Information Course Schedule fall 2022

Upper-Division

This class introduces key issues, concepts, and methodologies of information studies. Students consider questions such as: what does it mean to live in an information society? What are the human and social aspects of the design of technology? How do policy, law, and other social forces affect this? How can technology and data be designed for social good? Students will become familiar with the kinds of research and multidisciplinary methods used in information studies. Students leave the course with tools to understand the politics, economics, and culture of information systems; a nuanced understanding of contemporary case studies involving technological systems in society; and a solid foundation for further study in information science.

Section 1
TuTh 11:00 am - 12:30 pm — 202 South Hall
Instructor(s): Morgan Ames

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.

Section 1
Tu 3:30 pm - 6:30 pm — Valley Life Sciences 2060
Instructor(s): Deirdre Mulligan

Graduate

Introduces the data sciences landscape, with a particular focus on learning data science techniques to uncover and answer the questions students will encounter in industry. Lectures, readings, discussions, and assignments will teach how to apply disciplined, creative methods to ask better questions, gather data, interpret results, and convey findings to various audiences. The emphasis throughout is on making practical contributions to real decisions that organizations will and should make.

Section 1
We 6:30 pm - 8:00 pm — Physics Building 2
Instructor(s): Michael Rivera

15 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.

Section 1
TuTh 9:30 am - 11:00 am — Joan and Sanford I. Weill 101
Instructor(s): David Bamman

This course introduces the basics of computer programming that are essential for those interested in computer science, data science, and information management. Students will write their own interactive programs (in Python) to analyze data, process text, draw graphics, manipulate images, and simulate physical systems. Problem decomposition, program efficiency, and good programming style are emphasized throughout the course.

Section 1
MoWe 2:00 pm - 4:00 pm
Instructor(s): John Chuang

The ability to represent, manipulate, and analyze structured data sets is foundational to the modern practice of data science. This course introduces students to the fundamentals of data structures and data analysis (in Python). Best practices for writing code are emphasized throughout the course. This course forms the second half of a sequence that begins with INFO 206A. It may also be taken as a stand-alone course by any student that has sufficient Python experience.

Section 1
MoWe 2:00 pm - 4:00 pm
Instructor(s): John Chuang
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.
Section 1
MoWe 11:00 am - 12:30 pm — 210 South Hall
Instructor(s): Niloufar Salehi

This course introduces students to experimentation in data science. Particular attention is paid to the formation of causal questions, and the design and analysis of experiments to provide answers to these questions. This topic has increased considerably in importance since 1995, as researchers have learned to think creatively about how to generate data in more scientific ways, and developments in information technology has facilitated the development of better data gathering.

Section 1
Mo 5:30 pm - 7:00 pm — 210 South Hall
Instructor(s): Josue Martinez

This course is a survey of technologies that power the user interfaces of web applications on a variety of devices today, including desktop, mobile, and tablet devices. This course will delve into some of the core front-end languages and frameworks (HTML/CSS/JavaScript/React/Redux), as well as the underlying technologies that enable web applications (HTTP, URI, JSON). The goal of this course is to provide an overview of the technical issues surrounding user interfaces powered by the web today, and to provide a solid and comprehensive perspective of the web’s constantly evolving landscape.

Section 1
Mo 9:00 am - 11:00 am — 210 South Hall
Instructor(s): Kay Ashaolu
UNDERGRADUATES: Please add yourself to the waitlist of the... more
UNDERGRADUATES: Please add yourself to the waitlist of the undergraduate version of the course: INFO 153A. No permission codes are needed. A limited number of undergraduates will be enrolled in the course as we approach the beginning of the semester. Note that undergraduate enrollment in the course will be limited, so we encourage you to work with your undergraduate advisor to ensure you have a backup plan in the event that you do not make it into the class.
Laboratory Section 101
Fr 9:00 am - 10:00 am — 210 South Hall
Instructor(s): Kay Ashaolu

This course will cover the principles and practices of managing data at scale, with a focus on use cases in data analysis and machine learning. We will cover the entire life cycle of data management and science, ranging from data preparation to exploration, visualization and analysis, to machine learning and collaboration, with a focus on ensuring reliable, scalable operationalization.

