Information Course Schedule spring 2017
Lower Division Courses
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.
Student Learning Outcomes: Upon completion of the course, students will be able to critically assess their own work and education in the area of data science; to identify and articulate basic ethical and policy-based frameworks; and to understand the relationship between data, ethics, and society
Upper Division Courses
According to conventional wisdom, the “information age” began just a few decades ago and promptly superseded everything that went before it. But the issues we are wrestling with now—questions about piracy, privacy, trust, “information overload,” and the replacement of old media by new—all have their roots in the informational cultures of earlier periods. In this class we will take a long view of the development of these cultures and technologies, from the earliest cave painting and writing systems to the advent of print, photography and the telegraph to the emergence of the computer and Internet and the world of Twitter, Pinterest and beyond. In every instance, be focused on the chicken-and-egg questions of technological determinism: how do technological developments affect society and vice-versa?
Three hours of lecture per week. 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.
Students will receive no credit for Sociology C167/Information C167 after taking Sociology 167.
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 the study of information and information technology, from a social science perspective. As a result, this course will continuously introduce students to applied and practical problems, theoretical issues, as well as methods for answering different types of questions.
The following three questions will guide the material throughout the course: 1) Why do social scientists study information and information technology, 2) What are some of the key topics and issues that are studied, and 3) How do we study these issues? As we work our way through many different topics and problems in information, we will focus on various levels of analysis. This includes the micro (i.e., interpersonal relationships and information in small groups) to the macro level (i.e., organizational and institutional problems of information). By the end of the course, all students will be familiar with the social science approach to information and information technology, as well as many of the key problems and the methods used to solve these problems. This knowledge is essential to having a well-rounded understanding of information issues in professional environments.
NOTE: Before Fall 2016, this course was named Social and Organizational Issues of Information. The course was offered for 3 units in Spring 2010 and Spring 2011.
Three hours of lecture per week. Law is one of a number of policies that mediates the tension between free flow and restrictions on the flow of information. This course introduces students to copyright and other forms of legal protection for databases, licensing of information, consumer protection, liability for insecure systems and defective information, privacy, and national and international information policy.
NOTE: Before Fall 2010, this course was offered for 2 units.
This course addresses concepts and methods of user experience research. The emphasis will be on methods of collecting and interpreting many kinds of data about real-world user activities and practices and translating them into design decisions. The course includes hands-on practice with a number of major user experience research methods, including heuristic evaluation; observation; interviews, surveys and focus groups. The emphasis will be on naturalistic/ethnographic (qualitative) methods, but we will also address major quantitative methods. Finally, we will discuss methods of bringing user experience research into the design process.
This course is appropriate for both 1st and 2nd-year MIMS students, and for students from other departments with a strong interest in user experience research, with the instructor's permission. Students will complete at least one major group project related to needs assessment and evaluation. Second-year MIMS students may use this project to meet their capping project requirement.
Application of 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; economics of information goods, services, and platforms; strategic pricing; strategic complements and substitutes; competition models; network industry structure and telecommunications regulation; search and the "long tail"; network cascades and social epidemics; network formation and network structure; peer production and crowdsourcing; interdependent security and privacy.
Information visualization is widely used in media, business, and engineering disciplines to help people analyze and understand the information at hand. The industry has grown exponentially over the last few years. As a result there are more visualization tools available, which have in turn lowered the barrier of entry for creating visualizations.
This course provides an overview of the field of Information Visualization. It follows a hands-on approach. Readings and lectures will cover basic visualization principles and tools. Labs will focus on practical introductions to tools and frameworks. We will discuss existing visualizations and critique their effectiveness in conveying information. Finally, guest speakers from the industry will give an insight into how information visualization is used in practice.
All students are expected to participate in class discussion, complete lab assignments, and create an advanced interactive data visualization as a semester project.
Priority for attending this class is given to I School students. The semester project involves programming; therefore students are expected to have some coding experience. Interested students from other departments are invited to join the class if they can demonstrate the required skills.
Note: This course is offered for a letter grade only.
Note: Until 2014, this course was offered for 3 units.
