privacy and security claims made by service providers. There are no prerequisites and enrollment is open to law students to encourage cross-disciplinary exchanges.
The Future of Cybersecurity Reading Group (FCRG) is a two-credit discussion seminar focused on cybersecurity. In the seminar, graduate, professional, and undergraduate students discuss current cybersecurity scholarship, notable cybersecurity books, developments in the science of security, and evolving thinking in how cybersecurity relates to political science, law, economics, military, and intelligence gathering. Students are required to participate in weekly sessions, present short papers on the readings, and write response pieces. The goals of the FCRG are to provide a forum for students from different disciplinary perspectives to deepen their understanding of cybersecurity and to foster and workshop scholarship on cybersecurity.
In this course you’ll learn industry-standard agile and lean software development techniques such as test-driven development, refactoring, pair programming, and specification through example. You’ll also learn good object-oriented programming style. We’ll cover the theory and principles behind agile engineering practices, such as continuous integration and continuous delivery.
This class will be taught in a flip-the-classroom format, with students programming in class. We'll use the Java programming language. Students need not be expert programmers, but should be enthusiastic about learning to program. Please come to class with laptops, and install IntelliJ IDEA community edition. Students signing up should be comfortable writing simple programs in Java (or a Java-like language such as C#).
This course is currently offered as Info 254. Data Mining and Analytics.
The goal of Data Mining and Analytics is to introduce students to the practical fundamentals of data mining and machine learning with just enough theory to aid intuition building. The course is project-oriented, with a project beginning in class every Thursday and to be completed outside of class by the following week, or two weeks for longer assignments. The in class portion of the project is meant to be collaborative and a time for the instructor to work closely with groups to understand the learning objectives and help them work through any logistics that may be slowing them down. Tuesdays are lecture days which introduce the concepts and algorithms which will be used in the upcoming project. The primary objective is for everyone to leave the class with hands-on data mining and data engineering skills they can confidently apply. Knowledge of basic python programming is a strong prerequisite for this course.
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.
This participatory class explores civic engagement and political activism in the information age, through the lens of technology-enabled collective action. We will focus on both the theory and real-world cases of the Internet mobilizing people by spreading alternative views and news — and the parallel emergence of collective identity and civic action. Students will read books on communication power, watch documentary films on the Arab Spring, and do case studies about US, Iran, China, and elsewhere. The class will also look into issues such as online surveillance and filtering, circumvention tools, and how repressive regimes have countered digital activism.
In addition to analytic readings, students will engage in collective knowledge-gathering and construct a resource wiki as public good. Students will do individual or group projects relating to concepts and themes discussed in this course.
This research seminar class is not limited to the graduate students in the School of Information; students from other departments on campus, including undergraduates, are welcome.
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.