Business Intelligence

Department

Master's Program Industrial Engineering & Management

Course unit code

WIW_MAS_21_3_1

Number of ECTS credits allocated

1.0

Name of lecturer(s)

Mag. Wagner Andreas

Recommended optional program components

none

Recommended or required reading

- The Data Science Handbook - Cady
- Data Science in Practice - Said, Torra
- Data Science - what is it exactly? - Ng Soo
- Big Data, Cloud Computing, and Data Science Engineering - Lee
- Deep Learning Concepts and Architectures - Pedrycz
- Deep Learning Algorithms and Applications - Pedrycz

Level of course unit

Master

Year of study

Fall 2025

Semester when the course unit is delivered

3

Language of instruction

English

Learning outcomes of the course unit

Students …
• … Know the path from collecting raw data to evaluating business metrics and can also apply predictive analytics methods.
• … Know the central goals and tasks of Data Science as well as its typical methods and tools.
• … Can manage data science projects and apply simple analysis methods independently.
• …Know which modern methods are available and how they differ from classical Data Science.
• … Are able to make statements about the past and the future based on existing data
• … Can perform advanced analysis with the help of Matlab.

Course contents

The course 'Business Intelligence' covers the contents from the collection of data to the evaluation of data for the past. After an introduction to the topic and clarification of terms, the first part covers the acquisition of data. In particular, the concepts of 'classic data' and 'big data' are examined in more detail. The second part deals with the processing of the raw data in order to prepare it for further analysis. In the third and final part, this acquired and prepared data is used to calculate simple metrics to measure business performance and present them clearly. The entire course is accompanied by practical examples in Matlab.
The content of the course at a glance:
• Introduction to the field
• Data acquisition
o Data collection
o Data sources: Classic Data, Big Data
• Data set preparation
o Data Masking
o Data Mining
o Classic Data: Data Labelling (categorical vs. numeric), Data cleansing / Data correction, Dealing with missing values, Case specific
o Big Data: Data Labelling (numbers, text, digital images, videos and audio); Data cleansing / Data correction, Dealing with missing values
• Business Intelligence
o Data analysis
o Measurement of business performance
o Extract and present information: Key figures, KPIs, Reports, Dashboards

Planned learning activities and teaching methods

The course comprises an interactive mix of lectures, discussions and individual and group work.

Work placement(s)

none