Information Systems

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.

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 course focuses on data and database management, with an emphasis on modeling and design, and their application to decision support. The course is organized around the following general themes: Strategic Data Planning, Data Governance, Enterprise Data Integration, Data Management Approaches, Data Design for Transaction Processing vs. Decision Support, Data Management Functions, Abstraction and Modeling, Data- and Information Modeling (ER, Object-oriented), Database Schemas (Conceptual Schema), Database Design (Functional Dependencies and Normalization), Query languages (SQL, DDL, QBE), Metadata Development and Application, Data Quality Approaches, Master and Reference Data Management (e.g., Customer and Product Data), Temporal Data, Data, Analytics, and Business Performance, Introduction to Data Warehousing, OLAP, OLTP, and Data Mining, Strategic Data Policies and Guidelines (e.g. Enterprise Data and Integration, Governance, Markets, Customers, and Competitors, Leadership, Analysts and Knowledge Worker Skills and Training, Communities of Analysts). There are numerous case studies and modeling projects throughout the course.

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 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 streamline business systems, are covered.

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. Instead, this course concentrates on the interdependencies among these architectures. In addition, this course will utilize management research on organizational integration and coordination. 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 for coordination available, and create a process for establishing and maintaining an ongoing enterprise architecture.

The objective of this course is to address the important question, "How does one 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, strategic alignment, methods/metrics for building strategies, and achieving alignment.

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.

This course 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 concentration offers a deeper dive into big data and its applications in business, as well as specific analytic tools. 

• MIS 635 Designing the Knowledge Organization

• MIS 636 Data Warehousing and Business Intelligence

• MIS 637 Data Analytics and Machine Learning OR 1 Elective

Students in this concentration will specialize in understanding effective business processes and supply-chain management. Students in this concentration can choose one of the following two:

• MIS 690 Supply Chain Management and Strategy OR BIA 674 Supply Chain Analytics

Students in this concentration can choose one of the following two:

• MIS 712 Advanced Business Process Management OR BIA 650 Process Optimization and Analytics

• MIS 714 Service Innovation

This concentration gives students the perspective and technical skill required to work in the highly fluid area of cybersecurity. Courses taken as part of this concentration will support your preparation for important industry certification exams. 

• FIN 545 Risk Management for Financial Cybersecurity

• MIS 645 Cybersecurity Principles for Managers

• MIS 720 Managing Enterprise Network Security Architectures

With advisor permission, you may substitute one of the above courses with a different cybersecurity elective.

Your application must include two letters of recommendation. The strongest applications will include one letter from a current supervisor, and one from a former supervisor or previous employer who can speak to your leadership potential and discuss your professional performance. 

Stevens often invites master's candidates to interview prior to making an admissions decision. If you are selected for an interview after submitting your application, you will receive instructions via email.

You must include a résumé with your application that highlights: 

• Academic record

• Work and internship experience

• Leadership abilities

Your application must include official transcripts from all universities you have attended. These records must show your name, the name of the university attended, enrollment dates, coursework completed, and grades assigned.