|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 58 subscriptions|
|Schedule:||Official schedule representation can be found in MyTimetable|
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
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
|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: Data Warehousing, Descriptive Statistics, 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 2020-2021. There will be interactive sessions on Tuesdays and Thursdays via MS Teams, as scheduled.
For announcements and the detailed schedule, see blackboard.
The projects in this course will be centred around either R or Python , in connection with PostgreSQL as DBMS. To help you getting started with these tools, see also the suggested modules at DataCamp.
- Tue 09:15-11:00 - Interactive session for the lecture (Georg).
- Tue 11:00-12:30 - Interactive exercise/tutorial class with the student teaching assistants (per video conferencing/chat).
- Thu 15:15-17:00 - Lectures (Georg), Project Presentations.
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 individual assignments
- Practical team project
- 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) + (0.2 * Individual Practical Assignments)+ (0.3 * Practical Team Project) + (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: 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.
IMPORTANT: 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.
|Minimum effort to qualify for 2nd chance exam:||Om aan de aanvullende toets te mogen meedoen moet de oorspronkelijke uitslag minstens 4 zijn.|