Information & Decision Sciences Requirements

Students must take a minimum of 40 semester credits of coursework. Required major field coursework includes all offered IDSc PhD seminars (IDSc 8511, 8521, 8531, 8541, 8721, and 8801). At least 16 credits of supporting/methodology coursework are required. These credits must be 5000-level or above and can include such courses as regression, experimental design, multivariate statistics, econometrics, microeconomics, game theory, data mining, or business intelligence. Students who lack technical and business knowledge of IDSc will need to take MBA or MSBA courses to make up for any discrepancies.

Course descriptions:

IDSc 8511: Conceptual Topics and Research Methods in IDSc

This course covers the relationships of IDSc to underlying disciplines; major research streams; seminal articles; survey literature; and major researchers. Provides the framework for organizing knowledge about information and decision sciences.

IDSc 8521: Seminar in Systems Development

Concepts and practice in information systems development; process and data analysis; system development life cycle research issues; research methods with an emphasis on modeling and simulation.

IDSc 8531: Organizational Theory and Research in Information Systems

Introduction, adoption, use/exploitation of information systems in organizations. Critically examine empirical work. Formulate research questions. Conduct research.

IDSc 8541: Introduction to Economics of Information Systems

Classical research questions. Methods/findings that form the backbone of the economics of IS. Online auctions, electronic markets, off-shoring, human capital issues.

IDSc 8721: Behavioral Decision Making

Traditional/current research. Major models/methodologies. Issues of preference, judgment, and choice under conditions of certainty/uncertainty. Seminar format.

IDSc 8620: Data mining and personalization

The course provides a comprehensive overview of the exploratory and predictive machine learning methodologies and techniques, focusing on the fundamentals but covering a number of advanced issues as well, and will demonstrate how these techniques can be applied in various application areas, including text analytics as well as personalization and recommender systems. The course puts significant emphasis on practical, hands-on experience applying machine learning techniques using real-world datasets, but will also discuss the use and value of machine learning in a variety of research contexts.

IDSc 8630: Emerging Technologies: Artificial Intelligence Blockchain and Social Media 

This course covers the latest research on emerging technologies such as artificial intelligence, blockchain, metaverse, and social media. Example topics include AI transparency, AI aversion, human-robot interactions, designs and mechanisms of blockchain, the impact of augmented reality and virtual reality, peer production, and online social networks. We will be reading and discussing book chapters and articles from multiple disciplines including but not limited to information systems, human-computer interaction, management, communication, and computer science. 

IDSc 8801: Research Seminar in IDSc
New areas of research, research methods, and issues

The first-year exam consists of students reviewing a selected set of papers, with consideration given to students’ respective areas of interest. Students are asked to define their interest area (e.g., organizational, computer science, economics, or behavioral decision theory). IDS faculty choose papers to be critiqued from published and unpublished papers of varying quality. Three articles are assigned to each student usually in early May with the reviews being due in late May. After faculty examine the reviews, an oral exam takes place to provide faculty with additional input to more fully evaluate areas of weakness that may have been identified in the written reviews. Immediately after the oral exam students are given oral feedback on their performance on the exam as well as on coursework and their TA/RA assignments. Students not performing up to standards will be encouraged to address their deficiencies or withdraw from the program.

After the first-year exam, students are expected to work towards a second-year original research paper under the supervision of a second-year paper committee. The committee will be created by the end of the first year summer, consisting of three IDSc tenure track faculty members. A second-year paper proposal needs to be submitted by the beginning of September of the student's second year in the program. By the beginning of December, students should turn in a work-in-progress paper for the first mid-term review. By the beginning of May, students should turn in the first draft of their second-year paper for the second mid-term review. By the beginning of September, students should turn in the final draft of the second-year paper. The second-year paper should be independent work, and students need to be the principal writer of the paper. At the beginning of September, students will make a half-hour presentation of the second-year paper to the full IDSc faculty.

Both the second-year paper and the comprehensive exam are part of the written preliminary exam. The comprehensive exam is an in-house exam based on core materials discussed in the required IDS seminars (IDSc 8521, 8531, 8541, and 8721). The in-house exam is open book/note, conducted in mid-June for a period of two days, with three hours allocated to each subject. The comprehensive exam will be evaluated by the full IDSc tenure track faculty.

Students must schedule an oral exam before the end of the semester following completion of the written prelim exam (usually the fall semester of their third year). The format varies based on committee makeup.

Students must develop a written dissertation proposal describing their research problem and the method of addressing that problem. The proposal must be defended before the dissertation committee, generally during the fourth year.

All students are required to attend the IDS Friday workshops and PhD brownbag seminars. First and second-year students are required to discuss and obtain approval for their courses with the PhD coordinator. Third-year and above students also need the approval of the PhD Coordinator for taking non-thesis credits. Students should TA at least one MBA or MSBA course to help develop their teaching skills and also help prepare and deliver at least one class session under the tutelage of the faculty instructor. Two-course instructor experiences are expected, usually during the third and fourth years. Prior to interviewing for a faculty position, each student should have a defended proposal and present their thesis work at an IDS workshop where they can obtain constructive feedback.