Department of Information and Computing Sciences

Departement Informatica Onderwijs
Bachelor Informatica Informatiekunde Kunstmatige intelligentie Master Computing Science Game&Media Technology Artifical Intelligence Human Computer Interaction Business Informatics

Onderwijs Informatica en Informatiekunde

Vak-informatie Informatica en Informatiekunde

Business intelligence

Website:website containing additional information
Course code:INFOMBIN
Credits:7.5 ECTS
Period:period 4 (week 17 through 26, i.e., 22-4-2019 through 28-6-2019; retake week 28)
Timeslot:B
Participants:up till now 97 subscriptions
Schedule:Official schedule representation can be found in Osiris
Teachers:
formgrouptimeweekroomteacher
lab session group 1 Tue 11.00-12.4517-25 RUPPERT-011 studentassistent KN
group 2 Tue 11.00-12.4517-25 BOL-1.138 studentassistent JD
lecture   Tue 9.00-10.4517 RUPPERT-D Georg Krempl
18 RUPPERT-ROOD
19 RUPPERT-PAARS
20-21 RUPPERT-ROOD
22-24 EDUC-MEGARON
25 RUPPERT-ROOD
Thu 15.15-17.0017-21 RUPPERT-ROOD
23-25 RUPPERT-ROOD
Thu 17.15-19.0018-20 RUPPERT-ROOD
25 RUPPERT-ROOD
tutorial group 1        Armel Lefebvre
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:
  • 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 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:

    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. 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.
    Course form:In general, the course is structured around the principle of 6 contact hours a week. For announcements and the detailed schedule, see blackboard:
    • Tue 09:15-10:45 - Lectures (Georg).
    • Tue 11:00-12:30 - Exercise/Tutorial classes by Armel and student teaching assistants (starting on April 30th).
    • Thu 15:15-16.45 - Lectures (Georg), Tutorials, Project Presentations.
    • Thu 17:00-18:00 (only upon announcement, see blackboard) Tutorials, Project Presentations
    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.

    In short, your deliverables in the course are:

    • written final exam (closed book, one page cheat sheet allowed)
    • 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, participation in presentation, discussion, participation in peer review, 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 CLOSED BOOK (with one A4-sized page cheat sheet allowed) 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: One BI team project grade, consisting of the final project deliverables: working prototype, peer reviewing, 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 * Tests)) + (0.4 * Exam) + (0.5 * Project) + (OptionalParticipationBonus)

    IMPORTANT: In order to pass this course, you need to have scored at least positive 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.
    wijzigen?