Course: Data Mining

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Course title Data Mining
Course code USII/PZDM
Organizational form of instruction Lecture + Lesson
Level of course Master
Year of study 1
Semester Winter
Number of ECTS credits 4
Language of instruction Czech
Status of course Compulsory
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
  • Petr Pavel, doc. Ing. Ph.D.
  • Jirava Pavel, Ing. Ph.D.
  • Kašparová Miloslava, Ing. Ph.D.
Course content
Introduction to data mining. Methodologies of data mining. Phases and tasks of the CRISP methodology. Data understanding. Data preparation for modelling. Essentials of a model creation. Model designing.

Learning activities and teaching methods
Monologic (reading, lecture, briefing), Methods of individual activities, Monitoring, Demonstration
Learning outcomes
The aim of the course is to acquaint students with possibilities of data mining. The introductory part of the course is followed by presentation of definitions of aims and techniques for data mining. Further, selection of data sources and their preparation for modelling are explained.
Students will be able to define individual phases of a data mining project and its content. Using software tools they will know how to solve basic tasks in the area of data preparation and choose the appropriate methods for a model creation.
Bases work with database, knowledge from mathematics to the extent of subject 1. and 2. class.

Assessment methods and criteria
Oral examination, Written examination, Home assignment evaluation, Work-related product analysis

The assignment is granted upon elaboration of given tasks at seminars (minimum achievement of 60 percent is required) and submitting the seminar paper. Assessment methods: oral, written. The oral examination is based on defence of the seminar paper. The final assessment is comprised of the following proportions: work at seminars - 40 percent, defence of the seminar paper and responding to examiner questions - 60 percent; the written examination might also be considered.
Recommended literature
  • Berka, Petr. Dobývání znalostí z databází. Praha: Academia, 2003. ISBN 80-200-1062-9.
  • Berry, Michael J. A. Data mining techniques : for marketing, sales, and customer relationship management. Indianapolis: Wiley, 2004. ISBN 0-471-47064-3.
  • Berry, Michael J. A. Mastering data mining. New York: John Wiley & Sons, 2000. ISBN 0-471-33123-6.
  • Petr, Pavel. Data Mining.. Pardubice: Univerzita Pardubice, 2006. ISBN 80-7194-886-1.
  • Petr, Pavel. Metody Data Miningu.. Pardubice: Univerzita Pardubice, 2014. ISBN 978-80-7395-872-5.
  • Petr, Pavel. Metody Data Miningu.. Pardubice: Univerzita Pardubice, 2015. ISBN 978-80-7395-873-2.
  • PYLE, D. Data Preparation for Data Mining. San Diego, Academic Press, 1999, 540 s.. San Diego, 1999.
  • RUD, O. L. Data Mining - Praktický průvodce dolováním dat pro efektivní prodej, cílený marketing a podporu zákazníků (CRM). Praha, Computer Press, 2001, 330 s.. 2001.

Study plans that include the course