Course: Data Mining II

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Course title Data Mining II
Course code USII/PDM2
Organizational form of instruction Lecture + Tutorial
Level of course Master
Year of study 2
Semester Winter
Number of ECTS credits 5
Language of instruction Czech
Status of course Optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Petr Pavel, doc. Ing. Ph.D.
  • Kašparová Miloslava, Ing. Ph.D.
Course content
Introduction to DM Methodology CRISP-DM Data access and data manipulation Modelling Visualisation Cluster analysis, basket market analysis, association rules linear regression logistic regression Decision trees Neural Networks Factor analysis Web Mining Text Mining Use of DM and software tools

Learning activities and teaching methods
Monologic (reading, lecture, briefing), Work with text (with textbook, with book), Methods of individual activities
Learning outcomes
The aim of the course is to acquaint students with possibilities of data mining, contents of individual phases of a data mining project as well as applied methods for a model creation.
Students will understand basic points of applied fields of data mining projects and will be able to operate individual project phases using the CRISP-DM methodology. In addition, they will know the base of the appropriate methods for a model creation and understand their right application in practice.
Prerequisites
unspecified

Assessment methods and criteria
Oral examination, Work-related product analysis, Creative work 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 - 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, M. - LINOFF G. Data Mining Techniques - For Marketing, Sales, and Customer Relationship Management. Indianapolis, John Wiley & Sons, 2004, 643 s.. 2004.
  • GUIDICI P. Applied Data Mining - Statistical Methods for Business and Industry. Guildford, John Wiley & Sons, 2003, 364 s.. 2003.
  • 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.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Economics and Administration Study plan (Version): Informatics in Public Administration (2013) Category: Economy 2 Recommended year of study:2, Recommended semester: Winter
Faculty: Faculty of Economics and Administration Study plan (Version): Insurance Engineering (2014) Category: Economy 2 Recommended year of study:2, Recommended semester: Winter
Faculty: Faculty of Economics and Administration Study plan (Version): Regional and Information Management (2014) Category: Economy 2 Recommended year of study:2, Recommended semester: Winter
Faculty: Faculty of Economics and Administration Study plan (Version): Regional and Information Management (2013) Category: Economy 2 Recommended year of study:2, Recommended semester: Winter
Faculty: Faculty of Economics and Administration Study plan (Version): Insurance Engineering (2014) Category: Economy 2 Recommended year of study:2, Recommended semester: Winter
Faculty: Faculty of Economics and Administration Study plan (Version): Informatics in Public Administration (2014) Category: Economy 2 Recommended year of study:2, Recommended semester: Winter
Faculty: Faculty of Economics and Administration Study plan (Version): Insurance Engineering (2013) Category: Economy 2 Recommended year of study:2, Recommended semester: Winter