Information Course Schedule fall 2015
Upper Division Courses
Three hours of lecture per week. With the advent of virtual communities and online social networks, old questions about the meaning of human social behavior have taken on renewed significance. Using a variety of online social media simultaneously, and drawing upon theoretical literature in a variety of disciplines, this course delves into discourse about community across disciplines. This course will enable students to establish both theoretical and experiential foundations for making decisions and judgments regarding the relations between mediated communication and human community.
Students will receive no credit for Sociology C167/Information C167 after taking Sociology 167.
Also listed as Sociology C167.
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.
This is a required introductory course for MIMS students, integrating perspectives and best practices from a wide range of disciplines.
NOTE: Before Fall 2017, Info 202 was offered as a full-semester course for 4 units.
7 weeks - 4 hours of laboratory per week. This course introduces software skills used in building prototype scripts for applications in data science and information management. The course gives an overview of procedural programming, object-oriented programming, and functional programming techniques in the Python scripting language, together with an overview of fundamental data structures, associated algorithms, and asymptotic performance analysis. Students will watch a set of instructional videos covering material and will have four hours of laboratory-style course contact each week.
NOTE: Before Fall 2017, Info 206 was titled “Distributed Computing Applications and Infrastructure” and was offered as a full-semester course for 4 units.
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.
This course covers the design, prototyping, and evaluation of user interfaces to computers which is often called Human-Computer Interaction (HCI). It is loosely based on course CS1 described in the ACM SIGCHI Curricula for Human-Computer Interaction (Association for Computing Machinery, 1992).
HCI covers many topics including:
- Human capabilities (e.g., visual and auditory perception, memory, mental models, and interface metaphors);
- Interface technology (e.g., input and output devices, interaction styles, and common interface paradigms); and,
- Interface design methods (e.g., user-centered design, prototyping, and design principles and rules), and interface evaluation (e.g., software logging, user observation, benchmarks and experiments).
This material is covered through lectures, reading, discussions, homework assignments, and a course project. This course differs from CS 160 primarily in two ways:
- There is an emphasis on interfaces for information technology applications; and,
- There is less emphasis on programming and system development, although some simple prototyping (for example, in visual basic or using JAVA GUI development tools) may be required. (CS 160 has a big programming project.)
This course focuses on managing people in information-intensive firms and industries, such as information technology industries. Students who seek careers in these industries will soon be asked to manage people, teams, departments, and units. They need to learn how to manage. However, managing is sometimes very different in these settings: Employees are highly educated; work is more fluid; teamwork and collaboration are essential; and external situations and strategies change rapidly. For these reasons several management principles born in a traditional manufacturing era no longer apply. In particular, the old style of “command and control” needs to give way to more distributed ways of work, with significant consequences for how managers need to manage. Of course, some universal management principles apply no matter what circumstance.
While we will cover these universal management principles in this course, we will pay particular attention to management issues that are highly relevant in information-intensive settings. Topics to be covered will likely include: managing knowledge workers; managing teams (incl. virtual ones); collaborating across disparate units, giving and receiving feedback; managing the innovation process (incl. in eco-systems); managing through networks; and managing when using communication tools (e.g., tele-presence). The course will rely heavily on cases as a pedagogical form.
This course satisfies the Management of Information Projects & Organizations requirement.
"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.
Previously offered as Info 290. Applied Behavioral Economics for Information Systems.
This course is a survey of Web technologies, ranging from the basic technologies underlying the Web (URI, HTTP, HTML) to more advanced technologies being used in the the context of Web engineering, for example structured data formats and Web programming frameworks. The goal of this course is to provide an overview of the technical issues surrounding the Web today, and to provide a solid and comprehensive perspective of the Web's constantly evolving landscape.
Students will receive no credit for 253 after taking 290. Web Architecture.
This course examines the state-of-the-art in applied Natural Language Processing (also known as content analysis and language engineering), with an emphasis on how well existing algorithms perform and how they can be used (or not) in applications. Topics include part-of-speech tagging, shallow parsing, text classification, information extraction, incorporation of lexicons and ontologies into text analysis, and question answering. Students will apply and extend existing software tools to text-processing problems.
Three hours of lecture per week. This course is concerned with the use of Database Management Systems (DBMS) to solve a wide range of information storage, management and retrieval problems, in organizations ranging from large corporations to personal applications, such as research data management. The course combines the practical aspects of DBMS use with more theoretical discussions of database design methodologies and the "internals" of database systems.
