Department of Information and Computing Sciences

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

Onderwijs Informatica en Informatiekunde

Vak-informatie Informatica en Informatiekunde

Business intelligence

Te lang geleden voor docent- en roosterinformatie
Website:website containing additional information
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:The lectures will be centered around the well-known book which you need to have in your possession before lecture 1: A newer edition is fine as well.
AD ALERT: One option is to buy the book from A-Eskwadraat.

This course book has the following structure:

  1. Introduction to Business Intelligence
  2. Data Warehousing
  3. Business Performance Management
  4. Data Mining for Business Intelligence
  5. Text and Web Mining
  6. Business Intelligence Implementation: Integration and Emerging Trends
Also, a number of scientific papers will be part of the study materials.
These are listed on the Course website's Materials Download page (only accessible by course students).
Course form:In general, the course is structured around the principle of 6 contact hours a week:
  • Tue 13:15-15.00 - Lectures, either by Marco, Floris, or a guest.
  • Thu 09:00-10.45 - Lectures, either by Marco, Floris, or a guest.
  • Thu 11:00-12.45 - Workshops where you will work on your BI team project.
    • Also, project milestone team presentations will occasionally be held.
Also, we will have a crash course BI using Jedox in weeks 3 and 4, and present the results in week 5!.
Exam form:Balancing out theory, practice and participation on BI is also reflected in the course grading balance:
  • Exam: 1 written open exam, as well as 5 multiple-choice mini-exams (one for each chapter), with one second-chance exam opportunity in August.
  • Project: 1 BI team project grade, consisting of the final project deliverables: working prototype, oral presentation and written report.
  • Assignments: the Jedox DWH presentation and the Meta-algorithmics report.
  • Talks: Most notably, scientific paper analysis talk, but also including project milestone and other presentations.
  • Participation: Your fair grading of other students' work.
Final grade: (5 * (0.04 * McExam)) + (0.3 * OpenExam) + (0.25 * Project) + (0.1 * Assignments) + (0.15 * Talks+Participation)

IMPORTANT: In order to pass this course, you need to have scored at least a 4.0 for each course component.

Minimum effort to qualify for 2nd chance exam:In order to qualify for 2nd chance exam, you need to have scored at least a 4.0 for each course component specified in Exam form.
Description:Please refer to the MBIN course website for more info.
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