Three hours of lecture per week. This course is concerned with the use of Database Management Systems (DBMS) to solve a wide range of information storage, management and retrieval problems, in organizations ranging from large corporations to personal applications, such as research data management. The course combines the practical aspects of DBMS use with more theoretical discussions of database design methodologies and the "internals" of database systems.
A significant part of the course will require students to design their own database and implement it on different DBMS that run on different computer systems. We will use both ACCESS and ORACLE.
In the theoretical portion of the course, we will examine the major types or data models of DBMS (hierarchical, network, relational, and object-oriented). We will discuss the principles and problems of database design, operation, and maintenance for each data model.
How does good design enhance or facilitate interaction between people? How does good design make the experience people have with computational objects and environments not just functional, but emotionally engaging and stimulating? This semester seminar 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 discussions, in addition to lectures and readings.
Also listed as New Media C265.
The goal of this course is to provide students with an introduction to many different types of quantitative research methods and statistical techniques. This course will be divided into two sections: 1) methods for quantitative research and, 2) quantitative statistical techniques for analyzing data. We begin with a focus on defining research problems, theory testing, causal inference, and designing research instruments. Then, we will explore a range of statistical techniques and methods that are available for empirical research. Topics in research methods include: Primary and Secondary Data Analysis, Sampling, Survey Design, and Experimental Designs. Topics in quantitative techniques include: Descriptive and Inferential statistics, General Linear Models, and Non-Linear Models. The course will conclude with an introduction to special topics in quantitative research methods.
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.
Special Topics Courses
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.
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.
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.
This course is cross-listed as MBA 247.
In Spring 2015 and Spring 2016, this course was offered for 2 units.
This is a 2-hour, intensive weekly discussion of current and ongoing research by Ph.D. students with a research interest in issues of information (social, legal, technical, theoretical, etc). Thus, we welcome Ph.D. students from inside and outside the I School who focus on these issues. Our goal is to focus on critiquing research problems, theories, and methodologies from multiple perspectives so that we can produce high-quality, publishable work in the interdisciplinary area of information research. We welcome a mix of older and newer Ph.D. students, which usually means we will have a mix of dissertation chapters from some and potential qualifying papers from others. For newer PhD's, a separate article or very new project idea might make more sense. No matter what you present to the group, the goal will be to compliment, critique, and suggest specific improvements. We want to have critical and productive discussion, but above all else we want to make our work better: more interesting, more accessible, more rigorous, more theoretically grounded, and more like the stuff we enjoy reading.
Currently offered as Info 294.
This one-credit reading group, sponsored by the Center for Long-Term Cybersecurity, will discuss contemporary cybersecurity policy problems. The seminar will focus on future trends in technology, as well as the economy and politics, and how those are affecting cybersecurity policy. Topics may include encryption, autonomous vehicles, and the ethics of artificial intelligence. Students would be required to attend weekly 50-minute sessions, present short papers on the readings, and write response pieces.
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.
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.
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.
Foster critical thinking about real world actionability from machine learned analytics.
Develop intuition in various machine learning classification algorithms (e.g. decision trees, neural networks / deep representation learning, support vector machines), clustering techniques (e.g. kmeans, spectral), as well as big data processing tools (e.g. map reduce).
Develop data engineering and High Performance Computing systems skills
Provide a preview of trends that will shape the need for data mining and analytics across a variety of disciplines.
(Previously offered as Info 290.)
Seminars & Colloquia
One hour colloquium per week. Must be taken on a satisfactory/unsatisfactory basis. Colloquia, discussion, and readings are 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.
The seminar explores selected advanced topics relating to 'digital libraries' with special emphasis on:
- Access to networked resources
- Use of two or more resources in conjunction
- Combined use of two or more retrieval systems (e.g. use of pre- or post-processing to enhance the capabilities)
- The redesign of library services
It is expected that these issues will require attention to a number of questions about the nature of information retrieval processes, the feasibility of not-yet-conventional techniques, techniques of making different systems work together, social impact, and the reconsideration of past practices. More generally, the seminar is intended to provide a forum for advanced students in the School. Anyone interested in these topics is welcome to join in — and to talk about their own work. This is a continuation of the previous Lynch/Buckland seminars.