Online Information Systems Master's Program

Corporate financial management requires the ability to understand the past performance of the firm in accounting terms; while also being able to project the future economic consequences of the firm in financial terms. This course provides the requisite survey of accounting and finance methods and principles to allow technical executives to make effective decisions that maximize shareholder value.

This 2-credit course focuses on data and database management, with an emphasis on modeling and design, and their application to business decision making. The course provides a conceptual understanding of both organizational and technical issues associated with data. The central theme concerns data modeling and databases. We examine organizational approaches to managing and integrating data. Among the topics included are normalization, entity-relationship modeling, relational database design, SQL, and data definition language (DDL). Discussed are specific applications such as strategic data management, master data management, and physical database design. The course concludes with a brief overview of Decision Support Systems, data warehousing and business intelligence, NoSQL databases (e.g., MongoDB) and cloud computing. The course includes a number of cases studies and modeling and design projects. Students in MIS 631 must also enroll in the associated 1-credit lab course MIS 632 Managing Data Lab.

This 1-credit lab course provides an experiential learning component for MIS 631 Data Management for which it is a co-requisite. MIS 632 provides hands-on experience in designing, implementing, and querying data bases. The relevant software is introduced using demonstrations, in-class exercises and homework exercises that are closely tied to and executed in synch with the conceptual and theoretical material covered in MIS 631. Specifically, students will gain hands-on experience in: (i) ERWIN - a widely used commercial tool for representing conceptual (e.g., E-R diagrams) and logical data models (e.g., relational DBMS), (ii) PostgreSQL (relational database software), (iii) SQL Structured Query Language) and (iv) MongoDB a NoSQL document data store. Students in MIS 632 must also be enrolled in the associated 2-credit lecture course MIS 631 Managing Data course.

Many managerial decisions - regardless of their functional orientation - are increasingly based on analysis using quantitative models from the discipline of management science. Management science tools, techniques and concepts (e.g., data, models, and software programs) have dramatically changed the way businesses operate in manufacturing, service operations, marketing, transportation, and finance. Business Analytics explores data-driven methods that are used to analyze and solve complex business problems. Students will acquire analytical skills in building, applying and evaluating various models with hands-on computer applications. Topics include descriptive statistics, time-series analysis, regression models, decision analysis, Monte Carlo simulation, and optimization models.

This course deals with the basic problems of managing a project, defined as a temporary organization built for the purpose of achieving a specific objective. Both operational and conceptual issues will be considered. Operational issues include definition, planning, implementation, control, and evaluation of the project. Conceptual issues include project management vs. hierarchical management, matrix organization, project authority, motivation, and morale. Cases will be used to illustrate problems in project management and how to resolve them.

This course focuses on the role of information technology (IT) in reengineering and enhancing key business processes. The implications for organizational structures and processes, as the result of increased opportunities to deploy information and streamlining business systems are covered.

IT organizations must be able to leverage new technologies. This course focuses on how organizations can effectively and efficiently assess trends and emerging technologies in data and knowledge management, information networks, and analyzing and developing application systems. Students will learn how to help their organizations define, select, and adopt new information technologies.

This course focuses on the issues surrounding the design of an overall information technology architecture. The traditional approach in organizations is to segment the problem into four areas - network, hardware, data and applications. This course will focus on the interdependencies among these architectures. In addition, this course will utilize management research on organizational integration and coordination science. The student will learn how to design in the large, make appropriate choices about architecture in relationship to overall organization goals, understand the different mechanisms available for coordination and create a process for establishing and maintaining an enterprise architecture.

The objective of this course is to address the important question, "How to improve the alignment of business and information technology strategies?" The course is designed for advanced graduate students. It provides the student with the most current approaches to deriving business and information technology strategies, while ensuring harmony among the organizations. Topics include business strategy, business infrastructure, IT strategy, IT infrastructure, strategic alignment, methods/metrics for building strategies and achieving alignment.

This course focuses on the design and management of data warehouse (DW) and business intelligence (BI) systems. The course is organized around the following general themes: Knowledge Discovery in Databases, Planning and Business Requirements, Architecture, Data Design, Implementation, Business Intelligence, Deployment, Maintenance and Growth, and Emerging Issues. Practical examples and case studies are presented throughout the course. This course also includes hands-on application in various software packages.

This course will focus on Data Mining & Knowledge Discovery Algorithms and their applications in solving real world business and operation problems. We concentrate on demonstrating how discovering the hidden knowledge in corporate databases will help managers to make near-real time intelligent business and operation decisions. The course will begin with an introduction to Data Mining and Knowledge Discovery in Databases. Methodological and practical aspects of knowledge discovery algorithms including: Data Preprocessing, k-Nearest Neighborhood algorithm, Machine Learning and Decision Trees, Artificial Neural Networks, Clustering, and Algorithm Evaluation Techniques will be covered. Practical examples and case studies will be present throughout the course.