M9DM2 Data mining II

Faculty of Science
Autumn 2012
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
doc. RNDr. Martin Kolář, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: Mgr. Martin Řezáč, Ph.D.
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Wed 13:00–14:50 MP2,01014a
  • Timetable of Seminar Groups:
M9DM2/01: Thu 10:00–11:50 MP2,01014a
Prerequisites (in Czech)
M8DM1 Data mining I
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 an analytical methodology for obtaining non-trivial hidden and potentially useful information from data. The course follows the course Data mining I and aims to deepen the already acquired knowledge in this area. In addition, students will be familiarized with the methodology of development of scoring functions and related financial indicators. Students will gain a basic knowledge of these methods, which they will deepen in the computer exercises using SAS system.
Syllabus
  • Credit scoring - basic concepts
  • Introduction to SAS EG/ SAS EM
  • Development methodology of scoring functions
  • Data preparation – advanced techniques
  • Cluster analysis
  • Cox regression
  • Evaluation of model II
  • Cut-off sutting, RAROA, CRE
  • Monitoring
Literature
  • THOMAS, L. C. Consumer credit models : pricing, profit, and portfolios. 1st pub. Oxford: Oxford University Press, 2009, xii, 385. ISBN 9780199232130. info
  • ANDERSON, Raymond. The credit scoring toolkit : theory and practice for retail credit risk management and decision automation. 1st pub. Oxford: Oxford University Press, 2007, lvi, 731. ISBN 9780199226405. 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., 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
Teaching methods
Lectures and exercises.
Assessment methods
project, oral exam.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2011, Autumn 2013, Autumn 2014, Autumn 2016, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.
  • Enrolment Statistics (Autumn 2012, recent)
  • Permalink: https://is.muni.cz/course/sci/autumn2012/M9DM2