PhD Courses – Track Specific

BUSADM 740 – Information Systems Theory I

This course is the first part of two-course series of Ph.D. seminars on classic literature of information systems. It is designed to provide doctoral students a broad introduction to various research issues and challenges in topics of information systems (IS) and information technology (IT) management. As the first one of this series, this course is focused on theories at the behavioral and group levels. Typical topics covered in the course include, but are not limited to, technology adoption and diffusion, IT-enabled communication, decision support, virtual teams, online community, cultural and power issues in IT activities, and other emerging topics in the research field.

BUSADM 741 – Information Systems Theory II

This course is the second part of two-course series of Ph.D. seminars on classic literature of information systems. It is designed to provide doctoral students specialized in information systems and business analytics a broad introduction to various research issues and challenges in topics of information systems (IS) and information technology (IT) management. As the second one of this series, this course is focused on theories at the organizational and economic levels. Topics covered include strategic IT planning, business value of IT, IT strategies, IT governance and controls, IT sourcing models, electronic marketplaces, economics of digital products, data science and business analytics, and other emerging topics in the research field.

BUSADM 742 – Regression   

This course will introduce the fundamental concepts and applications of linear regressions, such as simple linear regression, multiple regression, model fit, transformations, variable selection and logistic regression etc., and also various issues that we might face during those applications.  This course will be the foundation for applied quantitative research.

BUSADM 743 – Decision Analysis

Decision and risk analysis combine elements of probability, economics, logic, psychology and domain knowledge to characterize and analyze complex decision problems. Researchers in this scholarly discipline develop theoretical mathematical results, develop computational decision support tools grounded in formal theory, methods for populating models, as well as a large number of applied models for different real world problems or problem classes. Students will gain familiarity with the basic theory and methods from classic and recent texts, and will examine some real world applications from recent journal publications. There will be particular focus on connections between the approaches covered and developments in information systems and in analytics. The course will involve portions of problem sets, student led discussions. Students emerging from the class will be prepared to incorporate decision analysis into research involving applications or IS/Analytics, or to further investigate decision analysis in order to research in the methods of the field itself. Students will also keep a journal of ideas one of which will be the basis for a project or research paper that has the potential for expansion into publishable results.

BUSADM 744 – Quantitative Research

This course focuses on understanding, evaluating, and designing quantitative methods and methodologies for information systems research.  Through this course, students will review and exercise the basic skills required for quantitative research at the post-graduate level, including literature review, research design, data collection and analysis, and report writing.  To gain hands-on experience, students will work on an original research project during the semester and will be expected to submit a research outcome to an IS journal or conference.  This course will be especially helpful to students who wish to use the quantitative research methods (e.g., survey, experimental and/or quasi-experimental methods) in their dissertations and subsequent research endeavors.

BUSADM 745 – Multivariate Statistics

The goal of this course is to develop skills necessary in analyzing problems in which multiple variables are simultaneously present, without knowing beforehand which ones are playing important roles and hence are of interest, and which ones are not. Our main goal is to identify the signal or key features of the data. The course will cover the major techniques in this field. The focus will be on practical issues such as selecting the appropriate approach and how to prepare the data.

BUS ADM 775: Teaching and Professional Development

As an advanced student of business, skills are needed to effectively and persuasively disseminate knowledge. This course will provide knowledge needed to engage an audience (with specific applications on teaching), giving professional presentations, and being persuasive on policy matters informed by research.

BUSADM 780 – Advanced Data Mining and Predictive Modeling

One of premiere challenges businesses face today is how to take advantage of the vast amounts of data they can easily collect. Data mining is used to find patterns and relationships in data, and is integral to business analytics and fact-based decision-making. This course covers current data mining techniques including algorithms for classification, association, and clustering; the course also covers text mining techniques such as Latent Semantic Analysis and Latent Dirichlet allocation. Current software tools will be introduced to apply data mining techniques with approaches used for building effective models, such as sampling strategies, data transformation, feature selection and ensemble methods, will be incorporated. The techniques and approaches covered in this course will be examined in the context of current research and methodological use in the field of Information Systems.

BUSADM 782 – Optimization

This course teaches optimization theory and techniques that are powerful and important tools for conducting research in Data Science area. Optimization techniques can be used for mining and analytics of complex systems in Data Science field, which can greatly impact the decision making process in this area. This course covers mathematical programming techniques including linear programming, integer programming, and network optimization; and emphasizes on how they can be applied to research problems. It focuses on effective formulation techniques, basic mathematical and algorithmic concepts, and software solution of large-scale problems arising in Data Science applications.

BUSADM 785 – Big Data

This course covers a new and increasingly popular method of conducting research using large scale data analysis. The advent of the Internet, Social Media and subsequently machine generated data has enabled social scientists to have access to extremely large datasets about the behavior of millions (or billions) of people or objects. However, collecting, storing, and analyzing this data isn’t straightforward and requires specific skills.

The goal of this course is to help students gain the skills required for this type of research while exposing them to tools and big data research streams. The course will help students understand both the challenges and the opportunities and assist them to appreciate research related to Big Data.