Major/Minor Areas
Code | Title | Units |
---|---|---|
Human-Computer Interaction | ||
Introduction to User Experience Design | 4 | |
User Experience Research | 3 | |
Human-Computer Interaction (HCI) Research | 3 | |
Information Visualization and Presentation | 4 | |
Theory and Practice of Tangible User Interfaces | 4 | |
Interface Aesthetics | 3 | |
Special Topics in Information (Advanced HCI Research and Interaction Design only) | 1-4 | |
Special Topics in Technology (Biosensory Computing only) | 2-4 | |
Plus outside courses upon approval of your advisor | ||
Information Economics and Policy | ||
Information Technology Economics, Strategy, and Policy | 3 | |
Plus outside courses upon approval of your advisor | ||
Information Law and Policy | ||
Information Law and Policy | 3 | |
Technology and Delegation | 3 | |
Public Interest Cybersecurity: The Citizen Clinic Practicum | 3 | |
Special Topics in Social Science and Policy (Introduction to Politics of Information and Seminar in the Politics of Information only) | 2-4 | |
Plus outside courses upon approval of your advisor | ||
Information Organization and Retrieval | ||
Information Organization and Retrieval | 3 | |
Information Visualization and Presentation | 4 | |
Applied Machine Learning | 4 | |
Applied Natural Language Processing | 3 | |
Data Engineering | 4 | |
Natural Language Processing | 4 | |
Plus outside courses upon approval of your advisor | ||
Information Systems Design | ||
Introduction to Programming and Computation | 2 | |
Introduction to Data Structures and Analytics | 2 | |
Applied Machine Learning | 4 | |
Front-End Web Architecture | 3 | |
Back-End Web Architecture | 3 | |
Privacy Engineering | 3 | |
Data Engineering | 4 | |
Applied Natural Language Processing | 3 | |
Natural Language Processing | 4 | |
Plus outside courses upon approval of your advisor | ||
Social Aspects of Information | ||
Research Design and Applications for Data and Analysis | 3 | |
Social Issues of Information | 3 | |
User Experience Research | 3 | |
Concepts of Information | 3 | |
Leadership and Management | 3 | |
Social Psychology and Information Technology | 3 | |
Experiments and Causal Inference | 3 | |
Quantitative Research Methods for Information Systems and Management | 3 | |
Qualitative Research Methods for Information Systems and Management | 3 | |
Big Data and Development | 3 | |
Plus outside courses upon approval of your advisor | ||
Information and Communication Technologies and Devleopment | ||
Social Issues of Information | 3 | |
Introduction to User Experience Design | 4 | |
User Experience Research | 3 | |
Information and Communications Technology for Development | 3 | |
Big Data and Development | 3 | |
Plus outside courses upon approval of your advisor |
Info 201 research design and applications for data and analysis 3 units.
Terms offered: Fall 2024, Spring 2024, Fall 2023 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. Course must be taken for a letter grade to fulfill degree requirements. Research Design and Applications for Data and Analysis: Read More [+]
Hours & Format
Fall and/or spring: 15 weeks - 1.5 hours of lecture per week
Additional Format: One and one-half hours of lecture per week.
Additional Details
Subject/Course Level: Information/Graduate
Grading: Letter grade.
Research Design and Applications for Data and Analysis: Read Less [-]
Terms offered: Fall 2024, Fall 2023, Fall 2022 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. Information Organization and Retrieval: Read More [+]
Rules & Requirements
Prerequisites: Students should have a working knowledge of the Python programming language
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Additional Format: Three hours of lecture per week.
Information Organization and Retrieval: Read Less [-]
Terms offered: Spring 2024, Spring 2023, Spring 2022 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. Social Issues of Information: Read More [+]
Social Issues of Information: Read Less [-]
Terms offered: Spring 2024, Spring 2023, Spring 2022 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. Information Law and Policy: Read More [+]
Prerequisites: Consent of instructor required for nonmajors
Instructor: Mulligan
Information Law and Policy: Read Less [-]
Terms offered: Fall 2024, Fall 2023, Fall 2022 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. Introduction to Programming and Computation: Read More [+]
Fall and/or spring: 7.5 weeks - 4 hours of lecture per week
Additional Format: Four hours of lecture per week for seven and one-half weeks.
