460-6016/04 – Data Analysis (AD)

Gurantor departmentDepartment of Computer ScienceCredits10
Subject guarantorprof. RNDr. Václav Snášel, CSc.Subject version guarantorprof. Ing. Jan Platoš, Ph.D.
Study levelpostgraduateRequirementChoice-compulsory type B
YearSemesterwinter + summer
Study languageEnglish
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFEI, USPIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
PLA06 prof. Ing. Jan Platoš, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 28+0
Part-time Examination 28+0
Distance Examination 10+0

Subject aims expressed by acquired skills and competences

Goals of the course: data analysis

Teaching methods

Individual consultations

Summary

The content of the subject is following: data reduction methods, machine learning, data pre-processing, xxploratory data analysis, statistical data mining approach, cluster analysis (hierarchical and k-means clustering), Bayesian rules, k-nearest neighbor algorithm, decision trees, factor analysis , self-organizing SOM maps, association and fuzzy rules, rough sets, methods of analyzing multi-dimensional data, time series analysis, PCA, ICA, NMF, SVD, tensor data, tensor reduction, model evaluation, visualization, conceptual unions, knowledge mining from databases.

Compulsory literature:

Trevor Hastie, Robert Tibshirani, Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2009. Claudio Carpineto, Giovanni Romano. Concept Data Analysis: Theory and Applications,Wiley, 2004.

Recommended literature:

Bing Liu. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, Springer, 2009. David Skillicorn. Understanding Complex Datasets: Data Mining with Matrix Decompositions, Chapman & Hall, 2007. Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining, Addison Wesley, 2005.

Way of continuous check of knowledge in the course of semester

Samostatná práce ve zvoleném tématu. Ústní zkouška.

E-learning

Other requirements

Additional requirements for the student are not.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

Methods of data reduction, machine learning, data preprocessing, etc.

Conditions for subject completion

Full-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination  
Mandatory attendence parzicipation:

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2021/2022 (P0613D140021) Computational Science P English Ostrava Choice-compulsory type B study plan
2021/2022 (P0613D140021) Computational Science K English Ostrava Choice-compulsory type B study plan
2021/2022 (P0613D140006) Computer Science K English Ostrava Choice-compulsory type B study plan
2021/2022 (P0613D140006) Computer Science P English Ostrava Choice-compulsory type B study plan
2020/2021 (P0613D140006) Computer Science P English Ostrava Choice-compulsory type B study plan
2020/2021 (P0613D140006) Computer Science K English Ostrava Choice-compulsory type B study plan
2020/2021 (P0613D140021) Computational Science K English Ostrava Choice-compulsory type B study plan
2020/2021 (P0613D140021) Computational Science P English Ostrava Choice-compulsory type B study plan
2019/2020 (P0613D140006) Computer Science P English Ostrava Choice-compulsory type B study plan
2019/2020 (P0613D140006) Computer Science K English Ostrava Choice-compulsory type B study plan
2019/2020 (P0613D140021) Computational Science P English Ostrava Choice-compulsory type B study plan
2019/2020 (P0613D140021) Computational Science K English Ostrava Choice-compulsory type B study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner