# PřF:M8DM1 Data mining I - Course Information

## M8DM1 Data mining I

**Faculty of Science**

Spring 2016

**Extent and Intensity**- 2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
**Teacher(s)**- RNDr. Radim Navrátil, Ph.D. (lecturer)

Ing. Mgr. Jakub Buček (seminar tutor) **Guaranteed by**- doc. PaedDr. RNDr. Stanislav Katina, Ph.D.

Department of Mathematics and Statistics – Departments – Faculty of Science

Contact Person: RNDr. Radim Navrátil, Ph.D.

Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science **Timetable**- Mon 8:00–9:50 M1,01017
- Timetable of Seminar Groups:

*J. Buček*

M8DM1/02: Tue 16:00–17:50 MP1,01014,*R. Navrátil*

M8DM1/03: Mon 16:00–17:50 MP1,01014,*J. Buček* **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**- Financial and Insurance Mathematics (programme PřF, B-MA)
- Finance Mathematics (programme PřF, N-MA)

**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. On computer exercises they will learn to work with statistical software SAS and apply presented methods on real data.
**Syllabus**- History of data mining, basic concepts, software.
- Data organization.
- Data preparation.
- Exploratory analysis, visualization, contingency tables.
- Dimension reduction - principal components, factor analysis, multidimensional scaling
- Market basket analysis.
- Cluster analysis.
- Linear regression, assumptions violation, robustification.
- Logistic regression.Model evaluation– LC (ROC), Gini, KS, Lift.
- Decision trees.
- Discriminatory analysis.

**Literature**- GIUDICI, Paolo.
*Applied data mining : statistical methods for business and industry*. Chichester: Wiley, 2003, xii, 364. ISBN 0470846798. info - HAN, Jiawei and Micheline KAMBER.
*Data mining : concepts and techniques*. 2nd ed. San Francisco, CA: Morgan Kaufmann, 2006, xxviii, 77. ISBN 1558609016. URL info - HAND, D. J., Heikki MANNILA and Padhraic SMYTH.
*Principles of data mining*. Cambridge, Mass.: MIT Press, 2001, xxxii, 546. ISBN 026208290X. info *Business modeling and data mining*. Edited by Dorian Pyle. Boston: Morgan Kaufmann Publishers, 2003, xxvi, 693. ISBN 155860653X. info*Data mining and knowledge discovery handbook*. Edited by Oded Z. Maimon - Lior Rokach. New York: Springer, 2005, xxxv, 1383. ISBN 0387244352. info

- GIUDICI, Paolo.
**Teaching methods**- Lectures - gaing knowledge of data mining techniques. Exercises - practice of data mining techniques with the aid of statistical software SAS.
**Assessment methods**- Computer test on exercises - 50% points is needed to pass. Oral exam - 50% of correct answers and correctly solved project are needed to pass.
**Language of instruction**- Czech
**Further Comments**- Study Materials

The course is taught annually. **Listed among pre-requisites of other courses**

- Enrolment Statistics (Spring 2016, recent)
- Permalink: https://is.muni.cz/course/sci/spring2016/M8DM1