Instructor: Farid
Introduction to Programming and Computation: Read Less [-]
Terms offered: Fall 2024, Fall 2023, Fall 2022 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 106. It may also be taken as a stand-alone course by any student that has sufficient Python experience. Introduction to Data Structures and Analytics: Read More [+]
Prerequisites: INFO 206A or equivalent, or permission of instructor
Credit Restrictions: Course must be completed for a letter grade to fulfill degree requirements.
Formerly known as: Information 206
Introduction to Data Structures and Analytics: Read Less [-]
Terms offered: Fall 2024, Fall 2023, Fall 2022 This course will provide an introduction to the field of Human-Computer Interaction (HCI). Students will learn to apply design thinking to User Experience (UX) design, prototyping, & evaluation. The course will also cover special topic areas within HCI. Introduction to User Experience Design: Read More [+]
Introduction to User Experience Design: Read Less [-]
Terms offered: Spring 2024, Spring 2023, Spring 2022 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. User Experience Research: Read More [+]
Additional Format: Three hours of Lecture per week for 15 weeks.
User Experience Research: Read Less [-]
Terms offered: Fall 2024, Fall 2023 This course will give participants hands-on digital product design experience oriented around current industry practice. The course will be project-based with an emphasis on iteration, practice, and critique. During the course, participants will work on a series of design projects through a full design process, including developing appropriate design deliverables, gathering feedback, and iterating on designs. Product Design Studio: Read More [+]
Objectives & Outcomes
Course Objectives: The course objective is to provide students interested in web and mobile Product Design with skills, practice, and experience that will prepare them for careers in product design and design-related roles.
Prerequisites: DES INV 15 or COMPSCI 160 or INFO 213 AND INFO 214; or permission of the instructor. Students can take INFO 214 and INFO 215 concurrently, but students may not drop INFO 214 and remain in INFO 215
Formerly known as: Information Systems and Management 215
Product Design Studio: Read Less [-]
Terms offered: Spring 2024, Fall 2021, Fall 2020 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. Human-Computer Interaction (HCI) Research: Read More [+]
Instructor: Salehi
Human-Computer Interaction (HCI) Research: Read Less [-]
Terms offered: Spring 2024, Spring 2022, Spring 2020 As it's generally used, "information" is a collection of notions, rather than a single coherent concept. In this course, we'll examine conceptions of information based in information theory, philosophy, social science, economics, and history. Issues include: How compatible are these conceptions; can we talk about "information" in the abstract? What work do these various notions play in discussions of literacy, intellectual property, advertising, and the political process? And where does this leave "information studies" and "the information society"? Concepts of Information: Read More [+]
Prerequisites: Graduate standing
Instructors: Duguid, Nunberg
Concepts of Information: Read Less [-]
Terms offered: Fall 2024, Fall 2023, Fall 2021 This course focuses on the practice of leadership, collaboration, and people management in contemporary, distributed, information and technology-rich organizations. Not just for potential people managers, this course is derived from the premise that a foundation in leadership, management, and collaboration is essential for individuals in all roles, at any stage of their career. To build this foundation we will take a hybrid approach, engaging literature from disciplines such as social psychology, management, and organizational behavior, as well as leveraging case studies and practical exercises. The course will place a special emphasis on understanding and reacting to social dynamics in workplace hierarchies and teams. Leadership and Management: Read More [+]
Leadership and Management: Read Less [-]
Terms offered: Fall 2024, Spring 2013, Spring 2011 This class is for graduate students interested in getting an advanced understanding of judgments and decisions made with predictive algorithms. The course will survey the vast literature on the psychology of how people arrive at judgments and make decisions with the help of statistical information, focused mostly on experimental lab evidence from cognitive and social psychology. Then study the burgeoning evidence on how people use statistical algorithms in practice, exploring field evidence from a range of settings from criminal justice and healthcare to housing and labor markets. Special attention is paid to psychological principles that impact the effectiveness and fairness of algorithms deployed at scale. Decisions and Algorithms: Read More [+]
Course Objectives: Help students understand systematic human errors and explore potential algorithmic solutions.
