|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 1 subscriptions|
|Schedule:||Official schedule representation can be found in Osiris|
|Note:||No up-to-date course description available.|
Text below is from year 2017/2018
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
This course will adopted the following textbook:
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
This course book has more or less 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
Big Data Concepts and Tools
- Future Trends, Privacy and Managerial Considerations
|Course form:||In general, the course is structured around the principle of 6 contact hours a week:
In contrast to previous years, 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.
Thus, in the first week(s), we will have lectures/tutorials instead of the exercise class/consultation. That is, the lecture (resp. tutorial) will be on Tuesday from 9:15-12:30, and on Thursday from 15:15-18:30.
- Tue 09:15-10:45 - Lectures, either by Georg or a guest.
- Tue 11:00-12:30 - Exercise/Tutorial classes by Armel, Kristof, and Vincent.
- Thu 15:15-16.45 - Lectures, either by Georg or a guest.
- Thu 17:00-18:00 - Consultation/Tutorials with Armel, Kristof, and Vincent about your BI team project.
In short, your deliverables in the course are:
More details are below.
- written final exam
- 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, presentation, discussion 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 * McExam)) + (0.4 * Exam) + (0.5 * Project) + (OptionalParticipationBonus)
- THEORY: One individual written OPEN BOOK exam on ALL topics covered during the course including any guest lectures and extra assignments, as well as five multiple-choice mini-exams (CH2,CH3,CH4,CH5,CH6+7). 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, 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 a 5.0 for (a) the written Exam and (b) the Project.
|Minimum effort to qualify for 2nd chance exam:||To qualify for the retake exam, the grade of the original must be at least 4.|