|Website:||website containing additional information|
|Period:||period 4 (week 17 through 26, i.e., 22-4-2019 through 28-6-2019; retake week 28)
|Participants:||up till now 97 subscriptions|
|Schedule:||Official schedule representation can be found in Osiris|
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.
This course has been designed with the following learning objectives in mind:
During this course the following BI topics will be covered:
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
Reporting and online analytic processing (i.e., descriptive analytics)
Quantitative analysis and operations research (i.e., predictive analytics)
Management communications (written and oral)
Systems analysis and design
|Literature:||This course will adopted the following textbooks:
Sharda,R., Delen,D., Turban,E. (2018). Business Intelligence: A Managerial Approach, 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. (2015). Business Intelligence Guidebook: From Data Integration to Analytics, 1st Edition. Morgan Kaufmann
This course has the following structure:
Also, a number of recent journal papers will be part of the study materials. Please refer to the uu.blackboard.com - course page for more information.
In addition, this course is supported by an educational group on DataCamp.com, a data science learning platform.
An overview of Business Intelligence, Analytics, and Decision support
Descriptive Analytics: Descriptive Statistics, Data Warehousing, Business Reporting, Visual Analytics, and Business Performance Management
Predictive Analytics: Data Mining
Prescriptive Analytics: Optimization and Simulation
- Ethics, Privacy, Legal and Managerial Considerations
|Course form:||In general, the course is structured around the principle of 6 contact hours a week. For announcements and the detailed schedule, see blackboard:
The projects in this course will be centred around R , in connection with PostgreSQL as DBMS. To help you getting started with these tools, there will be a tutorial (given by Armel) in the first weeks of the course.
- Tue 09:15-10:45 - Lectures (Georg).
- Tue 11:00-12:30 - Exercise/Tutorial classes by Armel and student teaching assistants (starting on April 30th).
- Thu 15:15-16.45 - Lectures (Georg), Tutorials, Project Presentations.
- Thu 17:00-18:00 (only upon announcement, see blackboard) Tutorials, Project Presentations
In short, your deliverables in the course are:
More details are below.
- written final exam (closed book, one page cheat sheet allowed)
- taking at least 4 out of 5 tests (the best four of the five are considered in grading)
- project deliverables (BI strategy, final report, ETL with analytics and dashboard, participation in presentation, discussion, participation in peer review, and individual contribution)
- and, in preparation for the above, it is highly recommended to prepare the (mostly) weekly exercises (but they are not mandatory).
|Exam form:||Balancing out theory, practice and participation on BI is also reflected in the course grading balance:
Final grade: (4 * (0.025 * Tests)) + (0.4 * Exam) + (0.5 * Project) + (OptionalParticipationBonus)
- THEORY: One individual written CLOSED BOOK (with one A4-sized page cheat sheet allowed) 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.
- There is one second-chance exam opportunity for the Exam in July, before the summer break.
- PRACTICE: One BI team project grade, consisting of the final project deliverables: working prototype, peer reviewing, oral presentation, written report. Furthermore, your performance in various assignments, talks, and participation may influence your final grade.
- There is one second-chance report revision opportunity, which needs to be confirmed beforehand by the course team, by properly submitting your revised report second-chance exam date.
IMPORTANT: In order to pass this course, you need to have scored at least positive for (a) the written Exam and (b) the Project.
|Minimum effort to qualify for 2nd chance exam:||Om aan de aanvullende toets te mogen meedoen moet de oorspronkelijke uitslag minstens 4 zijn.|