460-4025/01 – Data Analysis Methods (MAD)

Gurantor departmentDepartment of Computer ScienceCredits5
Subject guarantorprof. RNDr. Václav Snášel, CSc.Subject version guarantorprof. RNDr. Václav Snášel, CSc.
Study levelundergraduate or graduate
Study languageCzech
Year of introduction2010/2011Year of cancellation2014/2015
Intended for the facultiesFEIIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
BUC0014 Ing. Clare Schenk
SNA57 prof. RNDr. Václav Snášel, CSc.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2
Combined Credit and Examination 10+0

Subject aims expressed by acquired skills and competences

Graduate Course gives the following knowledge and skills: basic theoretical background for data analysis, implementation and application of selected methods, practical application to real data, application of selected software packed to data analysis, visualization and analysis results.

Teaching methods

Lectures
Individual consultations
Tutorials
Project work

Summary

Evaluation and interpretation of information obtained from the measured and recorded data from the practice. Methods of data mining, mathematical, statistical and logical methods for solving this class of research and practical problems. Methods of searching for associations, clustering methods, classification methods and some other methods.

Compulsory literature:

D. Skillicorn, Understanding Complex datasets: data mining with matrix decompositions, Chapman & Hall/CRC, 2007. Han Jiawei; Kamber Micheline; Pei Jian, Data Mining, The Morgan Kaufmann Series in Data Management Systems, 3rd edition, 2011.

Recommended literature:

Eldén, L., Matrix Methods in Data Mining and Pattern Recognition, SIAM 2007. T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer; Corr. 3rd edition, 2009.

Way of continuous check of knowledge in the course of semester

Continuous assessment: Treatment of the subject mining of the data. Terms of the credit: Meeting all three points of the follow-up studies, each at least 10 points.

E-learning

Další požadavky na studenta

Additional requirements are placed on the student.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

Lectures: Defining the problem of multivariate data analysis. Methods of data analysis: mathematical statistics and exploratory data analysis. The input data types of formal and semantic aspects. Filtration, missing data, dichotomize, categorization Preprocessing, transformation. Normalization and standardization. Principal components. Cluster analysis, non-hierarchical methods, hierarchical methods, presentation and interpretation of results. Finding associations, automatic creation of hypotheses, presentation and interpretation of results. Decision tree construction, presentation and interpretation. Exercise: Practice methods of lectures on examples of specific data. Papers on new methods of data mining. Reports on the results of an analysis. Projects: Analysis of specific data from their own experience or from a database. Preprocessing, selection of appropriate methods. Own calculations, interpretation. Presentation of results, documentation. Computer Labs: A system for data analysis, control methods, presentation of results, applications.

Conditions for subject completion

Conditions for completion are defined only for particular subject version and form of study

Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2014/2015 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P Czech Ostrava 1 Optional study plan
2014/2015 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K Czech Ostrava 1 Optional study plan
2013/2014 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P Czech Ostrava 1 Optional study plan
2013/2014 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K Czech Ostrava 1 Optional study plan
2012/2013 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P Czech Ostrava 1 Optional study plan
2012/2013 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K Czech Ostrava 1 Optional study plan
2011/2012 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P Czech Ostrava 1 Optional study plan
2011/2012 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K Czech Ostrava 1 Optional study plan
2010/2011 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P Czech Ostrava 1 Optional study plan
2010/2011 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K Czech Ostrava 1 Optional study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner