| Website: | website containing additional information |
| Course code: | INFOMBIN |
| Credits: | 7.5 ECTS |
| Period: | period 4 (week 17 through 26, i.e., 26-4-2021 through 2-7-2021; retake week 28)
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| Timeslot: | B |
| Participants: | up till now 0 subscriptions |
| Schedule: | Official schedule representation can be found in MyTimetable |
| Teachers: |
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| Note: | No up-to-date course description available.
Text below is from year 2019/2020 |
| Contents: | 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:
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Understand the fundamentals of the collection of technologies called Business Intelligence (BI)
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Understand the relationships between BI technologies within a typical BI architecture
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Relate the theoretical foundations to professional experiences in daily practice
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Experience the typical steps performed in a BI implementation project
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Obtain hands-on experience with professional BI tooling
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Understand current state-of-the-art research in BI technologies
During this course the following BI topics will be covered:
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Business perspective
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Statistics
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Data management
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Data integration
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Data warehousing
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Data mining
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Reporting and online analytic processing (i.e., descriptive analytics)
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Quantitative analysis and operations research (i.e., predictive analytics)
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Management communications (written and oral)
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Systems analysis and design
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Software development
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| Literature: | May change!
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:
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An overview of Business Intelligence, Analytics, and Decision support
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Descriptive Analytics: Descriptive Statistics, Data Warehousing, Business Reporting, Visual Analytics, and Business Performance Management
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Predictive Analytics: Data Mining
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Prescriptive Analytics: Optimization and Simulation
- Ethics, Privacy, Legal and Managerial Considerations
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.
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| 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.
- 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).
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.
In short, your deliverables in the course are:
- 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).
More details are below. |
| Exam form: | 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.
- 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.
Final grade: (4 * (0.025 * Tests)) + (0.4 * Exam) + (0.5 * Practical) + (OptionalParticipationBonus)
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. |