Section 1
TuTh 6:30 pm - 8:00 pm — Valley Life Sciences 2050
Instructor(s): Lakshya Jain
*This class uses a 999 section. In addition to the lecture... more
*This class uses a 999 section. In addition to the lecture, you must enroll in the DIS 999 to enroll in the course. Selection and assignment into the actual section happens outside of CalCentral. Instructors will provide more information during the first lecture. *Discussion sections are likely to be held on Mondays.
Discussion Section 999
Students will receive no credit for C262 after taking 290 section 4. Three hours of lecture and one hour of laboratory per week. This course explores the theory and practice of Tangible User Interfaces, a new approach to Human Computer Interaction that focuses on the physical interaction with computational media. The topics covered in the course include theoretical framework, design examples, enabling technologies, and evaluation of Tangible User Interfaces. Students will design and develop experimental Tangible User Interfaces using physical computing prototyping tools and write a final project report. Also listed as New Media C262.
Section 1
TuTh 5:00 pm - 6:30 pm — 210 South Hall
Laboratory Section 101
Fr 12:00 pm - 1:00 pm — 210 South Hall

Three hours of lecture per week. Introduction to many different types of quantitative research methods, with an emphasis on linking quantitative statistical techniques to real-world research methods. Introductory and intermediate topics include: defining research problems, theory testing, causal inference, probability and univariate statistics. Research design and methodology topics include: primary/secondary survey data analysis, experimental designs, and coding qualitative data for quantitative analysis. No prerequisites, though an introductory course in statistics is recommended.

Section 1
TuTh 12:30 pm - 2:00 pm — Moffitt Library 101
Instructor(s): Andrew Reddie
Three hours of lecture per week. Theory and practice of naturalistic inquiry. Grounded theory. Ethnographic methods including interviews, focus groups, naturalistic observation. Case studies. Analysis of qualitative data. Issues of validity and generalizability in qualitative research.
Section 1
Fr 2:00 pm - 5:00 pm — 205 South Hall
Instructor(s): Laith Ulaby

New Venture Discovery introduces students to the process of launching an information-intensive venture — a social enterprise, business startup, or venture inside an established organization. It is motivated by the recognition that new enterprises fail more often from lack of customers than flaws in technology or product development. The course takes an iterative, design-oriented, and feedback-driven approach to the search process: identifying a problem or need to address, developing a prototype, discovering customers, refining the concept, testing and validating demand, and developing a sustainable business model.

Section 1
TuTh 11:00 am - 12:30 pm — 210 South Hall
Instructor(s): Elliott Adams

This course provides students with real-world experience assisting politically vulnerable organizations and persons around the world to develop and implement sound cybersecurity practices. In the classroom, students study basic theories and practices of digital security, intricacies of protecting largely under-resourced organizations, and tools needed to manage risk in complex political, sociological, legal, and ethical contexts. In the clinic, students work in teams supervised by Clinic staff to provide direct cybersecurity assistance to civil society organizations. We emphasize pragmatic, workable solutions that take into account the unique needs of each partner organization.

Section 1
MoWe 6:30 pm - 8:00 pm — Internet/Online
Instructor(s): Tiffany Rad
Enrollment into this course is by application ONLY.... more
Enrollment into this course is by application ONLY. Application will be posted in the next month. *This course DOES NOT run on Berkeley Time. *This course runs on a different academic schedule, please review the dates in CalCentral accordingly.

This course gives participants hands-on software product design experience based on current industry practice. The course is project-based with an emphasis on iteration, practice, and critique from experienced industry designers. During the course, participants work iteratively on a series of design projects (both solo and in groups) through a full design process, including developing appropriate design deliverables and gathering feedback. We’ll also cover specific topics, including design and prototyping tools, working with and developing design systems, typical phases and deliverables of the design process, and designing in different contexts (e.g. startups vs. larger companies). There will also be guest lectures from industry experts.

Section 1
Tu 2:00 pm - 5:00 pm — 202 South Hall
Instructor(s): Jamie Reffell

This course explores current debates about government regulation of online businesses. We start by examining the unintended consequences of digital advertising models that support many large online companies. We then review debates over antitrust, mis- and dis-information, privacy, content controls (e.g. pornography), and section 230 of the Communications Decency Act. The primary focus of the class is on US policy, but we will examine the EU's General Data Protection Regulation (GDPR), the most significant data protection legislation to date. We also look briefly at the way that these issues are being addressed elsewhere in the world and discuss the challenge of national regulation of global businesses.

Section 3
MoWe 10:00 am - 11:30 am — 202 South Hall
Instructor(s): AnnaLee Saxenian

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.

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

Section 1
Mo 4:00 pm - 7:00 pm — 202 South Hall
Instructor(s): Jen Dante
Class day/time subject to change.
Class day/time subject to change.

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.

Section 1
We 2:00 pm - 3:00 pm — 107 South Hall
Instructor(s): Marti Hearst, David Bamman

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.

Section 1
Fr 3:00 pm - 5:00 pm — 107 South Hall
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