Information Course Schedule spring 2021

Upper-Division

This course explores the history of information and associated technologies, uncovering why we think of ours as "the information age." We will select moments in the evolution of production, recording, and storage from the earliest writing systems to the world of Short Message Service (SMS) and blogs. In every instance, we'll be concerned with both what and when and how and why, and we will keep returning to the question of technological determinism: how do technological developments affect society and vice versa?

Section 1
TuTh 9:30 am - 11:00 am — Internet/Online
Instructor(s): Paul Duguid
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. Some live sessions may be recorded.
Discussion Section 101
Fr 10:00 am - 11:00 am — Internet/Online
Discussion Section 102
Fr 11:00 am - 12:00 pm — Internet/Online

Three hours of lecture per week. Methods and concepts of creating design requirements and evaluating prototypes and existing systems. Emphasis on computer-based systems, including mobile system and ubiquitous computing, but may be suitable for students interested in other domains of design for end-users. Includes quantitative and qualitative methods as applied to design, usually for short-term term studies intended to provide guidance for designers. Students will receive no credit for 114 after taking 214.

Section 1
TuTh 4:00 pm - 5:30 pm — Internet/Online
Instructor(s): Steve Fadden
If you are an undergraduate student interested in this... more
If you are an undergraduate student interested in this course, please add yourself to the INFO 114 WAITLIST! There is no guarantee that undergraduate students will be accepted into the course, but waitlisted students will be reviewed by the instructor in late January.

This course covers the 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 (IT) 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.

Section 1
MoWe 2:00 pm - 3:30 pm — Internet/Online
Instructor(s): John Chuang

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.

Section 1
TuTh 2:00 pm - 3:30 pm — Internet/Online
Instructor(s): David Bamman

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
Tu 6:00 pm - 7:30 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
12:00 am - 12:01 am
Section 2
We 6:00 pm - 7:30 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 202

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.

8 weeks - 3 hours of lecture per week.

Section 1
TuTh 12:30 pm - 2:00 pm — Internet/Online
Instructor(s): Morgan Ames
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 (1/19/21 - 3/4/21)

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.

7 weeks - 4 hours of lecture per week.

Section 1
TuTh 12:30 pm - 2:00 pm
Instructor(s): Deirdre Mulligan
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 (3/09/21 - 04/29/21) Due to COVID-19, this course will be taught remotely. Real-time attendance IS required for this course. There will be asynchronous video (Tuesdays) and recordings of live sessions that students can watch on their own time but attendance is still expected.

General

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.

Section 1
TuTh 4:00 pm - 5:30 pm — Internet/Online
Instructor(s): Steve Fadden
If you are an undergraduate student interested in this... more
If you are an undergraduate student interested in this course, please WAITLIST yourself on the INFO 114 section. There is no guarantee that undergraduate students will be accepted into the course. 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. Students are expected to be present, and active participants when class is in session. Because group work is required for most assignments, students are expected to be able to collaborate with their team members, with the expectation that they will make time available based on "Pacific time-friendly" hours.

"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

Discusses application of social psychological theory and research to information technologies and systems; we focus on sociological social psychology, which largely focuses on group processes, networks, and interpersonal relationships. Information technologies considered include software systems used on the internet such as social networks, email, and social games, as well as specific hardware technologies such as mobile devices, computers, wearables, and virtual/augmented reality devices. We examine human communication practices, through the lens of different social psychology theories, including: symbolic interaction, identity theories, social exchange theory, status construction theory, and social networks and social structure theory.

Section 1
TuTh 11:00 am - 12:30 pm — Internet/Online
Instructor(s): Coye Cheshire
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 and recordings of live sessions that students can watch on their own time but students still need to attend in real time.

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.

Section 1
MoWe 2:00 pm - 3:30 pm — Internet/Online
Instructor(s): John Chuang
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 (though this may be relaxed for students who are not in the Pacific time zone). There will be some asynchronous video and some recordings of live sessions that students can watch on their own time.

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
Tu 3:30 pm - 6:30 pm — Internet/Online
Instructor(s): Deirdre Mulligan
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.

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.

Section 1
MoWe 10:30 am - 12:00 pm — Internet/Online
Instructor(s): Marti Hearst
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 and expected for this course. Students who miss class will have to make up work. They will sometimes miss in-class quizzes. Class material is a mix of presentation and discussion. The main class will not be recorded but the labs will be. Students are strongly encouraged to attend labs in real time and then view the recordings as needed. This is a 4 unit course; it has a commensurate amount of work and will continue to have that large amount of work even under remote instruction. It also requires programming in javascript. We will learn javascript together, but students are expected to know HTML and CSS before the class begins. Students who are not familiar with HCI/design classes should be aware that this is a design class, which means there are no right algorithmic answers, and they need to be comfortable with that.
Laboratory Section 101
We 12:00 pm - 1:00 pm — Internet/Online

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.

Section 1
TuTh 9:30 am - 11:00 am — Internet/Online
Instructor(s): Joshua Blumenstock
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 on their own time.
Discussion Section 101
We 1:00 pm - 2:00 pm — Internet/Online

This course is a survey of web technologies that are used to build back-end systems that enable rich web applications. Utilizing technologies such as Python, Flask, Docker, RDBMS/NoSQL databases, and Spark, this class aims to cover the foundational concepts that drive the web today. This class focuses on building APIs using micro-services that power everything from content management systems to data engineering pipelines that provide insights by processing large amounts of data. The goal of this course is to provide an overview of the technical issues surrounding back-end systems today, and to provide a solid and comprehensive perspective of the web’s constantly evolving landscape.

