Information Course Schedule fall 2020

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.

Section 1
MoWeFr 11:00 am - 12:00 pm — Internet/Online
Instructor(s): David Wagner, Swupnil K Sahai
Laboratory Section 999
12:00 am - 12:01 am — Internet/Online

Upper-Division

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
TuTh 12:30 pm - 2:00 pm — Internet/Online
Due to COVID-19, this course will be taught remotely. Real-... more
Due to COVID-19, this course will be taught remotely. Real-time attendance IS required for this course. There will be asynchronous video component that students can watch on their own time.

What can computation allow us to discover about literature and how might machine learning transform literary theory? Can we use algorithms to challenge gender norms and complicate ideas about race and authorship in novels and other narratives? And what can quantification tell us about the markers of poetic form, stylistic innovation, or literary prestige? This course will study these and other issues in the field of digital humanities or cultural analytics to teach humanities scholars computational methods to develop new insights in their disciplines and instruct students of data science in literary and cultural theories to deepen and nuance their approach to data.

Open to both undergraduate and graduate students, our course will pursue three ends: first, we will explore the landscape of methods currently used by scholars at the intersection of computation and cultural theory (including methods in natural language processing such as named entity recognition, coreference resolution and parsing; computer vision, including image recognition; classification; social network analysis; and hypothesis testing), and discuss others that are at the leading edge of adoption. Second, we will cast a critical eye on those methods and discuss the assumptions they make about their objects of study in order to determine when they can be appropriately applied. And third, we will lead students through the act of operationalizing a research question in the digital humanities.

The course will be structured in two parts: the first will focus on reading articles in the field and detailing the technical knowledge needed to execute that work. We will focus on research that touches several core themes — theories of gender, race, and class, narratology (including free indirect discourse and focalization, or representations of consciousness), poetic theory and meter, and structuralist philosophy — and explore several modalities, including text, images and sound. The second part of the course will be structured as a lab/studio/critique, where students lead discussion about their specific research projects and receive feedback from the class and professors. Our hope is that you will leave the class with the knowledge to carry forward your own projects in computation and critique.

Section 2
TuTh 2:00 pm - 3:30 pm — Internet/Online
Instructor(s): David Bamman Thomas Patrick McEnaney
Prerequisites: Data 8 (or equivalent); proficient... more
Prerequisites: Data 8 (or equivalent); proficient programming in Python Due to COVID-19, this course will be taught remotely. Real-time attendance IS required for this course. There will NOT be asynchronous video or recordings of live sessions that students can watch on their own time.

Core

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 2:00 pm - 3:30 pm — Internet/Online
Instructor(s): Michael Rivera
Due to COVID-19, this course will be taught remotely. Real-... more
Due to COVID-19, this course will be taught remotely. Real-time attendance IS required for this course. There will be an asynchronous video component that students can watch on their own time. There will be NO recordings of live sessions. Please note that group work will require live Zoom meetings with your team. The instructor will try to assign groups based on convenient time zones, as much as possible.
Web-Based Lecture Section 101

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.

Section 1
TuTh 9:30 am - 11:00 am — Internet/Online
Instructor(s): Marti Hearst
Please note this class is a half-semester course that... more
Please note this class is a half-semester course that starts in the 1st half of the semester. (8/27 - 10/15). This course is a required core course for MIMS graduate students. There is generally no space for outside graduate students, however you are welcome to waitlist if you are really interested. Due to COVID-19, this course will be taught remotely. Real-time attendance IS required for this course. There will NOT be asynchronous video or recordings of live sessions that students can watch on their own time.

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 10:00 am - 12:00 pm — Internet/Online
Instructor(s): Hany Farid
Please note this class is a half-semester course that... more
Please note this class is a half-semester course that starts in the 1st half of the semester. (8/26 - 10/14). If you are an undergraduate student, please email studentaffairs@ischool.berkeley.edu for a permission code to get on a wait-list for the course. Due to COVID-19, this course will be taught remotely. Real-time attendance is NOT required for this course. There will be an asynchronous video component that students can watch on their own time. There will also be recordings of live sessions that students can watch later. All deadlines for assignment submissions will be in Pacific Standard Time, so please plan accordingly.

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 10:00 am - 12:00 pm — Internet/Online
Instructor(s): Hany Farid
Please note this class is a half-semester course that... more
Please note this class is a half-semester course that starts in the 2nd half of the semester. (10/19 - 12/09) If you are an undergraduate student, please email studentaffairs@ischool.berkeley.edu for a permission code to get on a wait-list for the course. Due to COVID-19, this course will be taught remotely. Real-time attendance is NOT required for this course. There will be an asynchronous video component that students can watch on their own time. There will also be recordings of live sessions that students can watch later. All deadlines for assignment submissions will be in Pacific Standard Time, so please plan accordingly.

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.
Section 1
Mo 2:00 pm - 5:00 pm — Internet/Online
Instructor(s): Niloufar Salehi
Due to COVID-19, this course will be taught remotely. Real-... more
Due to COVID-19, this course will be taught remotely. Real-time attendance IS required for this course. There will NOT be asynchronous video or recordings of live sessions that students can watch on their own time.

This course is a graduate-level introduction to HCI research. Students will learn to conductoriginal HCI research by reading and discussing research papers while collaborating on asemester-long research project. Each week the class will focus on a theme of HCI research andreview foundational and cutting-edge research relevant to that theme. The class will focus on thefollowing areas of HCI research: ubiquitous computing, social computing, critical theory, andhuman-AI interaction. In addition to these research topics the class will introduce commonqualitative and quantitative methodologies in HCI research.

Section 1
Tu 9:30 am - 12:30 pm — Internet/Online
Instructor(s): Niloufar Salehi
Due to COVID-19, this course will be taught remotely. Real-... more
Due to COVID-19, this course will be taught remotely. Real-time attendance IS required for this course. There will be an asynchronous video component that students can watch on their own time. There will be NO recordings of live sessions.

Three hours of lecture per week. This course focuses on managing people in information-intensive firms and industries, such as information technology industries. Topics include managing knowledge workers; managing teams (including virtual ones); collaborating across disparate units, giving and receiving feedback; managing the innovation process (including in eco-systems); managing through networks; and managing when using communication tools (e.g., tele-presence). The course relies heavily on cases as a pedagogical form.

Section 1
Fr 9:00 am - 1:00 pm — Internet/Online
Instructor(s): Morten Hansen
Due to COVID-19, this course will be taught remotely. Real-... more
Due to COVID-19, this course will be taught remotely. Real-time attendance IS required for this course. There will NOT be asynchronous video or recordings of live sessions that students can watch on their own time.

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
MoWeFr 2:00 pm - 3:00 pm — Internet/Online
Instructor(s): Kay Ashaolu
Due to COVID-19, this course will be taught remotely. Real-... more
Due to COVID-19, this course will be taught remotely. Real-time attendance is NOT required for this course. There will be recordings of live sessions that students can watch later. For students not in the Pacific time zone, all real-time class sections will be recorded and posted for students to view. If you need support, please send messages through the Piazza instance that will be set up. The instructor and the TA as well as the class can answer and help out. Other learning resources will be made available too. There is a group project at the end of this class, so coordination beyond timezones will be key to completing that project, and the coordination will be the responsibility of the group members.

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.

Section 1
We 2:00 pm - 5:00 pm — Internet/Online
INFO 254 is for Graduate Students ONLY. Undergraduates... more
INFO 254 is for Graduate Students ONLY. Undergraduates interested in this course should register/wait list for DATA 144. You need to contact the Undergraduate Data Science Program for enrollment questions regarding DATA 144. Due to COVID-19, this course will be taught remotely. Real-time attendance IS required for this course. There will be recordings of live sessions that students can watch on their own time.

Three hours of seminar per week. How does the design of new educational technology change the way people learn and think? How do we design systems that reflect our understanding of how we learn? This course explores issues on 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. Also listed as New Media C263.

Section 1
Fr 9:30 am - 12:30 pm — Internet/Online
Instructor(s): Kimiko Ryokai Erin Sarah Gelgoot
Due to COVID-19, this course will be taught remotely. Real-... more
Due to COVID-19, this course will be taught remotely. Real-time attendance IS required for this course. There will NOT be asynchronous video or recordings of live sessions that students can watch on their own time.

