Catalog: Info Courses

Lower Division

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.

A fast-paced introduction to the Python programming language geared toward students of data science. The course introduces a range of Python objects and control structures, then builds on these with classes on object-oriented programming. The last section of the course is devoted to Python’s system of packages for data analysis. Students will gain experience in different styles of programming, including scripting, object-oriented design, test-driven design, and functional programming. Aside from Python, the course also covers use of the command line, coding and presentation with Jupyter notebooks, and source control with Git and GitHub. This is an online course; students will attend regular live online sessions as well as reviewing recorded material.

This course is conducted entirely online, so students do not need to be on the Berkeley campus in order to participate. However, live session attendance via our course management software is required, so students will attend class as a group on a weekly basis. Students will also view recorded content as a supplement to the live session meetings. Exact section days and times will be announced soon.

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

Course may be repeated for credit. One to four hours of directed group study per week. Must be taken on a passed/not passed basis. Lectures and small group discussions focusing on topics of interest, varying from semester to semester.

This course explores the centrality of technology to processes of political transformation, starting from the tension between discourses of liberation and domination. We will study the interplay of computing with present struggles in the privatization of education, intellectual property, militarization, mass surveillance, labor, gender, sexuality, race, coloniality/decoloniality, and transnational activism. Questions to be addressed include: how do financial, legal, and algorithmic, and other domains of control shape global flows of information? How do old concepts in social theory (e.g., the ‘public sphere’) translate to the digital context? How can we propose technological interventions without reproducing naïve solutionism or false universalism?

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. Students who previously completed The Politics of Digital Piracy (Info 98/Info 198) will receive no credit for Discourse on Computing.

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.

Upper Division

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?

This course introduces students to natural language processing and exposes them to the variety of methods available for reasoning about text in computational systems. NLP is deeply interdisciplinary, drawing on both linguistics and computer science, and helps drive much contemporary work in text analysis (as used in computational social science, the digital humanities, and computational journalism). We will focus on major algorithms used in NLP for various applications (part-of-speech tagging, parsing, coreference resolution, machine translation) and on the linguistic phenomena those algorithms attempt to model. Students will implement algorithms and create linguistically annotated data on which those algorithms depend.

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.

How to store, process, analyze and make sense of Big Data is of increasing interest and importance to technology companies, a wide range of industries, and academic institutions. In this course, UC Berkeley professors and Twitter engineers will lecture on the most cutting-edge algorithms and software tools for data analytics as applied to Twitter microblog data. Topics will include applied natural language processing algorithms such as sentiment analysis, large scale anomaly detection, real-time search, information diffusion and outbreak detection, trend detection in social streams, recommendation algorithms, and advanced frameworks for distributed computing. Social science perspectives on analyzing social media will also be covered.

This is a hands-on project course in which students are expected to form teams to complete intensive programming and analytics projects using the real-world example of Twitter data and code bases. Engineers from Twitter will help advise student projects, and students will have the option of presenting their final project presentations to an audience of engineers at the headquarters of Twitter in San Francisco (in addition to on campus). Project topics include building on existing infrastructure tools, building Twitter apps, and analyzing Twitter data. Access to data will be provided.

Intended audience:
This course is intended for students, both graduate and undergraduate, with programming skills and an interest in analyzing data and/or building software to do so.

Prerequisites:
Undergraduates must be upper-division computer science or electrical engineering majors, or must have taken significant advanced programming courses including CS 162 and math courses including CS 70 or equivalent. Completion of a statistics course is also strongly recommended.

Graduate students must be comfortable with systems programming and be able to pick up new software programming tools with little structured support and be comfortable with basic math topics such as graph theory, statistics, and probability theory.

NOTE: This course is also offered as Info 290. Analyzing Big Data with Twitter.

Harnessing the power of “civic tech” (data, digital, design) to drive policy outcomes

From criminal justice to health care to municipal services, civic technology is transforming the public sector. Taught by experts from the California Department of Justice, this course explores the emerging disciplines of data science, digital services, and user­-centered design and their implications for government and public policy.

Data products can provide clarity on impact, helping to identify which policies and practices are working, and where interventions are most needed. Digital services that integrate data and user-centered design are helping governments a) deliver in ways that are both convenient and cost-effective for the public, b) build trust by providing transparency into and accountability for how institutions are functioning, and c) engage the civic-minded community to build ongoing constructive feedback mechanisms. On the other hand, the use of these approaches also poses unique challenges, ranging from poor data quality, to (mis)interpretation of statistics, to difficulty in conveying insights, and using those insights to transform operations and service delivery.

This course will use recent events in the criminal justice system and the California Department of Justice’s new OpenJustice initiative (http://openjustice.doj.ca.gov) as a central case study to understand the challenges and solution space for data-driven public policy. It will cover the full life cycle: from the dynamics of passing data legislation, to data collection and sharing, to data analysis (statistics, machine learning), publication (open data, visualization, dashboards), engagement strategies, and policy making.  It will explore the challenge of ensuring that data is actionable for internal and external users, that it is acted-upon, and that it actually informs and improves services and service delivery.

Overall, the goal is for students to understand the various components required to move from the limited uses of data as a “box-checking” exercise to a primary policy driver. Students will explore these topics via individual and group-based projects, including hands-on classroom assignments. The mid-term project will include a legislative proposal for new data collection and an implementation plan. The final project will include robust analysis of existing data, visualizations of findings, and an engagement plan. Data sets will be drawn from the OpenJustice portal as well as other public safety, public health and education data sources.

Knowledge of Excel is recommended; familiarity with statistical programming in Python or similar languages may be helpful but not required. Recommendations for resources to learn these skills will be provided as part of the course.

This course is intended to be 2 units but is currently awaiting Academic Senate approval to be offered with this unit count. Students may notice that the official CalCentral listing currently indicates 3 units. Check back later in August for updates.

This course aims to provide students with an overview of the many dynamic and interdisciplinary skills that are required for successful practice in the field of ICTD.

Information and Communications Technology for Development (ICTD) is the broad study of information technology to alleviate poverty and stimulate development (economic, social, and human) in developing and transitional countries. In the last 15 years, there has been an exponential expansion in the number of ICTD projects, but insufficient human skills to design and manage them, leading to a “forever-pilot” culture and a rather dismal failure rate. Successful oversight of these projects requires a combination of interdisciplinary and dynamic skills. This course serves to introduce students to these skills under three areas of competencies:

A. Contextual: Broader conceptual, policy-level frameworks of understanding the landscape of ICTD.

B. Technical: The different ways in which ICTs, through e-applications, can contribute to socioeconomic development. While specific computer skills are important, this course given its broad reach will focus on applications.

C. Management: Methods and techniques of project program planning and management, including assessment, design, funding, implementation, and evaluation.

Along with these areas, we will explore cross-cutting themes such as politics, gender, culture, and the reality of development work.

Students will be introduced to these skills through lectures and discussions (face-to face and online), as well as application to cases (possibly live consulting cases). Expect to have a lot of fun while working hard — not unlike development work in real life!

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.

Prerequisites

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.

For Computer Science Students

Those students from Computer Science who would prefer a programming component, and who would like to receive technical course requirement credits for this course should enroll in a 1 unit optional companion course that is being offered alongside this course. This companion course will teach JavaScript and d3.js for information visualization applications.

Instructor

The course instructor is Professor Marti Hearst, one of the founders of the IEEE Infovis Conference. She is also a leader in the latest wave of innovations in teaching data visualization, and co-organized high-profile events on this topic at IEEE Infoviz 2015, 2016, and 2017. Prof. Hearst is internationally known for her research in user interfaces for search and text visualization, having recently been inducted into the ACM CHI Academy.

This is a weekly one-hour seminar on the latest topics in the field of Natural Language Processing (also known as Computational Linguistics). Researchers from across UC Berkeley as well as visitors from out of town will present their recent work for discussion and feedback. Past topics have included multilingual language processing, analyzing social text, analyzing text using joint models, unsupervised morphology induction using word embeddings, deep learning of visual question answering, and unsupervised transcription of music and language.

In Fall 2016, we will meet every week, with alternating weeks consisting of discussions of readings and presentations of new research by local and visiting speakers.

Anyone is welcome to audit the course. Graduate students and undergraduates may enroll in this course for 1 unit of credit. In order to earn that unit of credit, students must write a synopsis of a research paper every two weeks, must attend at least 11 class meetings (and arrive on time), and must lead (or co-lead) at least one discussion of a research paper during the course of the semester.


This course is cross listed as Computer Science 294 and Information 290.

Free communication has changed the world, including the expectations and work and play. The class begins with the two data revolutions--the first about passively collected clicks on the web, the second about actively contributed data, as platforms like Facebook empower individuals to contribute a variety of quantitative and qualitative data (transactions, social relations, attention gestures, intention, location, and more.) With active student participation, we explore the far-reaching implications of the consumer data revolution for individuals, communities, business, and society.

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

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.

Course may be repeated for credit. One to four hours of lecture per week. Meetings to be arranged. Must be taken on a passed/not passed basis.

This course explores the centrality of technology to processes of political transformation, starting from the tension between discourses of liberation and domination. We will study the interplay of computing with present struggles in the privatization of education, intellectual property, militarization, mass surveillance, labor, gender, sexuality, race, coloniality/decoloniality, and transnational activism. Questions to be addressed include: how do financial, legal, and algorithmic, and other domains of control shape global flows of information? How do old concepts in social theory (e.g., the ‘public sphere’) translate to the digital context? How can we propose technological interventions without reproducing naïve solutionism or false universalism?

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. Students who previously completed The Politics of Digital Piracy (Info 98/Info 198) will receive no credit for Discourse on Computing.

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.

Course may be repeated for credit. Must be taken on a pass/not passed basis. Individual study of topics in information management and systems under faculty supervision.

Core

8 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.

This is a required introductory course for MIMS students, integrating perspectives and best practices from a wide range of disciplines.

NOTE: Before Fall 2017, Info 202 was offered as a full-semester course for 4 units.

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.

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.

NOTE: Before Fall 2017, Info 206 was titled “Distributed Computing Applications and Infrastructure” and was offered as a full-semester course for 4 units.

General

Three hours of lecture per week. User interface design and human-computer interaction. Examination of alternative design. Tools and methods for design and development. Human- computer interaction. Methods for measuring and evaluating interface quality.

This course covers the design, prototyping, and evaluation of user interfaces to computers which is often called Human-Computer Interaction (HCI). It is loosely based on course CS1 described in the ACM SIGCHI Curricula for Human-Computer Interaction (Association for Computing Machinery, 1992).

HCI covers many topics including:

  1. Human capabilities (e.g., visual and auditory perception, memory, mental models, and interface metaphors);
  2. Interface technology (e.g., input and output devices, interaction styles, and common interface paradigms); and,
  3. Interface design methods (e.g., user-centered design, prototyping, and design principles and rules), and interface evaluation (e.g., software logging, user observation, benchmarks and experiments).

This material is covered through lectures, reading, discussions, homework assignments, and a course project. This course differs from CS 160 primarily in two ways:

  1. There is an emphasis on interfaces for information technology applications; and,
  2. There is less emphasis on programming and system development, although some simple prototyping (for example, in visual basic or using JAVA GUI development tools) may be required. (CS 160 has a big programming project.)

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.

This course will survey results in computer security, cryptography, and privacy, with an emphasis on work done in the last 3 years. Student projects (creative work, demonstrations, or literature reviews) will form a substantial portion of the course work.

This course focuses on managing people in information-intensive firms and industries, such as information technology industries. Students who seek careers in these industries will soon be asked to manage people, teams, departments, and units. They need to learn how to manage. However, managing is sometimes very different in these settings: Employees are highly educated; work is more fluid; teamwork and collaboration are essential; and external situations and strategies change rapidly. For these reasons several management principles born in a traditional manufacturing era no longer apply. In particular, the old style of “command and control” needs to give way to more distributed ways of work, with significant consequences for how managers need to manage. Of course, some universal management principles apply no matter what circumstance.

While we will cover these universal management principles in this course, we will pay particular attention to management issues that are highly relevant in information-intensive settings. Topics to be covered will likely include: managing knowledge workers; managing teams (incl. virtual ones); collaborating across disparate units, giving and receiving feedback; managing the innovation process (incl. in eco-systems); managing through networks; and managing when using communication tools (e.g., tele-presence). The course will rely heavily on cases as a pedagogical form.

This course satisfies the Management of Information Projects & Organizations requirement.

"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.

Previously offered as Info 290. Applied Behavioral Economics for Information Systems.

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.

The introduction of technology increasingly delegates responsibility to technical actors, often reducing traditional forms of transparency and challenging traditional methods for accountability. This course explores the interaction between technical design and values including: privacy, accessibility, fairness, and freedom of expression. We will draw on literature from design, science and technology studies, computer science, law, and ethics, as well as primary sources in policy, standards and source code. We will investigate approaches to identifying the value implications of technical designs and use methods and tools for intentionally building in values at the outset.

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.

Provides a theoretical and practical introduction to modern techniques in applied machine learning. Covers key concepts in supervised and unsupervised machine learning, including the design of machine learning experiments, algorithms for prediction and inference, optimization, and evaluation. Students will learn functional, procedural, and statistical programming techniques for working with real-world data.

This course is a survey of Web technologies, ranging from the basic technologies underlying the Web (URI, HTTP, HTML) to more advanced technologies being  used in the the context of Web engineering, for example structured data  formats and Web programming frameworks. The goal of this course is to provide an  overview of the technical issues surrounding the Web today, and to provide a  solid and comprehensive perspective of the Web's constantly evolving landscape.

Students will receive no credit for 253 after taking 290. Web Architecture.

This course introduces students to 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 week 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, with the instructor working closely with groups to understand the learning objectives and help them work through any logistics that may be slowing them down. Weekly lectures introduce the concepts and algorithms which will be used in the upcoming project. Students leave the class with hands-on data mining and data engineering skills they can confidently apply.

Course Objectives

  • Develop data engineering and High Performance Computing systems skills.
  • 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).
  • Foster critical thinking about real world actionability from machine learned analytics.
  • Provide a preview of trends that will shape the need for data mining and analytics across a variety of disciplines.

Previously offered as Info 290T. Data Mining and Analytics.

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.

Restrictions for non–I School students interested in taking Info 256.

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.

This course introduces students to natural language processing and exposes them to the variety of methods available for reasoning about text in computational systems. NLP is deeply interdisciplinary, drawing on both linguistics and computer science, and helps drive much contemporary work in text analysis (as used in computational social science, the digital humanities, and computational journalism). We will focus on major algorithms used in NLP for various applications (part-of-speech tagging, parsing, coreference resolution, machine translation) and on the linguistic phenomena those algorithms attempt to model. Students will implement algorithms and create linguistically annotated data on which those algorithms depend.

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.

NOTE: This course is cross-listed as Education C260F. Machine Learning in Education.

(Previously offered as Info 290 & Educ 290A.)

This course will explore the theory and practice of Tangible User Interfaces, a new approach to HCI which focuses on the physical interaction with computational media. The topics covered in the course include:

  • Theoretical framework of Tangible User Interfaces
  • Design examples of Tangible User Interfaces
  • Enabling technologies for Tangible User Interfaces

Students will design and develop experimental Tangible User Interfaces, applications, underlying technologies, and theories using concept sketches, posters, physical mockups, working prototypes, and a final project report. The course will have 3 hours of lecture and 1 hour of laboratory per week.

Note:  Previously listed as Info 290: Theory and Practice of Tangible User Interfaces. Students who completed INFO 290 section 4 in Fall 2008 will receive no credit for Info 262.

This course is cross-listed as New Media C262.

How does the design of new educational technologies change the way children learn and think? Which aspects of creative thinking and learning can technology support? How do we design systems that reflect our understanding of how we learn? This course explores issues in designing and evaluating technologies that support creativity and learning. The class will cover theories of creativity and learning, implications for design, as well as a survey of new educational technologies such as works in computer supported collaborative learning, digital manipulatives, and immersive learning environments.

This course was previously offered as as Info 290.

Also listed as New Media C263.

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 course will focus upon the use of qualitative methods for research about information technologies. Methods including interviewing, focus groups, participant observation and ethnography will be taught and practiced. Significant qualitative research findings about the social impact of information technologies will be read, to analyze what we know about IT thus far, how we know it, and as models of theories and methods for future research. Frequent field exercises will be assigned to develop qualitative research skills and best practices, but the primary assignment will be to engage in a substantial fieldwork project. Methods covered will include video if grant support or other budget resources are found.

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

In this course, we will survey a broad range of “alternative” thinkers — including, but not limited to, Mahatma Gandhi, Rabindranath Tagore, Ivan Illich, Paulo Freire, and E.F. Schumacher — and try to derive some potential implications of each of their ideas for the design and use of technological artifacts. For each, we will try to understand their perspectives on technology, society, and human development and the underlying values that drive these perspectives, and to apply these values to practical design considerations. The course will consist of weekly readings, discussion, and regular design activities.

How to store, process, analyze and make sense of Big Data is of increasing interest and importance to technology companies, a wide range of industries, and academic institutions. In this course, UC Berkeley professors and Twitter engineers will lecture on the most cutting-edge algorithms and software tools for data analytics as applied to Twitter microblog data. Topics will include applied natural language processing algorithms such as sentiment analysis, large scale anomaly detection, real-time search, information diffusion and outbreak detection, trend detection in social streams, recommendation algorithms, and advanced frameworks for distributed computing. Social science perspectives on analyzing social media will also be covered.

This is a hands-on project course in which students are expected to form teams to complete intensive programming and analytics projects using the real-world example of Twitter data and code bases. Engineers from Twitter will help advise student projects, and students will have the option of presenting their final project presentations to an audience of engineers at the headquarters of Twitter in San Francisco (in addition to on campus). Project topics include building on existing infrastructure tools, building Twitter apps, and analyzing Twitter data. Access to data will be provided.

Intended audience:
This course is intended for students, both graduate and undergraduate, with programming skills and an interest in analyzing data and/or building software to do so.

Prerequisites:
Undergraduates must be upper-division computer science or electrical engineering majors, or must have taken significant advanced programming courses including CS 162 and math courses including CS 70 or equivalent. Completion of a statistics course is also strongly recommended.

Graduate students must be comfortable with systems programming and be able to pick up new software programming tools with little structured support and be comfortable with basic math topics such as graph theory, statistics, and probability theory.

NOTE: This course is also offered as Info 190. Analyzing Big Data with Twitter.

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.

The mobile landscape is constantly changing — new devices, new operating systems, new applications. Even seasoned designers are overwhelmed by the chaos, sometimes creating less than optimal designs that are soon outdated. But the successful designs, the ones that surprise and delight their users, look beyond the here and now.

Designing Mobile Experiences will start with an overview of current device and OS differences, featuring guest lecturers with deep Android and iOS expertise. The second part of the course will lay the foundation to create mobile application designs that can truly stand the test of time. Some of the topics we’ll cover include: exploratory mobile research, gesture design, and touch design. The latter part of the course will introduce ways to bring apps to life through animation, sound, and prototyping.

Course material will be covered through lectures, in class activities, readings, and a group project. Early in the semester students will pitch mobile application ideas; they will spend the rest of the term iteratively designing the app with their teammates. Ongoing design critiques will be provided by the instructor, classmates, and industry leaders.

