Management Science and Information Systems
Microcomputer-based course that provides a comprehensive understanding of computer systems and application software. Hands-on approach to learning widely used spreadsheet, database, word processor, and presentation application packages and internet tools.
Production and Operations Management
Managerial ideas and techniques for scheduling and controlling production processes and planning, organizing, and controlling functions are developed through quantitative applications; interrelationships, behavioral aspects, and practical applications.
Prerequisite: 29:623:220, 21:220:231, or equivalent
System Analysis and Design
Presents a practical approach to systems analysis and design using a blend of traditional development with current technologies. Defines and describes in detail the five phases of systems development life cycle (SDLC): systems planning, systems analysis, systems design, system implementation, and systems operations and support. Provides students with the tools for communication, economic analysis, and project planning across all phases of communication and SDLC. Furnishes students with an in-depth understanding of how information systems support business requirements in today's competitive environment.
Database Management Systems for Business Applications
Examines conceptual data modeling. Focus on identifying user information requirements and the use of commercial database management systems in designing and implementing database systems.
Designing and Creating Websites
Visual design and proper organization of interactive websites, including electronic commerce sites. Software tools for creating web material. Web design projects and critical analysis of existing website design and organization.
Data Warehousing and Data Mining
This course is an introduction to data warehousing, mining, and knowledge management. The overall objective of this course is to introduce students to both technical and managerial issues and implications for business decisions of knowledge management, data mining, and data warehousing. Through lectures, discussions, and hands-on work, students learn to use as well as understand the strategic and effective application of these technologies. The knowledge discovery process includes data selection, cleaning, coding, using different statistical pattern recognition and machine learning techniques, and reporting and visualization of the generated structures. The course will cover all these issues and will illustrate the whole process by examples of practical applications. Some topics covered include: knowledge discovery in databases, traditional statistics, neural networks, decision trees, Bayesian learning, association rules, commercial tools, feature selection, and advanced techniques. A special emphasis is made on the application domain of each method. Important related technologies such as data warehousing and Online Analytical Processing (OLAP) will be also discussed.