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 — Internet/Online
Instructor(s): Coye Cheshire, Andrew Chong
Due to COVID-19, this course will be taught remotely. Real-... more
Due to COVID-19, this course will be taught remotely. Real-time attendance IS required for this course. There will be an asynchronous video component that students can watch on their own time. There will be recordings of live sessions but students still have to be present during real-time sessions.
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
TuTh 2:00 pm - 3:30 pm — Internet/Online
Instructor(s): Jenna Burrell
Due to COVID-19, this course will be taught remotely. Real-... more
Due to COVID-19, this course will be taught remotely. Real-time attendance IS required for this course. There may be an asynchronous video component that students can watch on their own time. There will be NO recordings of live sessions.

This course provides students with real-world experience assisting politically vulnerable organizations and persons around the world to develop and implement sound cybersecurity practices. Students will spend the majority of their credit hours engaging directly with clients under the supervision of Clinic staff. Emphasis will be on research to develop innovative security mitigations in response to threats of political adversaries.

Section 1
— Internet/Online
Instructor(s): Steve Trush
Prerequisite: The Advanced Citizen Clinic practicum is for... more
Prerequisite: The Advanced Citizen Clinic practicum is for students who have already completed Info 289. Public Interest Cybersecurity: The Citizen Clinic Practicum.

Special Topics

In this group study class, we will cover the material in Data 8 using the online Data 8X a three-part professional certificate program in data science from UC Berkeley. This first course, “Computational Thinking with Python,” focuses on programming and data visualization. The second course, “Inferential Thinking by Resampling,” will focus on statistical inference. The third course is “Prediction and Machine Learning.”

This group study is intended for graduate students in professional schools who seek an introduction to data science in order to integrate techniques into their domain or to pursue further educational opportunities such as the graduate certificate in applied data science. The class format is essentially self-guided: students will watch the video lecture and complete the assignments before class, and then meet to discuss the lesson. Undergraduate assistants from Data 8 will coach class participants as necessary. There are small class projects that allow students to work with their own datasets.

Data 8X is based on a rigorous first-year undergraduate course at UC Berkeley called Foundations of Data Science. Over 1,000 students take this course each semester. The course is designed as an introduction to programming and statistics for students from many different majors. It teaches practical techniques that apply across many disciplines, and also serves as the technical foundation for more advanced courses in data science, statistics, and computer science.

No prior programming experience is necessary, but many of the programming techniques covered in this course do not appear in a typical introduction to programming. The programming content of this course focuses on manipulating data tables, rather than building software applications. Therefore, students who take the course after taking other programming courses often learn a new approach to programming that they haven't encountered before.

Section 7
Tu 4:00 pm - 7:00 pm — Internet/Online
Instructor(s): Karen Chapple Hans Kristian P Castro
Due to COVID-19, this course will be taught remotely. Real-... more
Due to COVID-19, this course will be taught remotely. Real-time attendance is NOT required for this course. There will be an asynchronous video component that students can watch on their own time. There will be recordings of live sessions that students can watch later. For students not in the Pacific time zone, all real-time class sections will be recorded and posted for students to view. These sections are designed to supplement the asynchronous video lectures through additional workshops, lab walkthroughs, and discussions. If you are unable to attend the live classes, we still aim to support students through the use of Piazza and other platforms to answer questions and provide feedback and help.

Ethnographic research provides rich detail about a given slice of the social world, providing a nuanced analysis of culture, context, and how people collectively make sense of their daily lives. These inductive and immersive methods can answer questions about shared experiences and perceptions and mechanisms of change, and provide the grounds for generating testable hypotheses. In recent years, ethnographic methods have further expanded in response to challenges from feminist, post-colonial, and anti-racist movements and in recognition of shifting socio-technical assemblages enabled by digital networks and platforms. A number of scholars have taken up the call for reflexive and flexible approaches to studying community life in the digital age, across a range of social spheres. In this course, we will dive into this robust body of recent scholarship and explore what it can teach us about how the digital shapes and is shaped by our social ecosystems.

