The required courses for the MIS PhD program are listed below. In addition, students must register for 18 credits of MIS 920 (Dissertation).
This course introduces the student to fundamentals of database analysis, design and implementation. Emphasis is on practical aspects of business process analysis and the accompanying database design and development. Topics covered include: conceptual design of databases using the entity relationship model, relational design and normalization, SQL and PL/SQL, web based database design and implementation using Oracle or some other modern Database Management Systems. Students are required to work with a local client organization in understanding their business requirements, developing a detailed set of requirements to support business processes and designing and implementing a web based database application to support their day- to-day business operations and decision making. Students will acquire hands-on-experience with a state-of-the-art database management system such as Oracle or Microsoft SQLServer, and web-based development tools. This course is also offered online.
Prerequisite: MIS 541 or consents of instructor
This course is designed to introduce fundamental statistical principles and modern applied machine learning techniques. The first part will cover the basics of classical probability theory and statistical inference. The second part will introduce statistical learning, with particular attention paid to R implementations. Examples are drawn from marketing, finance and other areas for illustration.
Introduces beginning doctoral degree students and advanced master's degree students to important research and survey articles in the field of management information systems.
Provides a knowledge of research methodologies used in the MIS discipline, including experimental design, surveys, case studies, field work and software engineering.
This full semester class will meet once a week for 2.5-3 hours. The objective of this class is to introduce second year MIS PhD students to research in the economics of information systems. The class will organized into three parts: (1) overview of economics of IS core concepts and historically major themes of research, (2) commonly used methodologies in Econ of IS research (econometrics, analytical models, experiments and newer approaches) and (3) contemporary themes and research topics.
This will be a readings and discussion class, with heavy emphasis on both reading and discussion and four-to-six research articles assigned for readings every week. Students will be expected to read and digest every article discussed in class. Class periods will focus heavily on student-run discussions. By the end of the semester, students in this class should be confident in their ability to review Econ of IS papers, pursue Econ of IS research projects, know what courses they should take to develop further expertise and have produced at least one work-in-progress research paper in economics of information systems.
This PhD level course aims to provide the foundation and knowledge in state-of-the-art data, text and web mining research for various structured, unstructured and web-based, data-centric applications. Students will become familiar with key data, text and web mining computational methods and techniques. They will also learn to apply such analytical techniques and related methodologies in advanced business, scientific or web research.
This course will introduce doctoral students to quantitative methods in network science used to model, analyze, and understand various complex systems and the unique interactions among their components. Topics to be covered include the mathematics of networks (graph theory), data analysis, and applications to technology, business, biology, medicine/healthcare, and other relevant fields. Students will learn about ongoing research in the field, and ultimately apply their knowledge to conduct their own analysis of a large real world complex system and corresponding dataset(s) of their choosing as part of the final research paper. You will learn fundamental network theory including, properties of networks, measures and metrics such as centrality, transitivity, reciprocity, homophily, and others. You will cover concepts such as small-world effects, modularity, degree distributions, and assortative mixing. Algorithms on graphs including traversals, and graph partitioning will be explained. You will study random graphs models together with graph clustering methods, small and giant components, power-law distribution, and others. You will also study the concepts of percolation and network reliance, epidemics, and dynamic systems as well as signed networks. Students at the end of the system will have gained knowledge to analyze complex systems by modeling interactions among the components as a network. Students will also learn to analyze these networks by implementing and using various algorithms and visualizing the results of the algorithms and ultimately integrating networks with prediction models. The course is expected to use Python, GEPHI, and SNAP to construct and analyze large datasets using network science.
The development and exchange of scholarly information, usually in a small group setting. The scope of work shall consist of research by course registrants, with the exchange of the results of such research through discussion, reports and/or papers.
Qualified students working on an individual basis with professors who have agreed to supervise such work. Graduate students doing independent work which cannot be classified as actual research will register for credit under course number 599, 699 or 799.
Course can qualify for 1 to 6 credits.
Research for the doctoral dissertation (whether library research, laboratory or field observation or research, artistic creation or dissertation writing).