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.
SNA57 prof. RNDr. Václav Snášel, CSc.
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

Ongoing review of learning activities and assignments as part of regular consultations. If the student's assignments also include publishing, the relevant article will be presented in the course.

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 pointsMax. počet pokusů
Examination Examination   3
Mandatory attendence participation:

Show history

Conditions for subject completion and attendance at the exercises within ISP:

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Occurrence in study plans

Academic yearProgrammeBranch/spec.Spec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2023/2024 (P0613D140021) Computational Science P English Ostrava Choice-compulsory type B study plan
2023/2024 (P0613D140021) Computational Science K English Ostrava Choice-compulsory type B study plan
2023/2024 (P0613D140033) Informatics and Computational Science K English Ostrava Choice-compulsory type B study plan
2023/2024 (P0613D140033) Informatics and Computational Science P English Ostrava Choice-compulsory type B study plan
2023/2024 (P0613D140006) Computer Science K English Ostrava Choice-compulsory type B study plan
2023/2024 (P0613D140006) Computer Science P English Ostrava Choice-compulsory type B study plan
2022/2023 (P0613D140006) Computer Science P English Ostrava Choice-compulsory type B study plan
2022/2023 (P0613D140006) Computer Science K English Ostrava Choice-compulsory type B study plan
2022/2023 (P0613D140021) Computational Science P English Ostrava Choice-compulsory type B study plan
2022/2023 (P0613D140021) Computational Science K English Ostrava Choice-compulsory type B study plan
2022/2023 (P0613D140033) Informatics and Computational Science P English Ostrava Choice-compulsory type B study plan
2022/2023 (P0613D140033) Informatics and Computational Science K English Ostrava Choice-compulsory type B study plan
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

Assessment of instruction



2019/2020 Winter