This class is ideal for graduate students and advanced undergraduates interested in qualitative methods in information studies, including but not limited to those in information,sociology, anthropology, geography, gender and ethnic studies, and other interdisciplinary programs.

Section 4
TuTh 12:30 pm - 2:00 pm — Internet/Online
Instructor(s): Anne Jonas
Due to COVID-19, this course will be taught remotely. Real-... more
Due to COVID-19, this course will be taught remotely. Real-time attendance IS required for this course. There will NOT be asynchronous video or recordings of live sessions that students can watch on their own time.

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 2
We 1:00 pm - 4:00 pm — Internet/Online
Instructor(s): James Reffell
Due to COVID-19, this course will be taught remotely. Real-... more
Due to COVID-19, this course will be taught remotely. Real-time attendance IS required for this course. There will be recordings of live sessions that students can watch later for reference. The course will involve both group work and working with external clients. Students will be responsible for meeting with groups and clients remotely, including managing different time zones when necessary.

This course introduces students to experimentation in the social sciences. 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 have facilitated the development of better data gathering. Key to this area of inquiry is the insight that correlation does not necessarily imply causality. In this course, we learn how to use experiments to establish causal effects and how to be appropriately skeptical of findings from observational data.

Section 6
MoWe 9:30 am - 11:00 am — Internet/Online
Instructor(s): D. Alex Hughes
Prerequisites for this course are INFO 271B or equivalent... more
Prerequisites for this course are INFO 271B or equivalent quantitative methods course per instructors discretion. Due to COVID-19, this course will be taught remotely. Real-time attendance IS required for this course. There will be an asynchronous video component that students can watch on their own time. There will be recordings of live sessions that students can watch later as a reference but real-time participation is still mandatory.

This course will explore how legal, ethical, and economic frameworks enable and constrain security technologies and policies. As digital technologies penetrate deeply into almost every aspect of human experience, a broad range of social-political-economic-legal-ethical-military and other non-technical considerations have come to envelope the cybersecurity landscape. Though cybersecurity itself is a technical discipline, these non-technical considerations constrain it, enable it, and give it shape. We will explore the most important of these elements. The course will introduce some of the most important macro-elements (such as national security considerations and the interests of nation-states) and micro-elements (such as behavioral economic insights into how people understand and interact with security features). Specific topics include policymaking (on the national, international, and organizational level), business models, legal frameworks (including duties of security, privacy issues, law enforcement access issues, computer hacking, intellectual property, and economic/military/intellectual property espionage), standards making, and the roles of users, government, and industry.

Section 5
MoWe 11:20 am - 12:46 pm — Internet/Online
Instructor(s): Chris Jay Hoofnagle Jennifer M Urban
Due to COVID-19, this course will be taught remotely. Real-... more
Due to COVID-19, this course will be taught remotely. Real-time attendance IS required for this course. There will NOT be asynchronous video or recordings of live sessions that students can watch on their own time.

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 2:00 pm - 5:00 pm — Internet/Online
Instructor(s): Jez Humble
Due to COVID-19, this course will be taught remotely. Real-... more
Due to COVID-19, this course will be taught remotely. Real-time attendance is NOT required for this course. There will be an asynchronous video component that students can watch on their own time. There will be recordings of live sessions that students can watch later. A significant amount of the final grade is for group work. While the instructor will try to arrange groups by timezone, students may need to schedule regular meetings to work with people outside their timezone.

Seminar

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
Th 11:00 am - 12:00 pm — Internet/Online
Instructor(s): Paul Duguid
Due to COVID-19, this course will be taught in a hybrid... more
Due to COVID-19, this course will be taught in a hybrid format, with both online and in-person components.

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 — Internet/Online

Individual/Group Study

Discussion, reading, preparation, and practical experience under faculty supervision in the teaching of specific topics within information management and systems. Does not count toward a degree.

Section 1
Tu 6:00 pm - 8:00 pm — Internet/Online
Instructor(s): Paul Duguid
Due to COVID-19, this course will be taught in a hybrid... more
Due to COVID-19, this course will be taught in a hybrid format, with both online and in-person components.