188.429 Business Intelligence
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2022W, VU, 4.0h, 6.0EC


  • Semester hours: 4.0
  • Credits: 6.0
  • Type: VU Lecture and Exercise
  • Format: Online

Learning outcomes

After successful completion of the course, students are able to...

  • apply analytic methods to extract business insights from vast amounts of data
  • systematically tackle business problems and questions with data
  • evaluate Data Warehousing and Big Data Technologies
  • compare and contrast the benefits and limitations of various data wareousing and big data architectures.
  • define solid processes to answer analytical questions
  • identify concrete business goals and data mining goals
  • perform solid analyses using both supervised as well as unsupervised machine learning techniques including the necessary preprocessing steps
  • critically reflect on results obtained and interpret them

Subject of course

  • Business Intelligence reference architecture
  • OLAP (multidimensionality)
  • Logical Modeling (STAR, SNOWFLAKE)
  • ETL Process
  • Closed-Loop Decision Making
  • Big Data technologies
  • Data Lakes
  • Data Mining - Knowledge Discovery in Databases
  • Patterns and taxonomies
  • Predictive and descriptive rules (classification, regression, association, clustering)
  • Business Intelligence applications

In the data warehousing part, students will learn to:

  • Define a data warehouse in terms of the characteristics that differentiate it from other information systems
  • Describe the benefits of data warehousing
  • Describe the structure of a data warehouse
  • List the features of different types of warehouse data
  • Define types of data models in data warehouses
  • Define the dimensional model and its components
  • Formulate OLAP queries
  • Identify types of schema (Star, Snowflake)
  • consider organisational aspects

In the Data Mining part, students will learn about

  • Definition von Data Mining, Data Mining Prozesse: CRISP-DM, ASUM-DM
  • Data Preprocessing
  • Unsupervised Techniques for Data Analysis, Clustering
  • Supervised Tech´niques for Data Analyses: Classification
  • Evaluation of data analysis processes and models

Teaching methods

  • Lectures:presence-teaching if possible according to regulations in place in winter term
  • flipped Classroom
  • Assignments to be elaborated in small groups

Mode of examination

Written and oral

Additional information

  • All required information will be presented in the preliminary lecture (Vorbesprechung)
  • All teaching materials will be available on TUWEL.


Preliminary Lecture (Vorbesprechung) 

  • The first session (attendance strongly recommended) will cover organization and modalities



18h   Lecture
90h   Exercises
38h   Reading and self-study
4h     Presence for oral and written assignments

150 Hours (= 6 ECTS)



Course dates

Thu12:00 - 14:0006.10.2022Hörsaal 6 - RPL Introduction
Thu12:00 - 14:0027.10.2022 - 03.11.2022GM 5 Praktikum HS- TCH Lecture
Thu12:00 - 14:0017.11.2022GM 5 Praktikum HS- TCH Lecture
Thu12:00 - 14:0024.11.2022 - 01.12.2022GM 5 Praktikum HS- TCH Lecture
Thu12:00 - 14:0022.12.2022 - 26.01.2023GM 5 Praktikum HS- TCH Lecture
Business Intelligence - Single appointments
Thu06.10.202212:00 - 14:00Hörsaal 6 - RPL Introduction
Thu27.10.202212:00 - 14:00GM 5 Praktikum HS- TCH Lecture
Thu03.11.202212:00 - 14:00GM 5 Praktikum HS- TCH Lecture
Thu17.11.202212:00 - 14:00GM 5 Praktikum HS- TCH Lecture
Thu24.11.202212:00 - 14:00GM 5 Praktikum HS- TCH Lecture
Thu01.12.202212:00 - 14:00GM 5 Praktikum HS- TCH Lecture
Thu22.12.202212:00 - 14:00GM 5 Praktikum HS- TCH Lecture
Thu12.01.202312:00 - 14:00GM 5 Praktikum HS- TCH Lecture
Thu19.01.202312:00 - 14:00GM 5 Praktikum HS- TCH Lecture
Thu26.01.202312:00 - 14:00GM 5 Praktikum HS- TCH Lecture

Examination modalities

  • written assignments and oral reviews of these assigments.
  • written assigment/exam (closed book) - according to current planning in presence. If the pandemic situation will not allow exams in presence at the scheduled time, we will switch to an on-line assessment via TUWEL. In case of low enrollment, this assigment can also be conducted orally (also depending on the development of the pademic situation.

Course registration

Begin End Deregistration end
26.09.2022 00:00 07.10.2022 23:55 07.10.2022 23:55

Registration modalities

Acceptance to the course will be by the lecturers. Priority is given to

  1. Students that have this course as a compulsory course (e.g. foundations, core modules) in their curriculum.
  2. Students that have this course as an elective course.
  3. ERASMUS students that have "Business Intelligence" in their learning agreement.
  4. Students that are currently in a bachelor programme of any of the studies mentioned in 1.), and are finishing their studies in the current semester.
  5. If there are still free places afterwards, they will be assigned according to the ranking on the waiting list


Study CodeObligationSemesterPrecon.Info
066 645 Data Science Not specified
066 926 Business Informatics Mandatory
066 933 Information & Knowledge Management Mandatory
066 936 Medical Informatics Mandatory elective
066 937 Software Engineering & Internet Computing Mandatory elective


No lecture notes are available.

Previous knowledge

Students should have a solid grasp on:

  1. Conceptual database design
  2. Relational database model
  3. Normalization
  4. DBMSs
  5. SQL
  6. Statistics

There will be an opportunity to recap that knowledge at the beginning of the exercises.

Accompanying courses

Continuative courses