Information Course Schedule spring 2016
Lower Division Courses
Foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership.
Also listed as Computer Science C8 and Statistics C8.
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According to conventional wisdom, the “information age” began just a few decades ago and promptly superseded everything that went before it.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 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 is a half-semester course, and is offered during the second half of the semester.
8 weeks - 3 hours of lecture per week
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 and for 4 units from 2012 to 2017.
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 is a half-semester course, and is offered during the first half of the semester.
7 weeks - 4 hours of lecture per week.
NOTE: Between 2011 and 2017, this course was offered for 3 units.
Delivering value to enterprises and ensuring long-term career success requires much more than pure technology skills. As information becomes increasingly strategic for all organizations, technology professionals must also develop the core business skills required to build personal brand, expand influence, build high-quality relationships, and deliver on critical enterprise projects.
Using a combination of business and academic readings, case discussions and guest speakers, this course will explore a series of critical business topics that apply both to start-up and Fortune 500 enterprises. Subjects to be explored include: communication and presentation skills, software and product development methodologies, negotiation skills, employee engagement, organizational structures and career paths, successful interviewing and CV preparation.
Note: This course is being offered on an S/U (Satisfactory/Unsatisfactory) basis and will involve reviewing and presenting updates on the School of Information final project.
Until 2015, this course was titled “Professional Skills Workshop.”
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.
This course covers the practical and theoretical issues associated with computer-mediated communication (CMC) systems. CMC includes many different types of technologies such as email, newsgroups, chat, and online games. We will focus on the analysis of CMC practices, the social structures that emerge when people use these applications, and the design and implementation issues associated with constructing CMC technologies.
We will primarily take a social scientific approach to computer-mediated communication (including research from psychology, social psychology, economics, and sociology). We will investigate questions such as: How do we represent identity and perceive others in CMC environments? How are interfaces and visualizations used in CMC to help make sense of relationships? Why do some Wikis "succeed" while others do not? How is the production of open source software such as Linux similar to (and different from) a social movement? Why are reputations useful in some online environments, and not in others? Can we really develop meaningful relationships and perhaps even love-purely through CMC?
This course was previously offered as INFO 290-12.
“Information” is a versatile word. It’s the name we attach to the age we live in, to and the technologies that define it, to the society and economy that they give rise to, and to the "revolution" that these technologies launch. It characterizes a variety of professions, activities, and social conditions (information architect, CIO, information overload, information haves and have-nots, information warfare), and not incidentally the new faculties that take “information” as their unifying focus. The word figures as a theoretical or technical term in a number of disciplines, including AI, computer science, philosophy, psychology, linguistics, economics, political science and information theory. In short, the word stands (along with its sister “data”) for a welter of social, technological and intellectual connections that seem to define a large swath of modern life.
In this class, we will not be trying to define “information” or “data” (though we’ll look at some attempts to do so). Rather we want to take the word as a point of entry to explore the connections and ideologies that it evokes. Why do people assume, for example, that the bits and bytes sitting on their hard drives are the same as the stuff that creates social revolutions and whose free exchange is necessary to the health of democratic society? (Would we make those connections if we didn’t use the word “information” to describe them?) How are the notions of information deployed by management science or artificial intelligence connected to the information theory developed by Shannon?
We’ll be taking on these questions by discussing readings both from historical periods and from a range of disciplines, focusing on the some of notions (such as “information,” “data,” “platform,” “technology,” “knowledge”) that seem to connect them.
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.
This course is also offered for undergraduate students as Info 190.
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.
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.
Students will receive no credit for C265 after taking 290 section 6 (Spring 2009 or Fall 2010; New Media 290 section 1 (Spring 2009) or New Media 290 section 2 (Fall 2010).
Also listed as New Media C265.
Special Topics Courses
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 seminar will explore the educational technology (Edtech) sector from policy, design, and legal lenses. Edtech is among the most exciting fields for personalization because such tools may enhance learning. But in practice, Edtech is often poorly implemented. An OECD report recently found that “student performance is mixed at best” from the incorporation of internet and communication technologies in the classroom. At least four different privacy regulatory regimes touch Edtech, yet enthusiasm for the field remains high, with venture funding now reaching almost $2b for the sector. This seminar, following a problem-based learning approach, will explore the Edtech field in depth. What can we realistically expect from Edtech? How can Edtech be used most efficaciously? How do we regulate student privacy and why? How can technology serve the regulatory requirements and ends of policy?
The ICTD group seminar will discuss topics of current interest in the emerging multidisciplinary field of Information and Communications Technologies and Development, or ICTD. Each semester will be focused on a particular topic or set of topics, under the direction of appropriate faculty from the I School's ICTD group. The course content will consist of paper discussions, invited lectures from both within and outside the class and a some relatively short written assignments. Students will also be responsible for presenting during at least on class session, either on their own research, ideas or on a selected set of papers relevant to the semester's chosen topic.
This course considers at the Internet of Things (IoT) as the general theme of real-world things becoming increasingly visible and actionable via Internet and Web technologies. The goal of the course is to take a top-down as well as a bottom-up approach, thereby providing students with a comprehensive understanding of the IoT: from a technical viewpoint as well as considering the societal and economic impact of the IoT.
By looking at a variety of real-world application scenarios of the IoT and diverse implemented applications, the various understandings and requirements of IoT applications become apparent. This allows students to understand what IoT technologies are used for today, and what is required in certain scenarios. By looking at a variety of existing and developing technologies and architectural principles, students gain a better understanding of the types of technologies that are available and in use today and can be utilized to implement IoT solutions. Finally, students will be given the opportunity to apply these technologies to tackle scenarios of their choice in teams of two or three, using an experimental platform for implementing prototypes and testing them as running applications. At the end of the semester, all project teams will present their completed projects.
- Based on student feedback, the class has been updated to offer more structured exercises in IoT programming that prepare students for the course project
- 2nd year MIMS students can use this class’s course project as part of their final project
This seminar will discuss topics of current interest in the multi-disciplinary field of ubiquitous sensing. The format will include paper discussions, invited lectures from both within and outside the class, and short written assignments. Students will also be responsible for presenting during at least one class session, either on their own research and ideas or on a selected set of papers relevant to the course topic.
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.)
Students will work on the full-stack web development process while applying concepts taught in INFO 202, “Information Organization and Retrieval,” which is a pre- or co-requisite for the course. Students will apply concepts and techniques for information architecture, resource description and transformation, categorization, and interaction design. Individual and team assignments will enable students to develop skills in data modeling, API design, responsive front-end design, version control, and deployment using Python, XML, jQuery and other tools and frameworks.
This course satisfies the technology requirement for the MIMS degree.
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.
The seminar explores leading-edge trends in data science and analytics at Silicon Valley and tech firms. The speakers will include executives, entrepreneurs, and researchers from leading firms.
The topics covered will include (a subset of):
- Data analytics and “Big Data”
- Machine learning and scalability
- Business analytics including online marketing and advertising, financial services and risk analytics, operational and service analytics
- Information retrieval (search)
- Information extraction
- Social networks and social media
- Healthcare analytics
- Energy analytics
The seminar will cover the types of problems being addressed in data science and analytics, the component methods and technologies being developed, and fruitful areas for research and entrepreneurial efforts.
This requires attendance and participation in the seminar series and is open to the broader student and faculty community.
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.