A significant part of the course will require students to design their own database and implement it on different DBMS that run on different computer systems. We will use both ACCESS and ORACLE.
In the theoretical portion of the course, we will examine the major types or data models of DBMS (hierarchical, network, relational, and object-oriented). We will discuss the principles and problems of database design, operation, and maintenance for each data model.
The goal of this course is to provide students with an introduction to many different types of quantitative research methods and statistical techniques. This course will be divided into two sections: 1) methods for quantitative research and, 2) quantitative statistical techniques for analyzing data. We begin with a focus on defining research problems, theory testing, causal inference, and designing research instruments. Then, we will explore a range of statistical techniques and methods that are available for empirical research. Topics in research methods include: Primary and Secondary Data Analysis, Sampling, Survey Design, and Experimental Designs. Topics in quantitative techniques include: Descriptive and Inferential statistics, General Linear Models, and Non-Linear Models. The course will conclude with an introduction to special topics in quantitative research methods.
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.
Special Topics Courses
This course addresses the fast-growing area of social and environmental measurement using technologies such as mobile devices, "Internet of Things" (or "Web of Things") style sensors, and remote sensing. We will take a project-based approach, with a classroom discussion each week, followed by a tutorial / practicum in BIDS. Note that the focus of this course is on data collection and management. Teams will likely do some basic visualization and exploratory data analysis; statistics and/or machine learning are not expected. We will leverage support from the Social Science Matrix, the D-Lab, BIDS, CEGA, and Berkeley Research Computing to provide necessary training, hardware, and compute resources. This course is being offered as a part of the Data Science Collaborative at the Berkeley Institute of Data Science (BIDS).
Class-entry code (CEC) required for enrollment. Prospective students must show up of the first day of class; CECs will be distributed after project teams are formed.
Course enrollment is limited to 25 students. In the event of over-enrollment, admission to the course will be based on the quality of the team and the fit with the semester’s projects.
The ICTD group seminar will discuss topics of current interest in the emerging multidisciplinary field of Information and Communications Technologies and Development, or ICTD. Each semester will be focused on a particular topic or set of topics, under the direction of appropriate faculty from the I School's ICTD group. The course content will consist of paper discussions, invited lectures from both within and outside the class and a some relatively short written assignments. Students will also be responsible for presenting during at least on class session, either on their own research, ideas or on a selected set of papers relevant to the semester's chosen topic.
In this seminar we will investigate the frontier of the concept of leadership by exploring how data analytics can help leaders develop and perform better, and enable stakeholders to better track and govern the conduct of leader behaviors.
Data analytics is making inroads into all areas of the economy, including management. Yet the practice of leadership is still seen as an art, not a science. As a result, research on leadership has been restricted by limited data and limited perspectives. That may change with advances in data analytics. Yet that brings up fascinating issues such as can leadership ever be reduced to data analysis? And, if it can, should it be?
In particular, we will explore the concept of leadership development through the lens of analytics by trying to answer the following question: How can smarts “apps” help leaders practice leadership competencies, get feedback on their behaviors, and modify their behaviors in order to improve?
Topics to be included: leadership development, theories of personal development, “nudging” theory, deliberate practice theory, social feedback technology, and the examination of existing "apps" and online tools that can be applied to leadership analytics.
This workshop will be a discussion seminar, where we will all prepare the material for the class and discuss it as a group.
There are few terrains that evoke such socio-political anxieties and ambitions as the human body. Our contemporary moment is characterized by a shift in who can understand, analyze and ‘hack’ the body. Consumer biosensors, citizen science movements such as biohacking, and patient power movements such as participatory medicine, all use technology to challenge the status quo around who can demonstrate expertise about the body. They bring both opportunities and responsibilities. How do we engage with this computational turn that now marks the everyday experience of our biology?
In this course, we will take a close look at critical debates and scholarship about the body. We will draw from art, design, theory and activism. This course will introduce and ground itself in the philosophies of critical technical practice and reflective design. That is, not only will we examine the various ways in which society conceives of the body, through the lens of data, posthumanism, nation, race, gender and dis/ability, we will also unpack how we have been personally formed by those very debates and influences. What underlying values and assumptions do you bring to engaging the body through technology? What kinds of norms are you reinforcing with your actions? What would you like to change?
This course will culminate in a set of group projects, all in conversation with each other, that we will publish online in service to a public audience. Each group project will offer a critical and reflective perspective on a theme of your choosing. You will choose a trajectory most suitable to your learning and communication preference, from three tracks:
Unpack a narrative about the body with a curated digital archive/gallery of representations (text/video/image/sound).
Articulate reflective and critical perspectives in written essay format.
Design a speculative, critical or reflective digital artifact to challenge existing values and assumptions.
Depending on your own prior experience, this class will begin, extend or support the following skills.
Intersectionality: Analyze how concepts of the body are interconnected with other systems of power. These could include, but are not limited to gender, age, ability, literacy, race, and membership in privileged knowledge institutions (universities, scientific labs, medical institutions). Demonstrate how these categories are mutually constituted and intersect with different technological engagements with the body.
Critical self-awareness: Demonstrate self-reflexivity about one’s values, ideas and goals, and how they are connected to one’s own body status and socio-economic position.
Engaged Practice: Explore how to advocate for differences in bodies, identities, marginalized communities, and non-normative practices. Understand the ways in which knowledge institutions are assigned legitimacy. Learn to recognize and support other non-traditional and diverse ways of producing knowledge about the body that are also valuable.
Creativity: Synthesize diverse perspectives, the aesthetics of writing/imagery/sound/touch, and activism to engage with issues of the body in a manner that is imaginative, inspiring and generative.
Just as the web browser brought us click-stream data and the mobile phone brought us geo-location data, ubiquitous low-cost sensors integrated with wearable and Internet-of-Things devices will bring us a new torrent of user data to collect, analyze, and exploit. The course takes a hands-on approach to exploring the possibilities and limitations of consumer-grade sensing technologies for physiological and contextual data.
We will survey the intellectual foundations and research advances in ubiquitous computing, physiological and affective computing, with applications in health and wellness, social computing, information security, novel user interfaces, etc. We will cover temporal and spectral techniques for time-series data analysis. We will consider data stewardship issues, including data ownership, data privacy, and research ethics. The class lending library will provide access to a variety of devices that can be used for data collection and application prototyping.
Project work can be undertaken in a variety of application domains, such as affective computing, ambient assisted living, biometric authentication, privacy by design, quantified self, smart cars and homes, social robotics, and virtual and augmented reality.
Health and health care have profound impact on a society's well being and economic productivity. Health care reform and ongoing economic forces are placing unprecedented pressure on the health care system to provide consumers and payers with value. Patients, purchasers, regulators, and other key stakeholders are demanding that care be readily accessible, proactive, and focused on improving health while containing costs. The health care system, policy makers, and key stakeholders are responding by developing new care models that focus on patient and customer centricity, novel information practices, and the seamless integration of care.
Following a review of the current trends in health care, the course will explore the relationship between health care and the information economy. We will also delve into information strategies being utilized by health care providers, patients, payers, and other key stakeholders to improve care while controlling costs. Health care leaders from Kaiser Permanente will serve as guest lecturers, providing tangible perspective to our discussions.
Mass communications technologies have been profound influencers of human identity, from the printing press and the rise of vernacular political cultures to television and the power of celebrity. While the Web is still a work in progress, salient characteristics such as the collapse of distance, the discovery of like-minded groups, and information delivered in short bursts are already affecting the way people see themselves and the way they consume information. Following an overview on the relationship of technology with identity and communications, the course will look at the uses of narrative in news, public relations, advertising, entertainment, and online gaming.
Free communication has changed the world, including the expectations and work and play. The class begins with the two data revolutions--the first about passively collected clicks on the web, the second about actively contributed data, as platforms like Facebook empower individuals to contribute a variety of quantitative and qualitative data (transactions, social relations, attention gestures, intention, location, and more.) With active student participation, we explore the far-reaching implications of the consumer data revolution for individuals, communities, business, and society.
This course was previously offered for 1 unit; in Fall 2016, the course increased to 2 units.
The Applied Data Analytics Project course offers students a chance to complete a data analytics project for a real client using real data - to develop impactful solutions to the client’s business challenge. MBA students from Haas will work on teams with data science focused graduate students from UC Berkeley’s School of Information, with support from Accenture’s big data group. Together you will take on a data-driven project, focused on solving a challenging issue for one of Accenture’s clients. Your team will take the challenge from data assessment and problem definition through to final client recommendations. The outcome of your project should be a set of strategic and tactical recommendations to increase the client’s effectiveness.
Successful analytics projects require managerial discipline, iterative problem solving skills, a solid grounding in the client’s business (whether an internal or external client) effective communications with both team and client, and data analytics tools and techniques — including data set analysis, modeling, interpretation, and presentation. The primary objective of this course — and of the projects — is to gain valuable experience in applying the approaches, skills, and tools needed to have an impact on business results through the use of data analytics.
This course is cross-listed as MBA247.1B.
- The course runs 8 weeks, starting October 19.
- The focus is on the project based application of data to drive a client’s business decision making. We will not be teaching the technical foundations or practical software for data analytics: Students are expected to contribute prior knowledge either in hard data analytics skills or strategic analysis of business problems using data-driven frameworks. As a result, we require that students have completed the basic data course (Data Science and Data Strategy). Equivalent work experience or prior background may also be accepted. Please contact the Haas@Work program office for further details
- For each project, there will be 2-3 formal client workshops/presentations outside of normal Monday evening class hours, in addition to the normal outside work and coordination needed to manage your client, the deliverables, and your team responsibilities.
- Once assigned a project, you may be required to sign an NDA and IP waiver
- As is the case with many of the Experiential learning courses, we need to make early client commitments and share information in advance of the course. Therefore, please note that there is no add/drop period for this course.
Every business depends on information — about customers, competitors, trends, performance, etc. Entire curricula have been focused on the technological, systems, strategic, and management challenges associated with that dependency. This course, however, looks at a different intersection between information and business. Specifically, it will explore how entrepreneurs across the world are developing ventures fundamentally centered on new and emerging information technologies and the business models and strategies they make possible. These include not only the Googles, Amazons, and Facebooks of the world, but also ventures like Comat and Samasource. In some cases, these are efforts on the proverbial cutting edge of technology; more often they involve creative application and/or integration of existing information technologies in innovative ways.
We will first examine the key elements of business models and the entrepreneurial process, before looking in more detail at a variety of ventures leveraging information-based technologies and strategies in an array of markets. Using of mix of case-study discussion, short lectures, and focused conversations with active entrepreneurs, this will be a highly interactive and collaborative course — not a sit-listen-take-notes type of class.
Expect to be actively involved in a series of in-class and outside assignments, both individual- and team-based, that will help you develop an understanding of how entrepreneurs are using information-centric technologies to create new markets and redefine old ones, and the lessons learned along the way. You may also explore your own ideas for new ventures along the way.
NOTE: This course was previously offered as 290. Information-Centric Entrepreneurship & Startup Strategies.
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.
This course satisfies the Management requirement for the MIMS degree.
In Fall 2015 & Fall 2016, this course was offered for 2 units.
What does it take to deliver a successful analytics project? Implementing the right method to analyze data is just one ingredient. Analytic project success is often determined by the framing of the problem, the selection of the data sources, the composition of the analytics team, and communication of results. While there are many books and courses that cover modeling, there are fewer opportunities for students to learn about non-modeling topics, where the bulk of analytic project time is often spent. This course is designed to fill this gap. We using a decision-based framework based on experience from a consulting perspective, talking about analytics with clients and delivering analytics-related engagements. The classes will make extensive use of case studies and discussions to illustrate the concepts, and students will also work in small groups to define, implement and present the results of an analytics project. Along the way students will learn how to:
- Incorporate vertical industry and horizontal process knowledge when gathering requirements
- Create an analytics project plan, including budgeting and staffing
- Select a cost-effective combination of internal and data sources
- Design analytic interfaces and visualizations for a variety of users
- Assist organizations with strategies to evolve their analytics capabilities
Seminars & Colloquia
One hour colloquium per week. Must be taken on a satisfactory/unsatisfactory basis. Colloquia, discussion, and readings are designed to introduce students to the range of interests of the school.
The seminar explores selected advanced topics relating to 'digital libraries' with special emphasis on:
- Access to networked resources
- Use of two or more resources in conjunction
- Combined use of two or more retrieval systems (e.g. use of pre- or post-processing to enhance the capabilities)
- The redesign of library services
It is expected that these issues will require attention to a number of questions about the nature of information retrieval processes, the feasibility of not-yet-conventional techniques, techniques of making different systems work together, social impact, and the reconsideration of past practices. More generally, the seminar is intended to provide a forum for advanced students in the School. Anyone interested in these topics is welcome to join in — and to talk about their own work. This is a continuation of the previous Lynch/Buckland seminars.
Individual & Group Study
This course is intended for graduate student instructors (GSIs) and is meant to be taken simultaneously with teaching as a GSI and to satisfy the Graduate Council's 300-level pedagogy course requirement. The practicum may include discussion, reading, preparation, and practical experience under faculty supervision in teaching, with a focus on topics within information management and systems.
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. Does not count toward a degree.
Spring 2014: Info 375 will be offered for 2 units.