Professors
The Learning Outcomes of this subject are:
- Understanding Key Concepts and Technologies: Students will comprehend the fundamental concepts and technologies underpinning Business Intelligence (BI) and Analytics.
- Analyzing and Utilizing Big Data: The course equips students to analyze and use Big Data effectively for decision support.
- Designing and Implementing Data Warehousing and Business Reporting Solutions: Participants will learn to design and implement solutions for descriptive analytics, focusing on data warehousing and business reporting.
- Applying Data Mining and Predictive Modeling Techniques: Students will gain skills in applying data mining and predictive modeling for decision support.
- Utilizing Text Analytics, Text Mining, and Web Analytics: The course covers the application of text analytics, text mining, sentiment analysis, web analytics, web mining, and social analytics for predictive analytics.
- Implementing Model-Based Decision Making and Heuristic Search Methods: Students will learn to implement model-based decision making, heuristic search methods, and simulation for prescriptive analytics.
- Utilizing Automated Decision Systems and Expert Systems: The course also teaches the use of automated decision systems, expert systems, knowledge management, and collaborative systems for advanced decision support.
1. Introduction to Business Intelligence and Analytics
2. Big Data and Analytics
3. Descriptive Analytics - Data Warehousing & Enterprise Reporting
4. Predictive Analytics - Data Mining and Predictive Modeling
5. Continuous Predictive Analytics - Text Analytics, Web Mining & Social Analytics
6. Models & Simulation / Prescriptive Analytics
7. Continuous Prescriptive Analytics - Automated Decision Systems & Knowledge Management
Weekly teaching will consist of three lecturing sessions to explain basic concepts to apply knowledge to practical situations. Exercises in class will be solved and problems will also be proposed so that students can apply the concepts learned.
Continuous assessment has the following evaluation structure:
Debates: 30%
Biweekly Case Studies: 40%
Final Assignment: 30%
The evaluation criteria apply to all the students, retakers must attend to class and submit all the deliverables requested. Any exceptional situation should be communicated previously to the professors and validated by the tutor.
The subject will be passed if the overall score is higher than 5.
RETAKE POLICY: The Retake will consist of an exam that includes all the content of the subject. All case studies homework must have been submitted to campus virtual before retake exam. Otherwise, the retake exam will not be possible.
Business Intelligence, Analytics, Data Science, and AI, 5th edition, Published by Pearson © 2024, by Ramesh Sharda Oklahoma State University | Dursun Delen Oklahoma State University | Efraim Turban Oklahoma State University, University of Hawaii ISBN-13: 978-1292459295