Section 1
MoWe 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. Live Sessions will be posted before 8 PM PDT the day of the live session.
Laboratory Section 101
Fr 2:00 pm - 3:00 pm
Instructor(s): Kay Ashaolu

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.

Section 1
TuTh 2:00 pm - 3:30 pm — Internet/Online
Instructor(s): David Bamman

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.

Section 1
Fr 9:30 am - 12:30 pm — Internet/Online
Instructor(s): Kimiko Ryokai

Three hours of seminar per week.  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.

Section 1
MoFr 3:30 pm - 5:00 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 NOT be asynchronous video or recordings of live sessions that students can watch on their own time.

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
MoWe 12:30 pm - 2:00 pm — Internet/Online
Instructor(s): AnnaLee Saxenian
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.

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. Through a careful reading of recent research 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.

Section 1
Mo 10:30 am - 1:00 pm — Internet/Online
Instructor(s): Joshua Blumenstock
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.

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
TuTh 2:00 pm - 3:30 pm — Internet/Online
Instructor(s): Steve Trush
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.
Lecture Section 101
12:00 am - 12:01 am

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

Special Topics

For firms and organizations that handle personal data, the desire to extract valuable information and insight must be balanced against the privacy interests of individuals. This task has grown considerably harder in the last few decades, with the development of advanced learning algorithms that can leverage statistical patterns to infer personal information. As a result, databases that were recently considered anonymized have been proven vulnerable to attack. Starting with the seminal definition of differential privacy, researchers are now responding with a new generation of algorithmic techniques, based on strong adversary models and offering mathematical bounds on worst-case privacy loss. This course is an introduction to the field known as formal privacy or differential privacy. It includes both foundational theory and algorithmic techniques for building private algorithms. A particular focus is placed on algorithms for statistical learning, and to research that incorporates a statistical perspective.

The first third of the course is structured like a bootcamp, with problem sets to build fluency in the most common mathematical structures used in the field. The latter two-thirds of the course is structured like a research seminar, with student-led discussion of published articles each week. The course completes with a final research project, giving students a chance to develop new algorithms, extend theoretical results, or build systems that incorporate formal privacy guarantees.

Section 3
Tu 11:00 am - 2:00 pm — Internet/Online
Instructor(s): Paul Laskowski, Nitin Kohli
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 but students still need to attend in real time.

Data and the algorithmic systems are ubiquitous in everyday life. These data encode our daily choices, actions, and behaviors, as well as our more persistent social identities. They also enrich the lives of some while limiting the life chances of others. In this way, data generated and collected about us form a type of information infrastructure: pervasive, hidden, and at times insidious. As technology and data-driven systems increasingly enter into our public, professional, and personal spheres, more of these worlds become encoded in data and result in shifts in the power relations within those worlds. In a word, data is a medium which reconfigures power.

In this seminar, we will engage readings around data, power, and infrastructure, drawing from a number of interdisciplinary academic, artistic, and activist traditions. We’ll discuss topics related to state projects of legibility and quantification; the genealogy of the modern data subject; the politics of classification systems; the surveillance of Blackness and the carceral logics of technology; administrative violence and trans and gender non-conforming identities; the invisible labor powering data-driven systems; and the resistances, obfuscations, and refusals to datafication and surveillance.

Section 4
Th 3:30 pm - 6:30 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 NOT be asynchronous video or recordings of live sessions that students can watch on their own time. However, the instructor is open to accommodating students from different time zones.

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 1
Mo 4:00 pm - 7:00 pm — Requested General Assignment

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.

Section 2
Fr 11:20 am - 12:46 pm — Internet/Online
Instructor(s): Chris Jay Hoofnagle Jennifer M Urban

This class 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.

The class will balance foundational concerns with exposure to practical languages, tools, and real-world concerns. We will study the foundations of prevalent data models in use today, including relations, tensors, and dataframes, and mappings between them. We will study SQL as a means to query and manipulate data at scale, including performance concerns like views and indexes, query processing and optimization, and transactions, all from a user perspective. We will study the foundations and realities of data preparation, including hands-on work with real-world data using standard Python and SQL frameworks. We will explore data exploration modalities for non-programmers, including the fundamentals behind spreadsheet systems and interactive visual analytics packages. We will look at approaches for managing the machine learning lifecycle of data preparation, model selection and training, model serving and monitoring. Time permitting we will look at technologies for moving, sharing, and caching data including event streaming systems, key-value/document stores, log analytics, and search engines.

Section 2
TuTh 5:00 pm - 6:30 pm — Requested General Assignment
Prerequisites: * COMPSCI C100/DATA C100/STAT C100 or... more
Prerequisites: * COMPSCI C100/DATA C100/STAT C100 or * COMPSCI 189 or * INFO 251 or * DATA 144/INFO 254 or * equivalent upper-division course in data science. AND * COMPSCI 61A or * COMPSCI 88 or * INFO 206B or * equivalent courses in programming. This class will not assume deep experience with databases or big data solutions. Prerequisites will be MANUALLY ENFORCED during the first week of class. Faculty will be provided a list of all enrolled students and their pre-req status for review/drop by the end of week 2. INFO 290T-002 is reserved for MIMS students. Undergraduate students interested in this course should enroll/waitlist for COMPSCI 194.035 and should contact the CS department for enrollment questions.

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#).

Section 1
Fr 12:30 pm - 3:30 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 required for this course. There will NOT be asynchronous video or recordings of live sessions.

Seminar

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.

Section 1
We 2:00 pm - 4:00 pm
Instructor(s): Coye Cheshire
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 on their own time but students still need to attend in real time.

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
Fr 1:00 pm - 2:00 pm — Internet/Online
Instructor(s): John Chuang
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.

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