Decisions and Algorithms: Read Less [-]
Terms offered: Spring 2024, Spring 2023, Spring 2022 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. Social Psychology and Information Technology: Read More [+]
Instructor: Cheshire
Social Psychology and Information Technology: Read Less [-]
Terms offered: Spring 2024, Spring 2022, Spring 2021 This course applies 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 goods, services, and platforms; economics of information and asymmetric information; economics of artificial intelligence, cybersecurity, data privacy, and peer production; strategic pricing; strategic complements and substitutes; competition and antitrust; Internet industry structure and regulation; network cascades, network formation, and network structure. Information Technology Economics, Strategy, and Policy: Read More [+]
Course Objectives: INFO234 is a graduate level course in the school's topical area of Information Economics and Policy, and can be taken by the masters and doctoral students to satisfy their respective degree requirements.
Student Learning Outcomes: Students will learn to identify, describe, and analyze business strategies and public policy issues of particular relevance to the information industry. Students will learn and apply economic tools and principles to analyze phenomena such as platform competition, social epidemics, and peer production, and current policy issues such as network neutrality and information privacy. Through integrated assignments and project work, the students will apply the theoretical concepts and analytic tools learned in lectures and readings to develop and evaluate a business model, product, or service of their choosing, e.g., a start-up idea they are pursuing.
Instructor: Chuang
Information Technology Economics, Strategy, and Policy: Read Less [-]
Terms offered: Fall 2021, Fall 2019, Fall 2018 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. Technology and Delegation: Read More [+]
Technology and Delegation: Read Less [-]
Terms offered: Fall 2024, Spring 2024, Fall 2022 This course introduces students to experimentation in data science. Particular attention is paid to the formation of causal questions, and the design and analysis of experiments to provide answers to these questions. 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 has facilitated the development of better data gathering. Experiments and Causal Inference: Read More [+]
Experiments and Causal Inference: Read Less [-]
Terms offered: Spring 2023, Spring 2022, Spring 2021 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. Course must be completed for a letter grade to fulfill degree requirements. Information Visualization and Presentation: Read More [+]
Prerequisites: INFO 206B or knowledge of programming and data structures with consent of instructor
Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of laboratory per week
Additional Format: Three hours of lecture and one hour of laboratory per week.
Instructor: Hearst
Information Visualization and Presentation: Read Less [-]
Terms offered: Spring 2024, Spring 2023, Spring 2022 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. Applied Machine Learning: Read More [+]
Student Learning Outcomes: • Effectively design, execute, and critique experimental and non-experimental methods from statistics, machine learning, and econometrics. • Implement basic algorithms on structured and unstructured data, and evaluate the performance of these algorithms on a variety of real-world datasets. • Understand the difference between causal and non-causal relationships, and which situations and methods are appropriate for both forms of analysis. • Understand the principles, advantages, and disadvantages of different algorithms for supervised and unsupervised machine learning.
Prerequisites: INFO 206B , or equivalent course in Python programming; INFO 271B , or equivalent graduate-level course in statistics or econometrics; or permission of instructor
Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week
Additional Format: Three hours of lecture and one hour of discussion per week.
Instructor: Blumenstock
Applied Machine Learning: Read Less [-]
Terms offered: Fall 2024, Fall 2023, Fall 2022 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/JS/React/Redux), as well as the underlying technologies 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. Front-End Web Architecture: Read More [+]
Prerequisites: Introductory programming
Fall and/or spring: 15 weeks - 2 hours of lecture and 1 hour of laboratory per week
Additional Format: Two hours of lecture and one hour of laboratory per week.