Priority for attending this class is given to I School students. Programming mobile applications will not be covered in the course.

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?
 

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.

This course addresses the fast-growing area of social and environmental measurement using technologies such as mobile devices, "Internet of Things" (or "Web of Things") style sensors, and remote sensing. We will take a project-based approach, with a classroom discussion each week, followed by a tutorial / practicum in BIDS. Note that the focus of this course is on data collection and management. Teams will likely do some basic visualization and exploratory data analysis; statistics and/or machine learning are not expected. We will leverage support from the Social Science Matrix, the D-Lab, BIDS, CEGA, and Berkeley Research Computing to provide necessary training, hardware, and compute resources. This course is being offered as a part of the Data Science Collaborative at the Berkeley Institute of Data Science (BIDS).

Class-entry code (CEC) required for enrollment. Prospective students must show up of the first day of class; CECs will be distributed after project teams are formed.

Course enrollment is limited to 25 students. In the event of over-enrollment, admission to the course will be based on the quality of the team and the fit with the semester’s projects.

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.

The purpose of this course is to provide a health care industry context for information systems as an important element for student work in
systems design and evaluation. Specifically, I hope to:

  • Provide an overview of issues and trends which will shape the need for and structures of information systems within health care:demographic, epidemiological, social and technologic
  • Identity and explore key topics in health care information systems: background, issues, examples, implications for future development

 

NOTE:
This course was previously offered as Info 290A: Information Systems and Health Care. May not be taken for credit if student has previously taken Info 290A: Information Systems and Health Care.

(In Spring 2011, this course was offered for 1–2 units.)

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.

NOTES:

  • 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

 

The internet — as a global, "always-on" platform — poses unique challenges to legal and political frameworks premised on territorial jurisdiction. Operating in this global marketplace exposes companies, and sometimes individuals, to conflicting normative, legal and political commitments. Through case studies, this course considers the options in (i) developing technologies and business strategies to address the varied, and sometimes competing, laws of different countries; (ii) amending laws and otherwise engaging in policy development for the global internet; and (iii) explaining these choices and limitations to regulators, business partners and users. It will consider the implications of these various strategies on an issue-by-issue basis in the areas of content regulation, intellectual property, information security, and privacy, and explore the cross-cutting consequences and dependencies between choices in these various issue areas.

In this seminar we will investigate the frontier of the concept of leadership by exploring how data analytics can help leaders develop and perform better, and enable stakeholders to better track and govern the conduct of leader behaviors.

Data analytics is making inroads into all areas of the economy, including management. Yet the practice of leadership is still seen as an art, not a science. As a result, research on leadership has been restricted by limited data and limited perspectives. That may change with advances in data analytics. Yet that brings up fascinating issues such as can leadership ever be reduced to data analysis? And, if it can, should it be?

In particular, we will explore the concept of leadership development through the lens of analytics by trying to answer the following question: How can smarts “apps” help leaders practice leadership competencies, get feedback on their behaviors, and modify their behaviors in order to improve?

Topics to be included: leadership development, theories of personal development, “nudging” theory, deliberate practice theory, social feedback technology, and the examination of existing "apps" and online tools that can be applied to leadership analytics.

This workshop will be a discussion seminar, where we will all prepare the material for the class and discuss it as a group.

Visual and multi-modal media are central to much of what we do in the I School and related disciplines. Data collection, reports, and presentations, face-to-face and distant, online and off, often rely heavily on visual and audio media. Because we are a media-literate society, with accessible hardware and software plus easy online distribution, it seems that everyone “knows" how to make and critique such media. However, our knowledge about how to effectively make, use, and present these media trails far behind our ability to create hours and gigabytes of content. Furthermore, it’s useful to consider how these resources are changing not just professional and research practice.

In this seminar, we will address both theoretical and practical issues of capturing and creating narratives with video, audio, and still images. We will draw on photojournalism, visual narrative, visual anthropology, visual studies, and related areas. We will get hands-on experience creating and editing our own media. This is not a technical course; nor is it a media production how-to. But you will get experience with media technologies while we reflect on them with the help of theoreticians and scholars in relevant areas.

This course is relevant to students in professional schools and to doctoral students interested in and qualitative research, including user experience research; technology designers who produce video scenarios and concept videos; and anyone concerned with collecting and presenting information via multiple media.

No prior experience is necessary, but students who are already grappling with visual (and audio) media will find this course especially useful. I School students are likely to find this course useful for the doing and presenting of final projects.

This is a weekly one-hour seminar on the latest topics in the field of Natural Language Processing (also known as Computational Linguistics). Researchers from across UC Berkeley as well as visitors from out of town will present their recent work for discussion and feedback. Past topics have included multilingual language processing, analyzing social text, analyzing text using joint models, unsupervised morphology induction using word embeddings, deep learning of visual question answering, and unsupervised transcription of music and language.

In Fall 2016, we will meet every week, with alternating weeks consisting of discussions of readings and presentations of new research by local and visiting speakers.

Anyone is welcome to audit the course. Graduate students and undergraduates may enroll in this course for 1 unit of credit. In order to earn that unit of credit, students must write a synopsis of a research paper every two weeks, must attend at least 11 class meetings (and arrive on time), and must lead (or co-lead) at least one discussion of a research paper during the course of the semester.

This course is cross listed as Computer Science 294 and Information 190.

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.

Information privacy law profoundly shapes how internet-enabled services may work. Privacy Law for Technologists will translate the regulatory demands flowing from the growing field of privacy and security law to those who are creating interesting and transformative internet-enabled services. The course will meet twice a week, with the first session focusing on the formal requirements of the law, and the second on how technology might accommodate regulatory demands and goals. Topics include:  Computer Fraud and Abuse Act (reverse engineering, scraping, computer attacks), unfair/deceptive trade practices, ECPA, children’s privacy, big data and discrimination (FCRA, ECOA), DMCA, intermediary liability issues, ediscovery and data retention, the anti-marketing laws, and technical requirements flowing from the EU-US Privacy Shield.

Required textbook: FTC Privacy Law and Policy (CUP 2016)

There are few terrains that evoke such socio-political anxieties and ambitions as the human body. Our contemporary moment is characterized by a shift in who can understand, analyze and ‘hack’ the body. Consumer biosensors, citizen science movements such as biohacking, and patient power movements such as participatory medicine, all use technology to challenge the status quo around who can demonstrate expertise about the body. They bring both opportunities and responsibilities. How do we engage with this computational turn that now marks the everyday experience of our biology?

