|Website:||website containing additional information|
|Period:||period 4 (week 17 through 26, i.e., 26-4-2021 through 2-7-2021; retake week 28)
|Participants:||up till now 0 subscriptions|
|Schedule:||Official schedule representation can be found in MyTimetable|
|Note:||No up-to-date course description available.|
Text below is from year 2019/2020
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 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:||Due to the current situation, the course will be offered as a online-only course in the academic year 2019-2020. This means that video recordings of the lectures will be provided (which are accessible at any time), and there will be an interactive session on Tuesdays.
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, see also the suggested modules at DataCamp.
- Tue 09:15-10:45 - Interactive session for questions regarding the lecture (Georg, per video conferencing/chat).
- Tue 11:00-12:30 - Interactive exercise/tutorial class with the student teaching assistant (per video conferencing/chat).
In short, your deliverables in the course are:
More details are below.
- Final exam
- Taking at least 4 out of 5 tests (the best four of the five are considered in grading)
- Practical projects
- 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 * Practical) + (OptionalParticipationBonus)
- 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.
- There is one second-chance exam opportunity for the Exam in July, before the summer break.
- PRACTICE: Individual practical project, consisting of several deliverables (tasks). Their scores are summed up to the overall practical grade, which corresponds to 50 percent of the course grade.
- In case one deliverable fails, a retake opportunity will be provided.
IMPORTANT: In order to pass this course, you need to have scored at least positive for (a) the Exam and (b) the Practical.
|Minimum effort to qualify for 2nd chance exam:||To qualify for the retake exam, the grade of the original must be at least 4.|