Information Course Schedule fall 2019

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 — 3108 Etcheverry
Instructor(s): Deirdre Mulligan

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.

Section 1
MoWe 10:30 am - 12:00 pm, We 12:00 pm - 1:00 pm — 210 South Hall
Instructor(s): Marti Hearst
Prerequisites This course is designed for upper division... more
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. Please note the Wednesday 12-1pm timeslot is a required lab section.

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.

Section 1
TuTh 11:00 am - 12:30 pm — 202 South Hall
Instructor(s): David Bamman
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. (10/22 - 12/12). 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.

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 — Moffitt Library 102
Instructor(s): Hany Farid
This is a 7.5 week class
This is a 7.5 week class

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 — Moffitt Library 102
Instructor(s): Hany Farid
This is a 7.5 week class.
This is a 7.5 week class.

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
Th 3:30 pm - 6:30 pm — 210 South Hall
Instructor(s): Niloufar Salehi
Course title may change.
Course title may change.

"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.

Section 1
TuTh 2:00 pm - 3:30 pm — 202 South Hall
Instructor(s): Steven Weber

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.

Section 1
TuTh 3:30 pm - 5:00 pm — 202 South Hall
Instructor(s): Deirdre Mulligan

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
We 12:30 pm - 3:30 pm — 202 South Hall
Instructor(s): Kay Ashaolu

This course covers 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. The course is project based; 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 add context and grounding to projects.

Section 1
TuTh 12:30 pm - 2:00 pm — Berkeley Way West 1215
Instructor(s): Zachary Pardos
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 9:30 am - 11:00 am — 202 South Hall
Laboratory Section 101

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 — 202 South Hall
Instructor(s): Coye Cheshire
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
Mo 2:00 pm - 5:00 pm — 205 South Hall
Instructor(s): Laith Ulaby

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 1:30 pm - 3:30 pm — 107 South Hall
Instructor(s): Steven Weber
All students are initially placed on the waitlist and will... more
All students are initially placed on the waitlist and will be contacted with instructions to apply for admission to the course. Students should be prepared to submit a resume and a brief explanation of their interest and applicable background. No specific classes or majors are needed as prerequisites, however we would like to limit enrollment to graduate students and select juniors/seniors.
Section 101

Special Topics

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 4
Fr 12:30 pm - 2:00 pm — 210 South Hall
This is a flipped class. Students will watch 1.5 hours of... more
This is a flipped class. Students will watch 1.5 hours of asynchronous videos every week and and attend an in-person 1.5 hour session each week.

Course may be repeated for credit as topic varies.  Two to six hours of lecture per week for seven and one-half weeks or one to four hours of lecture per week for 15 weeks.  Prerequisites:  Consent of instructor.  Specific topics hours, and credit may vary from section to section, year to year.

Section 6
TuTh 3:30 pm - 5:00 pm — 205 South Hall
Instructor(s): Daniel Aranki
Students will watch 1.5 hours of asynchronous videos every... more
Students will watch 1.5 hours of asynchronous videos every week and and attend an in-person 1.5 hour session each week.

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.

Section 2
Mo 4:00 pm - 7:00 pm — 107 South Hall
This is a 10 week class.
This is a 10 week class.

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 3
We 1:00 pm - 4:00 pm — 210 South Hall
Instructor(s): James Reffell

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 5
TuTh 11:00 am - 12:30 pm — 210 South Hall
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.

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 1
TuTh 11:20 am - 12:46 pm — 140 Boalt
Instructor(s): Chris Jay Hoofnagle Jennifer M Urban

Delivering value to enterprises and ensuring long-term career success requires much more than pure technology skills. This course is an industry technology executive’s view of how, as information becomes increasingly strategic for all organizations, future technology leaders can accelerate career growth and bring value to their organizations more quickly by developing this core set of 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 readings, industry guest speakers and hands-on practice of business skills:

  • 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 include:

    • Dick Daniels — CIO of Kaiser Permanente
    • Steve Comstock — CIO of CBS Interactive
    • Michael Kelly — CIO of Red Hat
    • Hugo Evans — VP of Data Science at A.T. Kearney
    • Aurangzeb Khan — CEO of disruptive 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.

Section 2
We 9:00 am - 12:00 pm — 202 South Hall

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 1:00 pm - 4:00 pm — 210 South Hall
Instructor(s): Jez Humble

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 10:00 am - 11:00 am — 205 South Hall
Instructor(s): Niloufar Salehi, John Chuang

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

Individual/Group Study

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. Formerly Information 310. 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
Instructor(s): Paul Duguid
Date/Time TBD.
Date/Time TBD.