460-6031/02 – Advanced Data Analysis (PAD)

Gurantor departmentDepartment of Computer ScienceCredits10
Subject guarantordoc. Mgr. Miloš Kudělka, Ph.D.Subject version guarantordoc. Mgr. Miloš Kudělka, Ph.D.
Study levelpostgraduateRequirementChoice-compulsory type B
YearSemesterwinter + summer
Study languageEnglish
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFEIIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
KUD007 doc. Mgr. Miloš Kudělka, 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

Subject aims expressed by acquired skills and competences

The aim of the course is to provide students an extended view of data analysis, realization of deeper analyzes and advantage of source data sets with regard to interpretation in selected target area. In addition, this knowledge and skills will be further enhanced in a direction that is in line with the specific focus of his PhD studies and dissertation work.

Teaching methods

Individual consultations
Project work
Other activities


This course provides the students, in its first part, necessary information about basic and advanced algorithms, typical algorithmic problems and their complexity. This part will also contain the introduction of programming techniques and programming and scripting languages. Next, foundations of vector data and network data analysis will be presented including simple algorithms used in both areas. The students will also be familiarized with different tools and libraries suitable for the solution of everyday tasks, primarily focused on biomedical data analysis.

Compulsory literature:

• Levitin, A. (2012). Introduction to the design & analysis of algorithms. Boston: Pearson. • Witten, I. H., Frank, E., Hall, M. A., Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques (Fourth Edition). Morgan Kaufmann Series in Data Management Systems.

Recommended literature:

• Libeskind-Hadas, R., Bush, E. (2014). Computing for biologists: Python programming and principles Cambridge University Press. • Barabási, A. L. (2016). Network science. Cambridge university press.

Way of continuous check of knowledge in the course of semester

Continuous monitoring of study activities and assigned tasks during regular consultations. If some publication activity will be a part of the student's tasks, the relevant article would be presented in the course.


Other requirements

The student prepares and presents the work on a given topic.


Subject has no prerequisities.


Subject has no co-requisities.

Subject syllabus:

• Algorithm. Problem-solving strategies using algorithms. Significant types of solved problems. • Sorting and searching algorithms. • Linear and tree data structures. • Complexity of algorithms and complexity of problems. • Vector data and their algebraic and geometric interpretation. • Clustering algorithms, K-means and Hierarchical Clustering. • Classification algorithms, Naïve Bayes, K-nearest Neighbors. • Network data and their representation. • Algorithms for transformation vector data to network data. • Measuring of network properties, algorithms and interpretation. • Network clustering algorithms.

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
2020/2021 (P0588D140004) Bioinformatics and Computational Biology P English Ostrava Choice-compulsory type B study plan
2020/2021 (P0588D140004) Bioinformatics and Computational Biology K English Ostrava Choice-compulsory type B study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner