Information Course Schedule fall 2015
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. 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.
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.
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.
"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.
Three hours of lecture per week. Letter grade to fulfill degree requirements. Prerequisites: Proficient programming in Python (programs of at least 200 lines of code), proficient with basic statistics and probabilities. 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. Introduction to relational, hierarchical, network, and object-oriented database management systems. Database design concepts, query languages for database applications (such as SQL), concurrency control, recovery techniques, database security. Issues in the management of databases. Use of report writers, application generators, high level interface generators.
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.
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.
This course takes a multi-disciplinary approach to explore the possibilities and limitations of ubiquitous sensing technologies for physiological and contextual data. We will survey the intellectual foundations and research advances in ubiquitous computing, biosensory computing, and affective computing, with applications ranging from brain-computer interfaces to health and wellness, social computing to cybersecurity. We will cover temporal and spectral analysis techniques for sensor data. We will examine data stewardship issues such as data ownership, privacy, and research ethics. Students signing up for the 3-unit option will continue in the second half of the semester with a student-led research project.
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.
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).
Specific topics, hours and credit may vary from section to section, year to year. May be repeated for credit with change in content.
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.
Course may be repeated for credit. Three hours of lecture per week for five weeks.
Course may be repeated for credit. Three hours of lecture per week for five weeks.
Course may be repeated for credit. One and one-half to two hours of lecture per week for eight weeks. Two hours of lecture per week for six weeks. Three hours of lecture per week for five weeks.
With the growing demand for analytics skills in business and government, there are many options for students to learn fundamentals of data and analytics modeling. There are fewer opportunities to learn how to manage analytics projects, which often involve leading teams with diverse skills and interacting with stakeholders in a variety of roles. This course is designed to offer students practical guidance and experience around the process of initiating, delivering, and evaluating analytics projects. It will draw on experience from a consulting perspective, talking about analytics with clients and delivering analytics-related engagements.
The course will cover the following topics:
- Starting the analytics conversation: Identifying needs, understanding constraints
- Planning and executing analytics projects: Sizing, staffing, communication
- Making choices around data: Sourcing, standards, licensing and privacy
- Making choices around analytics and visualizations: Techniques, technologies, and integration
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.
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 (“The I-School”), 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 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.
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.
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.
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.