This course has been designed with the following learning objectives in mind:

• Understand the fundamentals of the collection of technologies called Business Intelligence (BI)
• Understand the relationships between BI technologies within a typical BI architecture
• Relate the theoretical foundations to professional experiences in daily practice
• Experience the typical steps performed in a BI implementation project
• Obtain hands-on experience with professional BI tooling
• Understand current state-of-the-art research in BI technologies

During this course the following BI topics will be covered:

• Overview on Business Intelligence, Analytics and Data Science
• Business perspective, context and implementation of BI
• Data warehousing, data management, data integration, data preprocessing
• Descriptive Analytics: Descriptive statistics, online analytical processing (OLAP), visualisation and business reporting
• Predictive Analytics: Data mining with overviews on clustering, classification, time series analysis, frequent itemset mining
• Prescriptive Analytics: Optimisation
• Ethics, Privacy and Managerial Considerations
• Big Data and Future Trends

Assessment

• Final exam
• Taking at least 4 out of 5 tests (the best four of the five are considered in grading)
• Practical assignments
• Practical team project

in preparation for the above, it is highly recommended to prepare the - mostly - weekly exercises (but they are not mandatory).

Balancing out theory, practice and participation on BI is also reflected in the course grading balance:

• Theory
  One individual written exam on ALL topics covered during the course including any guest lectures and extra assignments, as well as five multiple-choice mini-tests. Only four of these five grades will be included in the final course formula, which means that you can miss out on one mini-exam in case of sickness or other misfortunes.
• Practice
  Several small individual practical assignments, plus one big integrative practical team project. Together, these account for 50 percent of the course grade.
  In case one deliverable fails, a retake opportunity will be provided.

Final grade: (4 * (0.025 * Tests)) + (0.4 * Exam) + (0.25 * Individual Practical Assignments)+ (0.25 * Practical Team Project) + (Optional Participation Bonus)

In order to pass this course, you need to have scored at least 55 percent on each of the following:
(a) the final exam
(b) the arithmetic average of small individual assignments, and
(c) the practical team project .
The participation bonus will be worth up to 0.025 of the overall points for outstanding contributions to the course.

A repair test requires at least a 4 for the original test.

This course deals with a collection of computer technologies that support managerial decision making by providing information of both internal and external aspects of operations.
They have had a profound impact on corporate strategy, performance, and competitiveness, and are collectively known as business intelligence.

The course has the following structure:

1. An overview of Business Intelligence, Analytics, and Decision support
2. Descriptive Analytics: Data Warehousing, Descriptive Statistics, Business Reporting, Visual Analytics, and Business Performance Management
3. Predictive Analytics: Data Mining
4. Prescriptive Analytics: Optimization and Simulation
5. Ethics, Privacy, Legal and Managerial Considerations

Course form
Lectures, interactive sessions.

Literature

• Sharda,R., Delen,D., Turban,E.. "Business Intelligence: A Managerial Approach", 2018, Global 4th Edition. Pearson. ISBN-10 1292220546 (previous editions are most likely fine as well, but might lack some of the chapters on, e.g. big data and prescriptive analytics)
• Sherman, R. "Business Intelligence Guidebook: From Data Integration to Analytics", 2015, 1st Edition. Morgan Kaufmann

Also, a number of recent journal papers will be part of the study materials. Please refer to the Blackboard course page for more information.

In addition, this course is supported by an educational group on DataCamp.com, a data science learning platform.