Formerly known as: Information 253
Front-End Web Architecture: Read Less [-]
Terms offered: Spring 2024, Spring 2023, Spring 2022 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. Back-End Web Architecture: Read More [+]
Back-End Web Architecture: Read Less [-]
Terms offered: Spring 2024, Spring 2023 The course overviews a broad number of paradigms of privacy from a technical point of view. The course is designed to assist system engineers and information systems professionals in getting familiar with the subject of privacy engineering and train them in implementing those mechanisms. In addition, the course is designed to coach those professionals to critically think about the strengths and weaknesses of the different privacy paradigms. These skills are important for cybersecurity professionals and enable them to effectively incorporate privacy-awareness in the design phase of their products. Privacy Engineering: Read More [+]
Course Objectives: Critique the strengths and weaknesses of the different privacy paradigms Describe the different technical paradigms of privacy that are applicable for systems engineering Implement such privacy paradigms, and embed them in information systems during the design process and the implementation phase Stay updated about the state of the art in the field of privacy engineering
Credit Restrictions: Students will receive no credit for INFO 255 after completing INFO 255 . A deficient grade in INFO 255 may be removed by taking INFO 255 .
Privacy Engineering: Read Less [-]
Terms offered: Fall 2024, Fall 2023, Fall 2021 This course examines the use of natural language processing as a set of methods for exploring and reasoning about text as data, focusing especially on the applied side of NLP — using existing NLP methods and libraries in Python in new and creative ways. 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. Applied Natural Language Processing: Read More [+]
Prerequisites: INFO 206A and INFO 206B or proficient programming in Python (programs of at least 200 lines of code). Proficient with basic statistics and probabilities
Instructor: Bamman
Applied Natural Language Processing: Read Less [-]
Terms offered: Spring 2024, Fall 2022 This course 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, with a focus on ensuring reliable, scalable operationalization. ensuring reliable, scalable operationalization. Data Engineering: Read More [+]
Prerequisites: INFO 206B or equivalent college-level course in computer science in Python with a C- or better AND COMPSCI C100/ DATA C100 / STAT C100 or COMPSCI 189 or INFO 251 or DATA 144 or equivalent college-level course in data science with a C- or better
Instructors: Hellerstein, Parameswaran, Jain
Data Engineering: Read Less [-]
Terms offered: Spring 2024, Spring 2023, Spring 2022 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. Natural Language Processing: Read More [+]
Prerequisites: Familiarity with data structures, algorithms, linear algebra, and probability
Natural Language Processing: Read Less [-]
Terms offered: Fall 2024, Fall 2023, Fall 2022 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. Theory and Practice of Tangible User Interfaces: Read More [+]
Instructor: Ryokai
Also listed as: NWMEDIA C262
Theory and Practice of Tangible User Interfaces: Read Less [-]
Terms offered: Spring 2024, Spring 2023, Spring 2022 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 discussions, in addition to lectures and readings. Interface Aesthetics: Read More [+]
Also listed as: NWMEDIA C265
Interface Aesthetics: Read Less [-]
Terms offered: Fall 2024, Fall 2023, Fall 2022 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, casual 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. Quantitative Research Methods for Information Systems and Management: Read More [+]
Prerequisites: Introductory statistics recommended
Quantitative Research Methods for Information Systems and Management: Read Less [-]
Terms offered: Fall 2024, Fall 2023, Fall 2022 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. Qualitative Research Methods for Information Systems and Management: Read More [+]
Instructor: Burrell
Qualitative Research Methods for Information Systems and Management: Read Less [-]
Terms offered: Spring 2024, Spring 2023, Spring 2022 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. Information and Communications Technology for Development: Read More [+]
Fall and/or spring: 15 weeks - 3 hours of seminar per week
Additional Format: Three hours of seminar per week.
Instructor: Saxenian
Formerly known as: Information C283
Information and Communications Technology for Development: Read Less [-]
Terms offered: Spring 2024, Spring 2021, Spring 2019 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. Big Data and Development: Read More [+]
Prerequisites: Students are expected to have prior graduate training in machine learning, econometrics, or a related field
Big Data and Development: Read Less [-]
Terms offered: Fall 2024, Spring 2024, Fall 2023 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. Public Interest Cybersecurity: The Citizen Clinic Practicum: Read More [+]
Repeat rules: Course may be repeated for credit with instructor consent.
Public Interest Cybersecurity: The Citizen Clinic Practicum: Read Less [-]
Terms offered: Fall 2024, Spring 2024, Fall 2023 Specific topics, hours, and credit may vary from section to section, year to year. Special Topics in Information: Read More [+]
Repeat rules: Course may be repeated for credit when topic changes. Students may enroll in multiple sections of this course within the same semester.
Fall and/or spring: 8 weeks - 2-8 hours of lecture per week 15 weeks - 1-4 hours of lecture per week
Summer: 10 weeks - 1.5-6 hours of lecture per week
Additional Format: One to four hours of lecture per week. One and one-half to six hours of lecture per week for 10 weeks. Two to eight hours of lecture per week for 8 weeks.
Special Topics in Information: Read Less [-]
Terms offered: Fall 2024, Spring 2024, Fall 2023 Specific topics, hours, and credit may vary from section to section and year to year. Special Topics in Management: Read More [+]
Additional Format: One to four hours of lecture per week. Two to eight hours of lecture per week for 8 weeks.
Special Topics in Management: Read Less [-]
Terms offered: Fall 2024, Fall 2023, Spring 2023 Specific topics, hours, and credit may vary from section to section and year to year. Special Topics in Social Science and Policy: Read More [+]
Fall and/or spring: 8 weeks - 4-8 hours of lecture per week 15 weeks - 2-4 hours of lecture per week
Additional Format: Two to four hours of lecture per week. Four to eight hours of lecture per week for 8 weeks.
Special Topics in Social Science and Policy: Read Less [-]
Terms offered: Spring 2024, Fall 2023, Spring 2023 Specific topics, hours, and credit may vary from section to section and year to year. Special Topics in Technology: Read More [+]
Special Topics in Technology: Read Less [-]
Terms offered: Prior to 2007 Specific topics, hours, and credit may vary from section to section, year to year. Special Topics in Information: Read More [+]
Repeat rules: Course may be repeated for credit when topic changes.
Fall and/or spring: 15 weeks - 1-4 hours of lecture per week
Additional Format: One to four hours of lecture per week.
Grading: Offered for satisfactory/unsatisfactory grade only.
Instructor: Hoofnagle
Terms offered: Fall 2016, Summer 2016 10 Week Session, Spring 2016 This course is designed to help School of Information graduate students maximize their internship, practicum, or independent research experiences. Information Management Practicum: Read More [+]
Course Objectives: Experience the practical application of your academic knowledge to real-world professional contexts; Gain insight into an organization and how one might make a valuable contribution; Reflect on the information the experience has provided, to see if it fits within one’s personal value set and work/life manifestos. Try out various professional activities to see when you are in ‘flow’;
Student Learning Outcomes: Assess the organizational culture of a company, governmental body, or non-governmental organization Connect academic knowledge about information management to real-world professional contexts Evaluate the effectiveness of a variety of information science techniques when deployed in organizational situations Integrate the student's own individual professional goals with the organization's needs relevant to the internship or practicum Reflect critically on the internship or practicum experience
Prerequisites: Consent of a Head Graduate Adviser for the School of Information
Repeat rules: Course may be repeated for credit without restriction.
Fall and/or spring: 15 weeks - 1 hour of internship per week
Summer: 10 weeks - 1.5 hours of internship per week
Additional Format: One hour of internship per week. One and one-half hours of internship per week for 10 weeks.
Information Management Practicum: Read Less [-]
Terms offered: Spring 2024, Spring 2023, Spring 2022 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. Doctoral Research and Theory Workshop: Read More [+]
Prerequisites: PhD students only
Fall and/or spring: 15 weeks - 2 hours of workshop per week
Additional Format: Two hours of workshop per week.
Doctoral Research and Theory Workshop: Read Less [-]
Terms offered: Fall 2024, Fall 2023, Spring 2023 Colloquia, discussion and readings designed to introduce students to the range of interests of the school. Doctoral Colloquium: Read More [+]
Prerequisites: Ph.D. standing in the School of Information
Fall and/or spring: 15 weeks - 1 hour of colloquium per week
Additional Format: One hour of colloquium per week.
Doctoral Colloquium: Read Less [-]
Terms offered: Fall 2024, Spring 2024, Fall 2023 Topics in information management and systems and related fields. Specific topics vary from year to year. Seminar: Read More [+]
Prerequisites: Consent of instructor
Fall and/or spring: 15 weeks - 2-4 hours of seminar per week
Additional Format: Two to Four hours of Seminar per week for 15 weeks.
Seminar: Read Less [-]
Terms offered: Fall 2019, Spring 2016, Fall 2015 Group projects on special topics in information management and systems. Directed Group Study: Read More [+]
Credit Restrictions: Students will receive no credit for INFO 298 after completing INFOSYS 298.
Fall and/or spring: 15 weeks - 1-4 hours of directed group study per week
Summer: 8 weeks - 1.5-7.5 hours of directed group study per week
Additional Format: One to four hours of directed group study per week. One and one-half to seven and one-half hours of directed group study per week for 8 weeks.
Directed Group Study: Read Less [-]
Terms offered: Spring 2022, Spring 2016, Spring 2015 The final project is designed to integrate the skills and concepts learned during the Information School Master's program and helps prepare students to compete in the job market. It provides experience in formulating and carrying out a sustained, coherent, and significant course of work resulting in a tangible work product; in project management, in presenting work in both written and oral form; and, when appropriate, in working in a multidisciplinary team. Projects may take the form of research papers or professionally-oriented applied work. Directed Group Work on Final Project: Read More [+]
Prerequisites: Consent of instructor. Course must be taken for a letter grade to fulfill degree requirements
Additional Format: One to four hours of directed group study per week.
Directed Group Work on Final Project: Read Less [-]
Terms offered: Fall 2023, Summer 2016 8 Week Session, Spring 2016 Individual study of topics in information management and systems under faculty supervision. Individual Study: Read More [+]
Fall and/or spring: 15 weeks - 1-12 hours of independent study per week
Summer: 8 weeks - 2-22.5 hours of independent study per week
Additional Format: Format varies.
Individual Study: Read Less [-]
Terms offered: Spring 2024, Fall 2021, Fall 2020 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. Teaching Assistance Practicum: Read More [+]
Fall and/or spring: 15 weeks - 2 hours of lecture per week
Additional Format: Two hours of lecture per week.
Subject/Course Level: Information/Professional course for teachers or prospective teachers
Instructor: Duguid
Teaching Assistance Practicum: Read Less [-]
School of information.
102 South Hall
Phone: 510-642-1464
Siu Yung Wong
Julia Sprague
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The PhD in Business Administration with a focus in Management Information Systems is a five-year full-time program. Consistently ranked among the best information systems PhD programs worldwide, the program is known for its cutting-edge research and support from actively publishing faculty.
The program prepares future information systems academics by providing strong foundations in a broad range of methods spanning psychometrics, econometrics, computational, design, and qualitative and by providing a strong emphasis in theory development to address important business and societal problems.
Given that information systems are ubiquitous and influence every aspect of life — individuals’ personal and work life, their transactions and interactions, organizational processes, outcomes, and interorganizational relationships, online platforms, markets, governments and society — the information systems field is broad and interdisciplinary and affords research opportunities across a diverse range of topics.
The research approach in the program is problem-focused, theory-based, and method-inclusive (i.e., all methods are welcome and no one single method is favored). Our PhD program provides you with significant individual flexibility, while at the same time ensuring you acquire the necessary conceptual and methodological skills to become a scholarly leader in our field.
Priority deadline: January 4
Applications after January 4 will also be considered until spots are filled
There are five compelling reasons to join our program:
We are among the most research-productive groups, consistently ranked in the top 10 or top 15 departments worldwide in publications in the top two IS journals ( MIS Quarterly and Information Systems Research ). Further, several of our faculty have won multiple research grants and awards for outstanding research.
