Business Intelligence & Analytics(E)
Niveau
Master`s course
Learning outcomes of the courses/module
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
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 for the course
not applicable
Course content
* 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
* 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 specialist literature
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
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
English
Number of ECTS credits awarded
6
Semester hours per week
Planned teaching and learning method
Lecture, individual work with software, group work, presentation and discussion of tasks
Semester/trimester in which the course/module is offered
3