Information Course Schedule spring 2018

Foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership. Also listed as Computer Science C8 and Statistics C8.

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

Must be taken on a P/NP basis.

Surveying history through the lens of information and information through the lens of history, this course looks across time to consider what might distinguish ours as “the information age” and what that description implies about the role of “information technology” across time. We will select moments in societies’ development of information production, circulation, consumption, and storage from the earliest writing and numbering systems to the world of Social Media. In every instance, we’ll be concerned with what and when, but also with how and why. Throughout we will keep returning to questions about how information-technological developments affect society and vice versa?

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

This course provides an introduction to ethical and legal issues surrounding data and society, as well as hands-on experience with frameworks, processes, and tools for addressing them in practice. It blends social and historical perspectives on data with ethics, law, policy, and case examples — from Facebook’s “Emotional Contagion” experiment to controversies around search engine and social media algorithms, to self-driving cars — to help students develop a workable understanding of current ethical and legal issues in data science and machine learning. Legal, ethical, and policy-related concepts addressed include: research ethics; privacy and surveillance; bias and discrimination; and oversight and accountability. These issues will be addressed throughout the lifecycle of data — from collection to storage to analysis and application. The course emphasizes strategies, processes, and tools for attending to ethical and legal issues in data science work. Course assignments will emphasize researcher and practitioner reflexivity, allowing students to explore their own social and ethical commitments.

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.

Three hours of lecture per week. Application of economic tools and principles, including game theory, industrial organization, information economics, and behavioral economics, to analyze business strategies and public policy issues surrounding information technologies and IT industries. Topics include: economics of information; economics of information goods, services, and platforms; strategic pricing; strategic complements and substitutes; competition models; network industry structure and telecommunications regulation; search and the "long tail"; network cascades and social epidemics; network formation and network structure; peer production and crowdsourcing; interdependent security and privacy.

This course is designed to be an introduction to the topics and issues associated with information and information technology and its role in society. Throughout the semester we will consider both the consequence and impact of technologies on social groups and on social interaction and how society defines and shapes the technologies that are produced. Students will be exposed to a broad range of applied and practical problems, theoretical issues, as well as methods used in social scientific analysis. The four sections of the course are: 1) theories of technology in society, 2) information technology in workplaces 3) automation vs. humans, and 4) networked sociability.

This course uses examples from various commercial domains — retail, health, credit, entertainment, social media, and biosensing/quantified self — to explore legal and ethical issues including freedom of expression, privacy, research ethics, consumer protection, information and cybersecurity, and copyright. The class emphasizes how existing legal and policy frameworks constrain, inform, and enable the architecture, interfaces, data practices, and consumer facing policies and documentation of such offerings; and, fosters reflection on the ethical impact of information and communication technologies and the role of information professionals in legal and ethical work.

This course addresses concepts and methods of user experience research, from understanding and identifying needs, to evaluating concepts and designs, to assessing the usability of products and solutions. We emphasize methods of collecting and interpreting qualitative data about user activities, working both individually and in teams, and translating them into design decisions. Students gain hands-on practice with observation, interview, survey, focus groups, and expert review. Team activities and group work are required during class and for most assignments. Additional topics include research in enterprise, consulting, and startup organizations, lean/agile techniques, mobile research approaches, and strategies for communicating findings.

Three hours of lecture per week. Prerequisites: Graduate standing. As it's generally used, "information" is a collection of notions, rather than a single coherent concept. In this course, we'll examine conceptions of information based in information theory, philosophy, social science, economics, and history. Issues include: How compatible are these conceptions; can we talk about "information" in the abstract? What work do these various notions play in discussions of literacy, intellectual property, advertising, and the political process? And where does this leave "information studies" and "the information society"?

Three hours of lecture per week. This course applies economic tools and principles, including game theory, industrial organization, information economics, and behavioral economics, to analyze business strategies and public policy issues surrounding information technologies and IT industries. Topics include: economics of information goods, services, and platforms; economics of information and asymmetric information; economics of artificial intelligence, cybersecurity, data privacy, and peer production; strategic pricing; strategic complements and substitutes; competition and antitrust; Internet industry structure and regulation; network cascades, network formation, and network structure.

The design and presentation of digital information. Use of graphics, animation, sound, visualization software, and hypermedia in presenting information to the user. Methods of presenting complex information to enhance comprehension and analysis. Incorporation of visualization techniques into human-computer interfaces. Three hours of lecture and one hour of laboratory per week.

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.

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

This course will cover new interface metaphors beyond desktops (e.g., for mobile devices, computationally enhanced environments, tangible user interfaces) but will also cover visual design basics (e.g., color, layout, typography, iconography) so that we have systematic and critical understanding of aesthetically engaging interfaces. Students will get a hands-on learning experience on these topics through course projects, design critiques, and discussion, in addition to lectures and readings. Two hours of lecture per week.

