M9DM2 Data mining II

Faculty of Science
Autumn 2019
Extent and Intensity
0/2/0. 2 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: k (colloquium).
Teacher(s)
RNDr. Radim Navrátil, Ph.D. (lecturer)
Mgr. Ondřej Pokora, Ph.D. (lecturer)
RNDr. Bc. Iveta Selingerová, Ph.D. (seminar tutor)
Guaranteed by
doc. RNDr. Martin Kolář, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable of Seminar Groups
M9DM2/01: Tue 12:00–13:50 MP1,01014, R. Navrátil, O. Pokora, I. Selingerová
Prerequisites
M8DM1 Data mining I
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.
The capacity limit for the course is 30 student(s).
Current registration and enrolment status: enrolled: 0/30, only registered: 0/30, only registered with preference (fields directly associated with the programme): 0/30
fields of study / plans the course is directly associated with
Course objectives
The course follows the course Data mining I and aims to deepen the already acquired knowledge in this area. Practical use of the methods, interpretation of the results and their application are emphasized.
Learning outcomes
At the end of the course students should be able to:
(1) describe basic methods (exploratory analysis, logistic regression);
(2) describe advanced methods (cluster analysis, discriminant analysis, text mining);
(3) apply these methods on real data;
(4) interpret outcomes of these methods.
Syllabus
  • Data preparation – advanced techniques.
  • SQL and databases.
  • Credit scoring - basic concepts.
  • Text mining.
  • Business Intelligence.
  • Discriminant analysis.
  • Modern data mining methods.
Literature
  • HAN, Jiawei, Micheline KAMBER and Jian PEI. Data mining : concepts and techniques. 3rd ed. Boston: Elsevier, 2012, xxxv, 703. ISBN 9780123814791. info
  • Data mining and statistics for decision making. Edited by Stâephane Tuffery. Hoboken, NJ.: Wiley, 2011, xxiv, 689. ISBN 9780470979167. info
  • MCCUE, Colleen. Data mining and predictive analysis : intelligence gathering and crime analysis. Boston: Butterworth-Heinemann, 2007, xxxi, 332. ISBN 9780750677967. 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
Computer lab seminars: 2 hours a week.
Assessment methods
Colloquium - doing homeworks and presentation of the project are needed to pass.
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 2012, Autumn 2013, Autumn 2014, Autumn 2016, Autumn 2018, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.
  • Enrolment Statistics (Autumn 2019, recent)
  • Permalink: https://is.muni.cz/course/sci/autumn2019/M9DM2