Our faculty includes a former president of the Association for Information Systems (Richard Watson), two Leo Award winners — the highest award in the field (Richard Watson and Elena Karahanna) — and three AIS Fellows (Richard Watson, Hugh Watson and Elena Karahanna).
Our faculty includes current and former senior editors at MIS Quarterly , Information Systems Research , and the Journal of the Association for Information Systems , associate editors at MIS Quarterly , Information Systems Research , Management Science , and the Journal of the Association for Information Systems , and editorial board members of the Journal of Management Information Systems and Strategic Management Journal , among others.
In these weekly seminars top scholars from around the world present and discuss their research. The PhD students have the opportunity to interact and discuss their research with these scholars in a meeting after the seminar.
Our culture is collaborative and supportive and one in which we view our students as junior colleagues. Students are provided extensive mentoring, support, and personal attention given our one-to-one faculty-student ratio. Evidence of the quality of mentoring is the outstanding placement of doctoral students and the plethora of journal papers co-authored with our faculty (over 100 publications in the past 10 years). Students can work with multiple faculty, not just their dissertation chair as they develop as scholars.
Our PhD graduates are placed in top research universities around the world.
Departments and program offices.
Doctoral program in information systems.
Program Requirements
Doctoral Courses
Overview of the IS Doctoral Program
Mission: To educate and train scholars who will produce first-rate IS research and who will succeed as faculty members in first-rate universities. We offer tracks in technical perspectives on IS, economic perspectives on IS, and organizational/management perspectives on IS. Admissions and performance: We enroll an average of three students each year out of more than 100 highly qualified applicants. Students enrolling typically have GMATS over 700 or GREs over 1400. International students typically have TOEFLs higher than 640. Our students are highly competitive within Stern and nationally. Recently our students have received school-wide awards as "outstanding doctoral students." They have won acceptance at doctoral consortia sponsored by the Academy of Management and the International Conference on Information Systems. And they have won national dissertation research competitions.
Advising and evaluation: The IS doctoral program faculty director advises all first-year doctoral students. During the first year students have many opportunities to get to know the research interests of all departmental faculty. By the beginning of the second year, students have selected a concentration advisor who will guide them through the comprehensive exam process and up to the thesis stage. By the middle of the third year students will have selected a thesis advisor. Each year every student submits a statement of intellectual progress to his/her advisor. All faculty meet to review the progress of all students in a day-long meeting each year. At this time, the student's intellectual progress is reviewed and plans for the following year are considered. The results of this review include a formal letter to the student assessing the previous year's work and offering guidance for the following year's work. All students take a comprehensive written and oral exam at the end of the second year. Students defend their thesis proposal by March of their fourth year and defend their completed dissertation at the end of the fourth year or during the fifth year.
Research and interaction with faculty: The heart of the IS doctoral program is immersion in a community of researchers. Every student has a formal research apprenticeship with one or more faculty members each year. Every student participates in formal and informal research seminars each week with departmental faculty and visitors. Every student presents research in progress and works toward producing publishable papers, usually with a faculty co-author. Students learn to be researchers by doing research. They learn to be research colleagues by working with others and critiquing their research.
Placement record: In the past ten years, our graduates have accepted faculty positions at such schools as University of California at Berkeley, Hong Kong University of Science & Technology, University of Maryland, University of Minnesota, University of Texas at Austin, the University of British Columbia, National University of Singapore, The Wharton School and the University of Cambridge, UK. Please click on the links on the right to learn how to apply, to attend an information session, and to contact the Stern School Doctoral Office.
Natalia Levina Coordinator, Information Systems Doctoral Program IOMS Department
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All students take a common core of courses during their first year which provides an overview of the major research areas in IS and the fundamental knowledge necessary for specialized course work in the second year. In the second year students take specialized course work in one of three concentrations: technical perspectives, economic perspectives, behavioral/managerial perspectives.
Mandatory Breadth Courses (3)
YEAR TWO - Each student chooses one concentration track
Technical Track:
Economics Track:
Behavioral/Managerial Track:
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