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Data Mining for Business Intelligence

Data Mining for Business Intelligence
Typ: Seminar (S)
Semester: WS 13/14
Dozent: Bagher Mirashrafi
SWS: 2
LVNr.: <a target="lvn" href="https://studium.kit.edu/meineuniversitaet/Seiten/vorlesungsverzeichnis.aspx?page=event.asp&objgguid=NEW&gguid=0x3833f83209709b4e8eb6cd4ab08a2ef3">2521030</a>
Hinweis:

Topics of this seminar are:

1-Data mining for business applications

2-Decision support systems and business intelligence

3-Multiple criteria decision making in data mining

4-Dynamic data mining and business applications

5-Customer relationship management decisions by data mining

In this seminar have been found: Accessible through the careful definition and introduction of each concept, followed by the extensive use of examples and numerous real-life case studies. To utilize mathematical models and analysis models to make effective and good quality business decisions. A practical guide to the mathematical models and analysis methodologies of business intelligence.

Bemerkungen:

Anmeldung :

To register for this seminar by e-mail is required. Interested persons please contact the seminar leader ( mirashrafi@statistik.uni-karlsruhe.de ).

Anmeldung: 1.10.2013

Anmeldungsschluss: 01.11.2013

Für weitere Informationen : http://statistik.econ.kit.edu/

Beschreibung:

Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. The term implies having a comprehensive knowledge of all factors that affect a business, such as customers, competitors, business partners, economic environment, and internal operations, therefore enabling optimal decisions to be made.

Literaturhinweise (en):

1. Han, J., Kamber, M., & Pei, J. (2011). Data mining: concepts and techniques, 3rd ed . Morgan Kaufmann.

2. Shmueli, G., Patel, N. R., & Bruce, P. C. (2008). Data mining for business intelligence: Concepts, techniques, and applications in Microsoft Office Excel with XLMiner. Wiley. com.

3. Turban, E., Aronson, J., & Liang, T. P. (2005). Decision Support Systems and Intelligent Systems 7”‘Edition. Pearson Prentice Hall.

4. Vercellis C. (2009). Business Intelligence: Data Mining and Optimization for Decision Making. John Wiley & Sons Ltd.

5. Berry, M. J., & Linoff, G. S. (2004). Data mining techniques: for marketing, sales, and customer relationship management. Wiley. com.

Kommentar:

Topics of this seminar are:

1- Data mining for business applications

2- Decision support systems and business intelligence

3- Multiple criteria decision making in data mining

4- Dynamic data mining and business applications

5- Customer relationship management decisions by data mining

In this seminar have been found: Accessible through the careful definition and introduction of each concept, followed by the extensive use of examples and numerous real-life case studies. To utilize mathematical models and analysis models to make effective and good quality business decisions. A practical guide to the mathematical models and analysis methodologies of business intelligence.