There is a burgeoning market for technologists and lawyers who can understand the application and implementation of privacy and security rules to network connected services. Privacy and Security Lab is a new course designed to promote the development of such “privacy technologists.” Students will meet twice a week, once in lecture, and the second time in a computer lab to gain hands-on skills in privacy and security analysis. The course will explore the concepts, regulations, technologies, and business practices in privacy and security, including how different definitions of “privacy” may shape technical implementation of information-intensive services; the nature of privacy and security enhancing services; and how one might technically evaluate the privacy and security claims made by service providers. There are no prerequisites and enrollment is open to law students to encourage cross-disciplinary exchanges.

The Future of Cybersecurity Reading Group (FCRG) is a two-credit discussion seminar focused on cybersecurity. In the seminar, graduate, professional, and undergraduate students discuss current cybersecurity scholarship, notable cybersecurity books, developments in the science of security, and evolving thinking in how cybersecurity relates to political science, law, economics, military, and intelligence gathering. Students are required to participate in weekly sessions, present short papers on the readings, and write response pieces. The goals of the FCRG are to provide a forum for students from different disciplinary perspectives to deepen their understanding of cybersecurity and to foster and workshop scholarship on cybersecurity.

Marketers want to deliver timely and relevant messages to their customers in support of brand building, acquisition, cross-sell, and retention. Though there are a wide array of channels, tools, and technologies available to multi-channel, multi-product marketers, the path to success is not an easy one.

The most formidable challenges include:

• What Are the Delivery Tools and Technologies Available to Marketers?
• Where and How to Spend Marketing Dollars Most Effectively?
• What Metrics Should Be Set to Gauge Success?
• What Data Are Available to and Generated by the Ecosystem?

The tools, metrics, and data used to execute and evaluate marketing spend can be described as the marketing analytics “ecosystem.” A common industry term is the “marketing technology stack.”

This class will provide a topical overview to the ecosystem and by the end of the class, have an understanding the connectivity between the marketing technology stack, the data utilized, data generated and useful metrics. This background is essential for students interesting in how marketing can drive successful outcomes for customers and for the business.

This course covers the fundamental data structures and algorithms found in many technical interviews. These data structures include (but are not limited to): lists, stacks, queues, trees, heaps, hashes, and graphs. Algorithms, such as those for sorting and searching, will also be covered, along with an analysis of their time and space complexity. Students will learn to recognize when these data structures and algorithms are applicable, implement them in a group setting, and evaluate their relative advantages and disadvantages.

Description TBA

Course Objective: Develop new ideas and technology for making a quantum leap in improving how people learn.

This is an interdisciplinary graduate research seminar whose goal is to design technology and learning practices that will make major, significant improvements over how learning and teaching are done today. The course will have a technology-centered focus, but the most important metrics will be those related to learning gains.

As this is a graduate seminar, students will be responsible for selecting and designing the materials and the presentations in the course, with only light supervision by the instructor.

Students earning 1 unit will do the following:

• Summarize current research papers and book chapters
• Complete paper and artifact evaluations before each class
• Complete in-class assignments, including peer-assessments
• Present information clearly and concisely
• Lead class sessions

Students earning 3 units will do the following:

• The work listed above for 1 unit, and:
• Innovate in one particular area of research
• Design, implement, and release a research artifact; one of
• Working with a team to engineer something great
• Writing a research paper proposing a future approach based on a detailed analysis of existing approaches

Course Prerequisites

Ph.D. students who have an interest in pushing the state of the art in education and educational technology are the intended participants of this course. It is preferred if students already have some background in learning sciences, but not required. It is also preferred that students have programming background, but also not required, if instead they come from learning sciences or some other relevant non-CS field such as psychology. The same applies to master’s students.

Undergraduates will be accepted to the course if they can demonstrate a proven interest in the topic, relevant background, and can present a recommendation from a UC Berkeley professor or equivalent. (Having taken a course with the instructor is equivalent.) Interested undergraduates should email the instructor with the name of the professor to contact for their reference, and should also include a copy of the UC Berkeley transcript and their resume.

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.

In this course you’ll learn industry-standard agile and lean software development techniques such as test-driven development, refactoring, pair programming, and specification through example. You’ll also learn good object-oriented programming style. We’ll cover the theory and principles behind agile engineering practices, such as continuous integration and continuous delivery.

This class will be taught in a flip-the-classroom format, with students programming in class. We'll use the Java programming language. Students need not be expert programmers, but should be enthusiastic about learning to program. Please come to class with laptops, and install IntelliJ IDEA community edition. Students signing up should be comfortable writing simple programs in Java (or a Java-like language such as C#).

This is a hands-on full-stack web development course, and students will work on all aspects of the full-stack web development process. Individual and team assignments will enable students to develop skills in data modeling, database and API design, responsive front-end design, version control, and deployment using Python, JavaScript, and full-stack frameworks such as Flask. The goal of this course is to help students understand different technologies and work towards being able to implement complete web-based projects for desktop and mobile.

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

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