Overview
Business decision making requires a thorough understanding of business needs and the computational tools that allow you to optimally execute your decisions. Decision makers need firsthand, in-depth, and contextual capacity to collect business data from highly distributed systems around the globe; to employ analytics techniques to discover business relationships; to communicate and collaborate effortlessly with clients, partners, and analysts; and to evolve a highly successful business practice. You can acquire these skills and strategies by studying and utilizing a seamlessly integrated set of computational and business techniques, together referred to as business intelligence.
Analytics is a particular type of analysis, discovery, and utilization of observed and inferred data traces (e.g., usage traces) in emergent and related levels of granularity. Analytics allows consumers to make informed decisions in virtually every industry imaginable. Business intelligence is one of the key drivers of the field of data analytics. Computer Science 684: Business Intelligence explores how analytics, data science, and artificial intelligence feed on each other for effective enterprise decision support.
COMP 684 approaches business intelligence from both technological and managerial viewpoints. You may orient your study either toward implementing technologies that assist in business decision making or toward strategizing business decisions that determine technological expectations. The course closely follows the assigned digital textbook by engaging you in extensive, vivid examples from large corporations, small businesses, government, and not-for-profit agencies. Each topic addressed in the textbook analyzes business perspectives and technological advancements, and how they interrelate to open the world of business intelligence.
Outline
COMP 684 includes three units:
- Unit 1: Introduction to Analytics and Artificial Intelligence
- Unit 2: Predictive Analytics / Machine Learning
- Unit 3: Caveats of Analytics and AI
Learning outcomes
Upon successful completion of this course, you should be able to
- discuss convergence frameworks for computer decision support, artificial intelligence, and analytics.
- examine predictive analytic techniques in data mining, traditional machine learning, deep learning, and cognitive computing.
- examine the implementation issues of business intelligence applications, including ethics, privacy, organizational impacts, and societal impacts.
Evaluation
To receive credit for COMP 684, you must achieve a grade of at least 60 percent on Assignment 1, at least 60 percent on Assignment 2, and at least 60 percent on the study journal.
To receive credit for COMP 684 as an Elective course in the Master of Science in Information Systems program, you must achieve a course composite grade of at least B– (70 percent).
The weighting of the composite grade is as follows:
| Activity | Weight |
| Assignment 1 | 70% |
| Assignment 2 | Pass/fail |
| Study journal | 30% |
| Total | 100% |
Materials
Digital course materials
Links to the following course materials will be made available in the course:
Sharda, R., Delen, D., & Turban, E. (2020). Analytics, data science, & artificial intelligence: Systems for decision support (11th ed.). Pearson.