ERP Systems & Business Process Management PT
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Business Intelligence & Analytics(E)

level of course unit

Master`s course

Learning outcomes of course unit

* Knows classification and demarcation of data warehousing, OLAP, data mining and reporting in the field of business intelligence
* Knows areas of application and potential use of data warehousing, OLAP and data mining
* Can assess areas of application and fields of application
* Can record reporting requests from business
* Can convert Reporting Requirements into Data Models
* Can design and implement data warehouse databases
* Knows different data formats / interface formats
* Knows methods of data conversion
* Can convert data
* Can load a data warehouse
* Knows OLAP - Terms
* Can design and implement OLAP cubes
* Can design access to OLAP cubes

Datamining / Data Science:
* Knows Datamining and Data Science algorithms and techniques
* Knows the data mining / data science process
* Can process the data for data mining
* Can apply data mining methods to problems
* Can present the results from data mining
* Can create simple rules
* Can implement/customize selected algorithms
* Knows BI - Functionalities of ERP Systems
* Knows products and manufacturers of BI solutions (backend, frontend) (Microsoft, Oracle, SAP, Infor, Crystal Report, etc.)

Process Mining:
* Knows goals of Process Mining
* Knows prerequisites for process mining
* Knows benefits, limitations, application areas of Process Mining
* Has an overview of Process Mining Software

prerequisites and co-requisites

not applicable

course contents

* Concept of business intelligence and specific aspects such as datawarehouse, OLAP.
* Methods and techniques of data mining and process mining
* Techniques and up-to-date tools in the field of data warehousing & data mining
* Case studies or projects in the subject area with practical application of tools
* Tools in the field of business intelligence

recommended or required reading

Runkler Th.; Information Mining; vieweg; 2000
Langit L.; Smart Business Intelligence Solutions with Microsoft SQL Server; Microsoft Press; 2008
Petersohn H.; Data Mining; Oldenbourg; 2005
Provost F., Fawcett T.; Data Science for Business; O’Reilly; 2013
Milton M.; Head First Data Analysis; O’Reilly; 2009
van der Aalst W. M.P.; Process Mining – Data Science in Action; Heidelberg; 2016;. 2nd edition

assessment methods and criteria

Written exam

language of instruction


number of ECTS credits allocated


eLearning quota in percent


course-hours-per-week (chw)


planned learning activities and teaching methods

Lecture, individual work with software, group work, presentation and discussion of tasks

semester/trimester when the course unit is delivered


name of lecturer(s)

Dr. Arnim Franzmann

year of study


recommended optional program components

not applicable

course unit code


type of course unit

integrated lecture

mode of delivery


work placement(s)

not applicable