Computer Science (COMP) 684

Overview

Business decision making requires a thorough understanding of business needs as well as computational tools that allow one to optimally execute decisions. Optimal execution implies that decision makers need first-hand, in-depth, and contextual capacity to collect business data from highly distributed systems around the globe; to employ analytic techniques to discover business relationships; to communicate and collaborate effortlessly with clients, partners, and analysts; and to evolve a highly successful business practice. Decision makers can acquire these skills and strategies by studying and using a seamlessly integrated set of computational and business techniques, together referred to as business intelligence (BI).

The course approaches BI from both technological and managerial viewpoints. Learners are welcome to orient their study either towards implementing technologies that support managerial approaches or towards business strategies that meet technological expectations. The course closely follows the textbook to engage learners in extensive, vivid examples from large corporations, small businesses, government, and not-for-profit agencies. Each topic addressed in the book analyzes business perspectives, technological advancements, and how they interrelate to open the world of BI. The course will also explore how BI and business analytics feed each other for effective enterprise decision support.

Course Objectives

The objectives of this course are to provide graduate students in M.Sc. Information Systems with comprehensive and in-depth knowledge of BI principles and techniques by introducing the relationship between managerial and technological perspectives. This course is also designed to expose students to the frontiers of BI-intensive big data computing and information systems, while providing a sufficiently strong foundation to encourage further research.

Learning Outcomes

After successfully completing this course, students will be able to

• relate the major frameworks of computerized decision support: decision support systems (DSS) and BI;
• explain the foundations, definitions, and capabilities of DSS and BI;
• describe DSS components and technology levels;
• summarize the various types of DSS and explain their use;
• demonstrate the importance of models and model management;
• apply data mining, neural networks, text mining, web mining, data warehousing, and business performance management;
• evaluate IT-based collaboration and communication to support group work in decision making;
• relate capabilities of groupware and group support systems to decision making;
• relate knowledge management to collaboration and communication;
• explain the foundations, definitions, and capabilities of artificial intelligence and knowledge-based systems;
• apply technologies such as expert systems, case-based reasoning, genetic algorithms, fuzzy logic, support vector machines, and intelligent software agents to develop intelligent DSS.
• apply some of the emerging technologies such as RFID, virtual worlds, Web 2.0, and big data analytics that may impact management support systems.

Outline

Unit 1: Introduction

• Introduces a collection of computer technologies that support managerial work—essentially, decision making—and explores their impact on corporate strategy, performance, and competitiveness.

Unit 2: Decision Support

• Concentrates on decision making, decision support methodology, technology components, and development.

Unit 3: Business Intelligence Techniques

• Explores computing-specific business intelligence techniques such as neural networks, web mining, warehousing, and business analytics in the context of business performance management.

Unit 4: Distributed Enterprise

• Investigates collaborative computing in several business situations and settings, supporting geographically and technologically distributed people, and leading to group decision support and knowledge management.

Unit 5: Using Artificial Intelligence

• Introduces artificial intelligence techniques such as expert systems, support vector machines, genetic algorithms, case-based reasoning, and software agents in the context of BI.

Unit 6: Emerging Technologies

• Introduces several emerging technologies that offer opportunities for application and extension of BI techniques and management support systems.

Evaluation

To pass this course, students must achieve an average grade of at least 60% in each assignment and at least 60% on the final assessment. Students will work in small groups to solve and submit assignment problems. The final assessment is a 3-day take-home assessment based on individual work.

To receive credit courses, you must achieve a course composite grade of at least C+ (66%).

The weighting of the composite grade is as follows: