M8DM1 Data mining I

Faculty of Science
spring 2012 - acreditation

The information about the term spring 2012 - acreditation is not made public

Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
Mgr. Martin Řezáč, Ph.D. (lecturer)
Guaranteed by
Mgr. Martin Řezáč, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
Data mining is a proven way how to get best knowledge from data for decision making. The course is an introduction to data mining issues, definitions of basic concepts, an introduction and practice of the methods and techniques that are used in practice. Students will gain a basic knowledge of these methods, which they will deepen in the computer exercises.
Syllabus
  • History of data mining, basic concepts, software.
  • Data organization.
  • Data preparation.
  • Exploratory analysis, visualization, kontingency tables.
  • Logistic regression I.
  • Decision trees I.
  • Neural networks I.
  • Discriminatory analysis.
  • Segmentation, cluster analysis.
  • Evaluation of given model – LC (ROC), Gini, KS, Lift.
Literature
  • GIUDICI, Paolo. Applied data mining : statistical methods for business and industry. Chichester: Wiley, 2003, xii, 364. ISBN 0470846798. info
  • THOMAS, L. C., David B. EDELMAN and Jonathan N. CROOK. Credit scoring and its applications. Philadelphia, Pa.: Society for Industrial and Applied Mathematics, 2002, xiv, 248. ISBN 0898714834. URL info
  • SIDDIQI, Naeem. Credit risk scorecards : developing and implementing intelligent credit scoring. Hoboken, N.J.: Wiley, 2006, xi, 196. ISBN 047175451X. info
  • THOMAS, L. C. Consumer credit models : pricing, profit, and portfolios. 1st pub. Oxford: Oxford University Press, 2009, xii, 385. ISBN 9780199232130. info
Teaching methods
Lectures and exercises.
Assessment methods
project, oral exam
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.
Listed among pre-requisites of other courses
The course is also listed under the following terms Spring 2011 - only for the accreditation, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.