In this course, we will take a close look at critical debates and scholarship about the body. We will draw from art, design, theory and activism. This course will introduce and ground itself in the philosophies of critical technical practice and reflective design. That is, not only will we examine the various ways in which society conceives of the body, through the lens of data, posthumanism, nation, race, gender and dis/ability, we will also unpack how we have been personally formed by those very debates and influences. What underlying values and assumptions do you bring to engaging the body through technology? What kinds of norms are you reinforcing with your actions? What would you like to change?

This course will culminate in a set of group projects, all in conversation with each other, that we will publish online in service to a public audience. Each group project will offer a critical and reflective perspective on a theme of your choosing. You will choose a trajectory most suitable to your learning and communication preference, from three tracks:

  1. Unpack a narrative about the body with a curated digital archive/gallery of representations (text/video/image/sound).

  2. Articulate reflective and critical perspectives in written essay format.

  3. Design a speculative, critical or reflective digital artifact to challenge existing values and assumptions.

Pedagogical Priorities

Depending on your own prior experience, this class will begin, extend or support the following skills.

Intersectionality: Analyze how concepts of the body are interconnected with other systems of power. These could include, but are not limited to gender, age, ability, literacy, race, and membership in privileged knowledge institutions (universities, scientific labs, medical institutions). Demonstrate how these categories are mutually constituted and intersect with different technological engagements with the body.

Critical self-awareness: Demonstrate self-reflexivity about one’s values, ideas and goals, and how they are connected to one’s own body status and socio-economic position.

Engaged Practice: Explore how to advocate for differences in bodies, identities, marginalized communities, and non-normative practices. Understand the ways in which knowledge institutions are assigned legitimacy. Learn to recognize and support other non-traditional and diverse ways of producing knowledge about the body that are also valuable.

Creativity: Synthesize diverse perspectives, the aesthetics of writing/imagery/sound/touch, and activism to engage with issues of the body in a manner that is imaginative, inspiring and generative.

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.

Topics and goals overview:

Exploratory data analysis is an approach to examining data that emphasizes visually describing and interactively and iteratively inspecting data. EDA is the first step in data analysis, prior to performing confirmatory statistical analysis (such as conducting statistical tests or fitting statistical models; this topic is taught in Info 271B Quantitative Research Methods, which is a terrific complement to this course). This distinction between exploratory and confirmatory statistics was originally championed by mathematical pioneer John Tukey, who said of EDA, “1. It is an attitude, and  2. a flexibility, and  3. some graph paper.”

Exploratory data analysis should be conducted before other types of analysis, in order to:

  • evaluate data quality and identify additional data to collect, if necessary,
  • suggest questions and hypotheses to pursue, or
  • assess assumptions on which later analysis will be based.

Exploratory data analysis techniques include:

  • visualization techniques (histograms, scatter plots, parallel coordinates, etc.)
  • projection methods (principal component analysis, multidimensional scaling, projection pursuit, t-SNE, etc.)
  • unsupervised machine learning (clustering, pattern mining, anomaly detection, etc.)

One challenge is that while there are a multitude of tools for data exploration, there is no established systematic understanding of or rules for guiding such exploration. Instead, data analysts learn how to do this work by slow trial and error or in an apprentice model from other analysts. Therefore, guidelines are needed to allow measurement of the amount of progress made in exploring a data set, to ensure complete coverage in exploration, to allow different sets of people to collaborate in exploring a data set individually and later combine their results, and to develop automated and intelligent assistance algorithms for data analysis interfaces.

Students in this course will expand their knowledge of and practice with exploratory data analysis techniques and at the same time will develop a repository of EDA case studies to be used to further our understanding of the EDA process. The first part of the course will consist of developing data sets and scenarios of use that can be used as examples, both for instruction and research, of best practices for EDA. The last few weeks of the course will be to help convert those examples into a systematic framework or theoretical model that characterizes the EDA process or processes, in order to guide future practice as well as to inform the design of new interactive data analysis tools.

Students will be expected to work together in teams and with the instructor to reach these goals. This is a research seminar, therefore students must be comfortable with open-ended problems, self-directed work and with setting their own goals.

The primary EDA tool used will be Tableau, but other programming abilities will be needed, e.g., for parsing and analyzing the Tableau log files, for wrangling data sets to get them into the right format, and so on. Students who are interested in the more analytic side of EDA (projection methods, clustering, etc) and who already have background in this area will be allowed to work on these problems, but must come to the course with strengths in those methods, as they will not be the focus of classroom work.

Requirements:

Course is open to graduate students from all fields, at discretion of the instructor. Students should have taken either:

  • Info 247 (Information Visualization and Presentation), or
  • CS 294-10 (Information Visualization)

Students will be expected to have:

  • Enjoyed the EDA aspects of their infoviz course
  • Familiarity with Tableau
  • Proficiency in programming and the use of software engineering tools like the unix command line, databases, version control, some scripting language (Python, MATLAB or R will be useful)
  • Ability to comfortably pick up new programming languages and software tools with minimal guidance

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.

Just as the web browser brought us click-stream data and the mobile phone brought us geo-location data, ubiquitous low-cost sensors integrated with wearable and Internet-of-Things devices will bring us a new torrent of user data to collect, analyze, and exploit. The course takes a hands-on approach to exploring the possibilities and limitations of consumer-grade sensing technologies for physiological and contextual data.

We will survey the intellectual foundations and research advances in ubiquitous computing, physiological and affective computing, with applications in health and wellness, social computing, information security, novel user interfaces, etc. We will cover temporal and spectral techniques for time-series data analysis. We will consider data stewardship issues, including data ownership, data privacy, and research ethics. The class lending library will provide access to a variety of devices that can be used for data collection and application prototyping.

Project work can be undertaken in a variety of application domains, such as affective computing, ambient assisted living, biometric authentication, privacy by design, quantified self, smart cars and homes, social robotics, and virtual and augmented reality.

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.

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

Digital technologies have brought consumers many benefits, including new products and services, yet at the same time, these technologies offer affordances that alter the balance of power among companies and consumers. Technology makes it easier to deny consumers access to the courts; to restrict well-established customs and rights, such as fair use and the reselling of goods; to manipulate digital fora that provide reviews of products and services; to retaliate against and/or monitor or even extort consumers who criticize them; to engage in differential pricing; to “brick” or turn off devices remotely, to cause systemic insecurity by failing to patch products; and to impose transaction costs in order to shape consumer behavior.

