Department of Information and Computing Sciences

Departement Informatica Onderwijs
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Onderwijs Informatica en Informatiekunde

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Business intelligence

Website:website containing additional information
Course code:INFOMBIN
Credits:7.5 ECTS
Period:period 4 (week 17 through 26, i.e., 23-4-2018 through 29-6-2018; retake week 28)
Participants:up till now 75 subscriptions
Schedule:Official schedule representation can be found in Osiris
Teachers:Dit is een oud rooster!
lab session group 1 Tue 11.00-12.4517 BBG-201 Armel Lefebvre
studentassistent KF
20-25 BBG-201
group 2 Tue 11.00-12.4517-25 KBG-228 studentassistent VG
lecture   Tue 9.00-10.4517-25 ANDRO-C101 Georg Krempl
Thu 15.15-19.0017-18 ANDRO-C101
20-25 ANDRO-C101
week: 26Thu 27-6-201913.30-16.30 uurroom: EDUC-THEATRON
week: 28Thu 11-7-201913.30-16.30 uurroom: BBG-223retake exam

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:
  • 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
During this course the following BI topics will be covered:
  • Business perspective
  • Statistics
  • Data management
  • Data integration
  • Data warehousing
  • Data mining
  • 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
  • Software development
Literature: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:

    1. An overview of Business Intelligence, Analytics, and Decision support
    2. Descriptive Analytics: Descriptive Statistics, Data Warehousing, Business Reporting, Visual Analytics, and Business Performance Management
    3. Predictive Analytics: Data Mining
    4. Prescriptive Analytics: Optimization and Simulation
    5. Big Data Concepts and Tools
    6. Future Trends, Privacy and Managerial Considerations
    Also, a number of recent journal papers will be part of the study materials. Please refer to the - course page for more information. In addition, this course is supported by an educational group on, a data science learning platform.
    Course form:In general, the course is structured around the principle of 6 contact hours a week:
    • 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 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.

    In short, your deliverables in the course are:

    • 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).
    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 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.
    Final grade: (4 * (0.025 * McExam)) + (0.4 * Exam) + (0.5 * Project) + (OptionalParticipationBonus)

    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:Om aan de aanvullende toets te mogen meedoen moet de oorspronkelijke uitslag minstens 4 zijn.