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Data Mining and Decision Support Systems – Master Program in Information Systems Type: PI | ECTS: 6 The basic methods of Data Mining will be discussed, i.e. supervised classification, association rules, and unsupervised classification/clustering. A special focus is laid on the practical applicability of the presented methods. After successfully passing the lecture students are able to solve a wide range of real-world Data Science problems. Thereby they are able to choose an appropriate solution method and are able to implement this with the programming language R. At the beginning of each unit, there is a theory block. At the end of each unit, numerical examples will be shown on the blackboard and/or appropriate implementations using the programming language R will be presented. At the end of the term, the progress of group projects will be discussed. Mid-Term exam (20%) Group Project (20%) Presentation of Group Project (20%) Data Science Problem Solution (Final exam, 40%) See the homepage of the Master’s program "Information Systems”. 1 1 Author: Drew Conway and john Myles White Title: Machine Learning for Hackers Publisher: O´Reilly Media Year: 2012 Content relevant for class examination: Yes Content relevant for diploma examination: No Recommendation: Reference literature Type: Book 2 3 Author: Toby Segaran Title: Programming Collective Intelligence Publisher: O´Reilly Media Year: 2007 Content relevant for class examination: Yes Content relevant for diploma examination: No Recommendation: Reference literature Type: Book Author: Anand Rajaraman, Jeffrey David Ullman Publisher: Cambridge University Press Year: 2011 Content relevant for class examination: Yes Content relevant for diploma examination: No Recommendation: Reference literature Type: Book 4 Author: Robert I. Kabacoff Title: R in Action:Data Analysis and Graphics with R. Publisher: Manning Year: 2011 Content relevant for class examination: Yes Content relevant for diploma examination: No Recommendation: Reference literature Type: Book Dipl.-Kfm. Nils Löhndorf, Ph.D.: [email protected] 2
