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Lecturer(s)
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Milota Josef, RNDr.
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Cepák Milan, Ing. Ph.D.
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Course content
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Lectures: 1. The concept of data mining; 2. When to use data mining, and when not to; 3. Typical applications of data mining in practice; 4. Introduction to data warehouses; 5. Data mining project and its phases; 6. Typical problems with the data and their possible solutions; 7. Selected methods and algorithms of data mining and statistics; 8. Data mining software; 9. Case study; 10. Overview of the main headings of the mining methods of the web site; 11. Overview of practical applications based on the mining of web site; 12. Overview of practical applications, data warehousing and data mining - an example of the use of Weka;
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Demonstration, E-learning
- Class attendance
- 18 hours per semester
- Preparation for classes
- 55 hours per semester
- Preparation for credit
- 40 hours per semester
- Preparation for exam
- 55 hours per semester
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Learning outcomes
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The aim of the subject is to acquaint students with knowledge of different types of enterprise data resources, to explain useful knowledge types and the steps of the process of acquiring knowledge from data stored in enterprise systems and beyond; to acquaint students with the tools and techniques used and give an overview of the problem, which is becoming increasingly used tool for analysis and decision support at various levels of organization management. This area is known as business intelligence (BI).
Students learn the skills needed to use tools for data mining. Students will know the theoretical foundations of data mining, but also know how to apply them in practice.
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Prerequisites
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Prerequisities: KMI/DBS1 Database systems, KMI/TPS2 or KMI/TPS2A Theory of Probability and Statistics 2
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Assessment methods and criteria
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Combined exam, Seminar work
Requirements on students: Active participation in seminars, elaboration of assignment with the theme of data mining, data warehousing or mining web site with the help of selected tools (e.g. R, etc.).
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Recommended literature
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Anděl, J. Statistické metody 3. vyd., Praha, Marfyzpress.2003.ISBN 80-86732-08-8.
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Berka, P. Dobývání znalostí z databází.. Praha: Academia, 2003. ISBN 80-200-1062-9.
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Hendl, J. Přehled statistických metod zpracování dat. Praha, Portál, 2016. ISBN 80-7367-123-9.
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Humphries, M., Hawkins, W.,M., Dy. M.C. Data warehousing Návrh a implementace. Computer Press, 2002. ISBN 8072265601.. Computer Press, 2002. ISBN 8072265601.
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LACKO, M. Databáze: datové sklady, OLAP a dolování dat. Computer Press, 2003. ISBN 80-7226-969-0.. Computer Press, 2003. ISBN 80-7226-969-0.
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Venables, W., N., Ripley, B.D. Modern Applied Statistics with S. New York : 4th ed, 2002. ISBN 0-387-95457-0.
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Weka 3. Data Mining Software in Java [online].. 1998.
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