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

Graduates/students:
Datawarehousing:
* 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

English

number of ECTS credits allocated

6

eLearning quota in percent

15

course-hours-per-week (chw)

4

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

3

name of lecturer(s)

Prof. (FH) DI Dr. Martin Adam

year of study

2

recommended optional program components

not applicable

course unit code

DAT.2

type of course unit

integrated lecture

mode of delivery

Compulsory

work placement(s)

not applicable