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., 26-4-2021 through 2-7-2021; retake week 28)
Timeslot:B
Participants:up till now 58 subscriptions
Schedule:Official schedule representation can be found in MyTimetable
Teachers:
formgrouptimeweekroomteacher
lecture          Georg Krempl
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:
  • 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:

    1. An overview of Business Intelligence, Analytics, and Decision support
    2. Descriptive Analytics: Data Warehousing, Descriptive Statistics, 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: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.
    • 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.
    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.

    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 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).
    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: 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.
    Final grade: (4 * (0.025 * Tests)) + (0.4 * Exam) + (0.2 * Individual Practical Assignments)+ (0.3 * Practical Team Project) + (OptionalParticipationBonus)

    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.
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