Information Course Schedule fall 2021
Foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership. Also listed as Computer Science C8 and Statistics C8.
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.
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.
15 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 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.
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.
This course is a graduate-level introduction to HCI research. Students will learn to conduct original HCI research by reading and discussing research papers while collaborating on a semester-long research project. Each week the class will focus on a theme of HCI research and review foundational and cutting-edge research relevant to that theme. The class will focus on the following areas of HCI research: ubiquitous computing, social computing, critical theory, and human-AI interaction. In addition to these research topics the class will introduce common qualitative and quantitative methodologies in HCI research.
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.
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.
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 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.
Biosensory computing is the multidisciplinary study and development of systems and practices that sense, represent, communicate, and interpret biological signals from the body.
Biosignals are expansive in scope, and can enable a diverse range of biosensory computing applications. They can include physiological (e.g., ECG/PPG, EDA, EEG) and kinesthetic signals (e.g., accelerometry, eye gaze, facial expressions). Many inferences can be drawn about the person from these signals, including their activities, emotional and mental states, health, and even their identities, intentions, memories, and thoughts.
While generated by the person, biosensory data have important characteristics that distinguish them from other types of user-generated data. They are intimate yet leakable, precise yet ambiguous, familiar yet unverifiable, and have limited controllability. Therefore, responsible stewardship of biosensory data must be in place before the full potential of biosensory computing can be realized.
This multidisciplinary course will explore the intellectual foundations and research advances in biosensory computing. We will survey the range of biosensing modalities and technologies, study temporal and spectral data analysis and visualization techniques, interrogate the designs of novel biosensing applications, and tackle issues of user 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.
This course explores current debates about government regulation of online businesses. We start by examining the unintended consequences of digital advertising models that support many large online companies. We then review debates over antitrust, mis- and dis-information, privacy, content controls (e.g. pornography), and section 230 of the Communications Decency Act. The primary focus of the class is on US policy, but we will examine the EU's General Data Protection Regulation (GDPR), the most significant data protection legislation to date. We also look briefly at the way that these issues are being addressed elsewhere in the world and discuss the challenge of national regulation of global businesses.
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.
This course will investigate the unique challenges and opportunities encountered when applying artificial intelligence (AI) to healthcare needs. It will present techniques of AI used in medicine for disease diagnosis, prevention, and treatment. Students from a variety of backgrounds are welcome! In addition to technical aspects of designing AI solutions, we will explore social, ethical, and health policy considerations. Class time will take a variety of formats, including lectures, open discussions, student presentations, and case studies. Final deliverable for the course will be a written report or computer program. The only prerequisites are an interest in Healthcare AI and a willingness to engage in interdisciplinary study; no programming experience is necessary. The curriculum is a journey through Artificial Intelligence (AI) technologies, as they relate to the Healthcare space (HAI). We will look at predictive models, using electronic health records as a data source, and other topics, and conclude with predictions for the future and understanding emerging trends.
This course provides an introduction to how racism influences technology and how technology influences racism. It will discuss practical ways to address discrimination and structural factors that perpetuate unfair algorithmic practices. It reviews the origins of race in science and technology while addressing racism in current practice. This course takes an antiracist approach by advancing the principles of social justice–oriented scientific research and its potential. Course assignments will emphasize hands-on approaches to promoting anti-racism in research and practice, with particular attention given to professional development.
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.
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.
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.