Fundamentally, the move to digital turns many products into services. While the law has long comprehensively regulated products under the Uniform Commercial Code and products liability regimes, artifacts and services with embedded software present new challenges. European governments are moving aggressively to establish comprehensive regulations for digital goods. But no such agenda is on the horizon in the United States.

This course will employ a problem-based learning method (PBL). Students in the course will work in small groups to generate hypotheses, learning issues, and learning objectives in digital consumer protection. Through this process we will develop a high level conception of consumer protection and its goals. We will then explore its fit in the digital realm.

Students will develop short presentations on these learning objectives to create group learning and discussion. For the culmination of the course, students will work together to generate a research agenda for the future of digital consumer protection.

This is a hands on course that will address two major challenges associated with the current shift from text-based to e-books: making them more engaging and informative through use of the capabilities of the medium, and identifying and analyzing the issues surrounding the collaborative authoring and usage of e-books in an educational context.

Course may be repeated for credit, as new issues will be explored.

(In Fall 2012, this course was offered for 1 unit.)

Much as Adam Smith saw his own age as marked by its engagement with “commerce” and thereby distinguished from all ages that had come before, it has become conventional to see our own era as a break from all that has preceded it, and thus distinguished principally by its engagement with information and computing technologies. Scholars have labeled the contemporary era as the “post-industrial,” “postmodern,” or “network society,” but probably the most widely used and enduring characterization distinguishes the present day as the “information age” or “information society.” This course will explore the notion of an “information society,” trying to understand what scholars have held to be the essential and distinguishing features of such a society, how these views compare with classic theories of society or with alternative accounts of the present age, and to what extent different conceptions of the “information age” are compatible. In pursuing this investigation, we shall bear in mind the admonition of the legal scholar James Boyle that while the idea of an “information age” may be “useful ... we need a critical social theory to understand it.” In the process of developing a critical, social, and political-economic analysis of this idea, we hope to assemble a corpus of information society readings.

Requirements: We will proceed by reading theorists in contrasting pairs in each class seeking to understand and compare the ways in which society is characterized in each account. All students will be asked to post their thoughts on the readings before each class. Each student will be expected to take responsibility for guiding the discussion in one class. Every few weeks, we will pause to consider how to apply these theoretical perspectives in our own research and writing, at which time students will be required to submit a one- page essay reflecting on how the works read up to that point construct and use theory and how the student might rely on or challenge these theories in the student’s own work. Each student will be required to submit a final paper exploring a subject to be agreed on with the instructors that relates the works and discussions of the course to the student’s own work and interests. Papers should be twenty pages long and submitted by the last day of exam week (December 16).

How do you create a concise and compelling User Experience portfolio? Applying the principles of effective storytelling to make a complex project quickly comprehensible is key. Your portfolio case studies should articulate the initial problem, synopsize the design process, explain the key decisions that moved the project forward, and highlight why the solution was appropriate. This course will include talks by several UX hiring managers who will discuss what they look for in portfolios and common mistakes to avoid.

Students should come to the course with a completed project to use as the basis for their case study; they will finish with a completed case study and repeatable process. Although this class focuses on UX, students from related fields who are expected to share examples and outcomes of past projects during the interview process (data science, product management, etc.) are welcome to join.

Health and health care have profound impact on a society's well being and economic productivity. Health care reform and ongoing economic forces are placing unprecedented pressure on the health care system to provide consumers and payers with value. Patients, purchasers, regulators, and other key stakeholders are demanding that care be readily accessible, proactive, and focused on improving health while containing costs. The health care system, policy makers, and key stakeholders are responding by developing new care models that focus on patient and customer centricity, novel information practices, and the seamless integration of care.

Following a review of the current trends in health care, the course will explore the relationship between health care and the information economy. We will also delve into information strategies being utilized by health care providers, patients, payers, and other key stakeholders to improve care while controlling costs. Health care leaders from Kaiser Permanente will serve as guest lecturers, providing tangible perspective to our discussions.

This course will explore the make-up of the healthcare industry, how healthcare players set strategy, the impact of system design on healthcare strategies, and the implications of these strategies on the future of the healthcare sector and society more broadly. The first two-thirds of the course will examine strategy in the US context. The last third will explore how different international models of healthcare influence strategy, and what these might mean for the future of US healthcare as well.

Mass communications technologies have been profound influencers of human identity, from the printing press and the rise of vernacular political cultures to television and the power of celebrity. While the Web is still a work in progress, salient characteristics such as the collapse of distance, the discovery of like-minded groups, and information delivered in short bursts are already affecting the way people see themselves and the way they consume information. Following an overview on the relationship of technology with identity and communications, the course will look at the uses of narrative in news, public relations, advertising, entertainment, and online gaming.

News, online, movies, advertising, television, mobile, videogames, music, books, social media — all part of the industry of informing and entertaining, and all being revolutionized. In this course we will do a quick overview of the media business — from startups to global conglomerates.

We will address a wide range of topics: the economics of media organizations (and industries), their organizational structures, cultures, brands, and approaches.

Some of the questions we'll discuss:

  • ­How do traditional media address changing technologies?
  • ­How is the media business driven by metrics and data? How is it driven by artistic creativity?
  • ­Are media companies too big? Are they too small?

Students will present strategies for media companies, hear from guest speakers, and discuss the transformations happening in media. Students should expect to have significant input into the companies and topics we discuss.

We will make every attempt to avoid predictions about the future; we might occasionally succeed.

Note: This course is cross-listed in the Haas School of Business.

May not be taken for credit if student has previously taken Info 290: Media, New and Otherwise.

Free communication has changed the world, including the expectations and work and play. The class begins with the two data revolutions--the first about passively collected clicks on the web, the second about actively contributed data, as platforms like Facebook empower individuals to contribute a variety of quantitative and qualitative data (transactions, social relations, attention gestures, intention, location, and more.) With active student participation, we explore the far-reaching implications of the consumer data revolution for individuals, communities, business, and society.

This course was previously offered for 1 unit; in Fall 2016, the course increased to 2 units.

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.

This short seminar will explore differences among theoretical perspectives by asking:

  • What does it mean to “have a theoretical perspective?”
  • How do you come to recognize different theoretical stances as you read and consider the work of others?
  • What are the implications of those differences for scholarly work and social engagement with the world?

One way to take up these questions is to look closely at how scholars/researchers approach the same problem from different perspectives. The course will take as a central text for this examination Understanding Practice: Perspectives on Activity and Context (Chaiklin and Lave, editors). All researchers in this collection of ethnographic studies address issues about learning, knowledge and social practice. The challenge for the seminar is to inquire into the theoretical stances that permeate these projects — similarly and differently. This will involve attempting to answer the seminar questions as we go along. Besides being more acute readers of academic work by the end of the seminar, students will have (we hope) a hands-on grasp of the craft of social theorizing, and an introduction to contrasting theories of learning, knowledge, context, and practice.

The Applied Data Analytics Project course offers students a chance to complete a data analytics project for a real client using real data - to develop impactful solutions to the client’s business challenge.  MBA students from Haas will work on teams with data science focused graduate students from UC Berkeley’s School of Information, with support from Accenture’s big data group. Together you will take on a data-driven project, focused on solving a challenging issue for one of Accenture’s clients. Your team will take the challenge from data assessment and problem definition through to final client recommendations.  The outcome of your project should be a set of strategic and tactical recommendations to increase the client’s effectiveness. 

Successful analytics projects require managerial discipline, iterative problem solving skills, a solid grounding in the client’s business (whether an internal or external client) effective communications with both team and client, and data analytics tools and techniques  — including data set analysis, modeling, interpretation, and presentation.  The primary objective of this course — and of the projects — is to gain valuable experience in applying the approaches, skills, and tools needed to have an impact on business results through the use of data analytics.

This course is cross-listed as MBA247.1B.

Notes:

  • The course runs 8 weeks, starting October 19.
  • The focus is on the project based application of data to drive a client’s business decision making. We will not be teaching the technical foundations or practical software for data analytics:  Students are expected to contribute prior knowledge either in hard data analytics skills or strategic analysis of business problems using data-driven frameworks.   As a result, we require that students have completed the basic data course (Data Science and Data Strategy). Equivalent work experience or prior background may also be accepted.  Please contact the Haas@Work program office for further details
  • For each project, there will be 2-3 formal client workshops/presentations outside of normal Monday evening class hours, in addition to the normal outside work and coordination needed to manage your client, the deliverables, and your team responsibilities.
  • Once assigned a project, you may be required to sign an NDA and IP waiver
  • As is the case with many of the Experiential learning courses,  we need to make early client commitments and share information in advance of the  course. Therefore, please note that there is no add/drop period for this course.

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.

NOTE: This course was previously offered as 290. Information-Centric Entrepreneurship & Startup Strategies.

This course is designed to give participants a practical overview of the modern lean/agile product management paradigm based on contemporary industry practice. We cover the complete lifecycle of product management, from discovering your customers and users through to sales, marketing and managing teams. We'll take an experimental approach throughout, showing how to minimize investment and output while maximizing the information we discover in order to support effective decision-making. During the course, we'll show how to apply the theory through hands-on collaborative problem-solving activities. There will also be guest lectures from industry experts.

This course satisfies the Management requirement for the MIMS degree.

In Fall 2015 & Fall 2016, this course was offered for 2 units.

What does it take to deliver a successful analytics project? Implementing the right method to analyze data is just one ingredient. Analytic project success is often determined by the framing of the problem, the selection of the data sources, the composition of the analytics team, and communication of results. While there are many books and courses that cover modeling, there are fewer opportunities for students to learn about non-modeling topics, where the bulk of analytic project time is often spent. This course is designed to fill this gap. We using a decision-based framework based on experience from a consulting perspective, talking about analytics with clients and delivering analytics-related engagements. The classes will make extensive use of case studies and discussions to illustrate the concepts, and students will also work in small groups to define, implement and present the results of an analytics project. Along the way students will learn how to:

  • Incorporate vertical industry and horizontal process knowledge when gathering requirements
  • Create an analytics project plan, including budgeting and staffing
  • Select a cost-effective combination of internal and data sources
  • Design analytic interfaces and visualizations for a variety of users
  • Assist organizations with strategies to evolve their analytics capabilities

This course is a hands-on exploration of the theory and practice of open online collaboration. Students will engage multi­disciplinary literature about collaboration while contributing to an existing open project (such as open source software, Wikipedia, or OpenStreetMap). Readings will explore business models for open source software organizations, incentives of cooperation and organization design for open source projects. Practical work will be organized around themes of project management infrastructure, community self-governance, and engineering education through open source participation. The goal of the class is to engage students in an existing open source community while developing functionality and expertise that can be part of masters final projects, faculty-­directed research, and beyond.

Course may be repeated for credit as topics in management vary. One to four hours of lecture per week; two to seven and one-half hours of lecture per week for seven weeks. Specific topics, hours, and credit may vary from section to section and year to year.

Delivering value to enterprises and ensuring long-term career success requires much more than pure technology skills. This course is an industry practitioner’s view of how, as information becomes increasingly strategic for all organizations, technology professionals can accelerate their career trajectories by identifying and beginning to develop a core set of strategic business skills.

This course will explore a series of critical business topics that apply both to start-up and Fortune 500 enterprises. This course is divided into three primary sections, delivered through a series of industry thought leadership and academic readings and industry guest speakers:

  • Examining business models and strategies: How do companies plan to succeed? What are their business strategies and how do those translate into technology strategies and investments in support of these plans? Secondly, how does one analyze whether an organization’s culture is enabling or inhibiting that success?

  • Interacting with SF bay area technology executives: Students will have access to C-level executives in an intimate classroom setting as they discuss their organizational strategies, cultures and technology styles. How do they trade off speed, quality and features? How do they manage innovation when they also must operate? Currently scheduled speakers for Fall 2017 include the CIO’s from Kaiser Permanente, CBS Interactive, Red Hat, as well as executives from consulting firm AT Kearney and Silicon Valley start-up Altia Systems.

  • Enhancing core business skills: Presentation skills, negotiations, leadership styles, organizational change, personal brand and future career vision are topics that will be explored in class and in written assignments. A brief presentation will be required from all students.

Note: This course may be taken on an S/U (Satisfactory/Unsatisfactory) basis.

It takes critical thinking, outstanding leadership, and a little magic to be a successful project manager. Come and learn not only the essential building blocks of project management, but the tricks to managing a variety of complex projects. We will have a combination of interactive lectures, guest speakers, and case studies discussions to  cover globally recognized standards, best practices and tools that successful project managers use.

This course satisfies the Management requirement for the MIMS degree.

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#).

To what extent can a machine know the inner workings of a person's mind, even theoretically? This course explores this question through a mixture of hands-on machine learning and critical discussions on theory. In this course, students will practice ML techniques on a provided corpus of data to produce a working brain-computer interface. Simultaneously, students will engage critically with recent research in ubiquitous sensing technologies, and the discourse around them, tracing ideas to their origins in cognitive science.

This half-semester course runs for the first eight weeks of the semester (8/23/17 - 10/17/17).

Each week will cover one topic in mind-reading machines. Tuesday classes will be a lecture, a survey of the week's readings, centering around one or two particular papers. Thursday classes will be lab-time, centered around supporting assignments, projects and hands-on engagement with the course dataset.

This class is a pre-requisite for Info 290T. Projects on Mind-Reading Machines, an (optional) 1-unit course taking place in the second half of the semester, which would continue the themes of this course through a student-led research project.

To what extent can a machine know the inner workings of a person's mind, even theoretically? This course explores this question through a mixture of hands-on machine learning and critical discussions on theory. In this course, students will practice ML techniques on a provided corpus of data to produce a working brain-computer interface. Simultaneously, students will engage critically with recent research in ubiquitous sensing technologies, and the discourse around them, tracing ideas to their origins in cognitive science.

This 1-unit course takes place in the second half of the semester, continuing the themes of Info 290T. Mind-Reading and Telepathy for Beginners and Intermediates through a student-led research project.

Course may be repeated for credit as topics in technology vary. One to four hours of lecture per week; two to six hours of lecture per week for seven weeks. Specific topics, hours, and credit may vary from section to section and year to year.

Open data — data that is free for use, reuse, and redistribution — is an intellectual treasure-trove that has given rise to many unexpected and often fruitful applications. In this course, students will 1) learn how to access, visualize, clean, interpret, and share data, especially open data, using Python, Python-based libraries, and supplementary computational frameworks and 2) understand the theoretical underpinnings of open data and their connections to implementations in the physical and life sciences, government, social sciences, and journalism.

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

Course may be repeated once. Must be taken on a satisfactory/unsatisfactory basis. This is a zero-unit independent study course for international students doing internships under the Curricular Practical Training program. The course will be individually supervised and must be approved by the head graduate adviser.

An 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.). 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. Circulated material may include dissertation chapters, qualifying papers, article drafts, and/or new project ideas. 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.

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.

The Internet has emerged as a crucial platform for freedom of expression and the exchange of ideas and information. Access to an open Internet offers an opportunity for a global citizenry to freely communicate, collaborate, and exchange ideas. This participatory class offers the chance to study issues, challenges, theory, and practice in the realm of Internet freedom. We will focus on real-world case studies of the Internet mobilizing people by spreading alternative views and news; the parallel emergence of collective identity and civic action; Internet censorship; and technologies used to evade surveillance and filtering. We will also study the technical challenges of measuring and assessing digital repression, designing anti-censorship tools that have trust, scalability, and usability, as well as related privacy and security issues. Students will do individual or group projects relating to the 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.

The US Federal Trade Commission (FTC) has emerged as the primary regulator of online privacy. In a recent case, the FTC marked the end of contract law approaches to online privacy in favor of a more interventionalist approach. Years of protecting consumers against “harm” has evolved to an attempt to protect consumer “dignity” in online commerce.

This transition has profound implications for US online commerce. In grounding privacy rights in dignitary interests, the line between acceptable and unacceptable behaviors will become less clear. Those wishing to represent online businesses should have a strong understanding of this agency, its norms, and approaches to address clients’ business challenges. This seminar will explore the agency’s dominance in the law of online privacy and security, its policy approaches, and in particular, how it should address growing concern over online privacy.

Students will be required to prepare a significant policy document on the FTC that will be shared with the agency’s leadership. Additionally, students will author a shorter paper focusing upon some aspect of the FTC or its leadership. (We post these on wikipedia.org). 

Note: In Fall 2011, this course is cross-listed as Law 279.7 section 1.

In Spring 2010, this course was offered for 2 units and cross-listed with Law 276P.1.

Individual & Group Study

This course takes a multidisciplinary, hands-on approach to exploring the sociotechnical practices and political-economic issues involved in building wireless networks in rural and under-resourced areas. Students will be introduced to a large-scale wireless network under development on the scenic South Mendocino coast (with optional field trips), and will have the opportunity to devise a semester-long project in their fields of interest. This course is of particular relevance to students in the following disciplines: computer science, electrical-engineering, business management, anthropology, sociology, political science, public policy, international relations, and education.

Course may be repeated for credit as topic varies. Weekly group meetings. Prerequisites: Consent of instructor. Group projects on special topics in information management and systems.

Directed group study (reading group or “book club”). Will meet approximately every other week.

The concept of “organizing system” that is presented in The Discipline of Organizing is proving to be a useful and generative framework for analyzing existing resource collections and designing novel ones.  But the multidisciplinary breadth of TDO and its new abstractions can make it challenging as a textbook. 

The plan for this seminar is to read three (or more) books on organizing that take widely different perspectives — cognitive, policy/procedural, even spiritual — on the ubiquitous problems of how to select, arrange, interact with, and maintain resources.  The goal is to find new insights that can be incorporated into The Discipline of Organizing to improve its future editions for general readers and students alike.

We start the semester by all reading these two books:

and we end the semester by reading one or more of these (students choose one; the professor will read them all):

Two hours of directed group study per week. Prerequisites:  Consent of instructor.  Course must be taken for a letter grade to fulfill degree requirements. The final project is designed to integrate the skills and concepts learned during the Information School master's program and helps prepare students to compete in the job market. It provides experience in formulating and carrying out a sustained, coherent, and significant course of work resulting in a tangible work product; in project management, in presenting work in both written and oral form; and, when appropriate, in working in a multidisciplinary team. Projects may take the form of research papers or professionally-oriented applied work.

Course may be repeated for credit as topic varies. Format varies. Prerequisites: Consent of instructor.  Individual study of topics in information management and systems under faculty supervision.

This course is intended for graduate student instructors (GSIs) and is meant to be taken simultaneously with teaching as a GSI and to satisfy the Graduate Council's 300-level pedagogy course requirement. The practicum may include discussion, reading, preparation, and practical experience under faculty supervision in teaching, with a focus on topics within information management and systems.

Course may be repeated for credit as topic varies. Four hours of work per week per unit. Must be taken on a satisfactory/unsatisfactory basis. Does not count toward a degree.

Spring 2014: Info 375 will be offered for 2 units.

Course may be repeated for credit. Must be taken on a satisfactory/unsatisfactory basis. Prerequisites: Consent of instructor. Individual study in consultation with the major field adviser, intended to provide an opportunity for qualified students to prepare themselves for the various examinations required of candidates for the Ph.D. degree.