Explore foundational concepts of information security and privacy, including information value, classifications, threats, liabilities and risk management, identity and access controls (IAM), trust frameworks, technology for network, web, software and cloud security; and privacy laws and regulations.
Examine how information is socially and culturally variable and fluid, changing throughout history and differing from place to place. Explore the anthropological study of information; societal norms; and individual, device, communal, and organizational information strategies.
Explore an overview of how information and public policy relate to each other. Examine key information policy areas including privacy, surveillance, theft, health information, business-to-business relationships, and the co-evolution of personal data and information technologies.
Explore an organizational perspective on the management and governance of information. Examine business practices and governance mechanisms for minimizing risks and maximizing returns of information.
Explore framing messages and the impact on people, organizations, risks and privacy; effective crisis management communications; communication and business continuity planning; time management; sense making processes in organizational crisis; and reputation management.
Explore and evaluate the risks and benefits related to information in multiple sectors including financial services, healthcare, consumer services, government, education, and energy.
Study the characteristics of information and the technologies, stakeholders, and questions involved in managing, protecting, and securing information.
Explore the enrollment and authentication for cyber and physical access and transactions, cryptography, biometrics, device identity security, and security culture.
Explore knowledge and data management, storage, and mining. Examine information representation and algorithms. Discuss information security and privacy applications in all market sectors for enrollment, authentication, operational use, fraud detection, and fraud prevention.
Examine laws and other policy instruments related to information security and privacy, different classes of protected personal information, and multiple genres of legal information and legal writing. Explore legal requirements and social responsibilities as they pertain to data protection and the prevention of different types of fraud and information crimes.
Study practical problems, current phenomenon, or professional issues in an institutional setting.
In-depth study of a problem or topic related to Information Security and Privacy. Individual Instruction. May be repeated for credit. ISP 189 is worth 1 hour of semester credit. Students wanting 2 or 3 hours of credit should take ISP 289 or INF 389 respectively.
In-depth study of a problem or topic related to Information Security and Privacy. Individual Instruction. May be repeated for credit. ISP 289 is worth 2 hours of semester credit. Students wanting 1 or 3 hours of credit should take ISP 189 or INF 389 respectively.
In-depth study of a problem or topic related to Information Security and Privacy. Individual Instruction. May be repeated for credit. ISP 389 is worth 3 hours of semester credit. Students wanting 1 or 2 hours of credit should take ISP 189 or INF 289 respectively.
Preparation of a report to fulfill the requirement for the master's degree under the report option. Per Graduate Catalog guidelines, a Master's Report must be supervised or co-supervised by a member of the iSchool Graduate Studies Committee.
Examine theory and practical applications of data and artificial intelligence governance related to information security, privacy, compliance, and ethics. Explore optimization of technical and human dimensions for information risk management. Explore pervasive and emerging risks, risk assessments of data architectures and analytics, and regulations, controls, and governance to protect and safeguard information assets.
Examine established and emerging laws and public policy related to information security and privacy. Examine different classes of protected information and case studies documenting compliance and violation of legal and public policy protections. Explore legal requirements, corporate responsibilities and social responsibilities as they pertain to data protection and the prevention of different types of fraud and information crimes.
Topics in the theoretical, methodological, and practical aspects of information studies. Repeatable with Different Topics.
This course provides an overview of the major subject areas, ideas, concepts, and theories of Information Studies and introduces the basics of research, publication, and academic conventions in Information Studies. Prerequisites: Admission to the doctoral program and consent of the graduate advisor.
An overview of the nature and purposes of research, and common methods and methodologies in information studies.
An overview of concepts, results, and perspectives from philosophical, social science, humanistic, design, and technological disciplines that provide important underpinnings for information studies.
No description provided.
This course starts by discussing broad landscape of epistemological and theoretical perspectives and styles of reasoning and by situating in it quantitative research. It introduces you to the foundational concepts in quantitative research methods, such as causality, conceptualization, operationalization, measurement and sampling. It presents experimental design, survey design, and basic descriptive and inferential (frequentist) statistics, as well as a brief introduction to Bayesian inference and statistics.
Explores a variety of approaches to qualitative methods including ethnography, participant observation, case studies, grounded theory, phenomenology, action research, and so forth. Students will have a hands-on opportunity to conduct their own research project in which they will learn, discuss, and reflect upon the procedures of qualitative research.
Intensive writing, critique, and rewriting to assist senior doctoral students with refining their research writing in preparation for qualifying papers, dissertation proposals, and formal publications. May be repeated for credit.
The individual student works under supervision of a member of the graduate faculty. Students must present the faculty member's name to the iSchool Registration/Course Administrator for registration.
The individual student works under supervision of a member of the graduate faculty. Students must present the faculty member's name to the iSchool Registration/Course Administrator for registration.
The individual student works under supervision of a member of the graduate faculty. Students must present the faculty member's name to the iSchool Registration/Course Administrator for registration.
The individual student works under supervision of a member of the graduate faculty. Students must present the faculty member's name to the iSchool Registration/Course Administrator for registration.
The individual student works under supervision of a member of the graduate faculty. Students must present the faculty member's name to the iSchool Registration/Course Administrator for registration.
Part 1 of the iRISE program sequence for PhD students. Participants will present their potential research projects, for which undergraduates will self-select select into teams. Together, teams and their advisors will compile a project management plan, design their study, identify intended audience beyond academia, and potential impact deliverable to communicate results.
Part 2 of the iRISE program sequence for PhD students. Once they receive university IRB approval to ensure their research meets ethics requirements, teams will begin and complete data collection and analysis. They’ll end with finalized results (i.e. answers to their research questions).
AI hype is everywhere—from classrooms to courtrooms, boardrooms to borders, protestors to investors. In this course, we’ll take a “production view” to get past this hype and critically examine the infrastructure, capital, and labor that goes into building AI, and study how AI is transforming global politics, policy, and economies. We will learn to think critically about major political economic actors in AI—the state, universities, and Big Tech—and how AI affects war fighting, borders, policing, and the environment.Over the semester, students will build towards a final project aligned with their ongoing research agenda by analyzing qualitative or quantitative data to support novel political economic analysis, and increase their work’s impact by situating it within live tech policy debates. Students will iteratively build towards this project via in-class reading reflections, discussions, and a mid-semester project plan. Out of class work will primarily consist of readings, preparing for an assigned day in which they lead discussion, and independent work towards the final project. The course will include select guest lectures and discussion with prominent thinkers in this field, helping build students' wider scholarly network.