Ing. Jiří Kříž, Ph.D.

Faculty of Business and Management (FP)

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BASL, J. a R. BLAŽÍČEK. Podnikové informační systémy: podnik v informační společnosti. 3., aktualiz. a dopl. vyd. Praha: Grada. (CS)
POUR, J., M. MARYŠKA a O. NOVOTNÝ. Business intelligence v podnikové praxi. Praha: Professional Publishing, 2012.ISBN 978-80-7431-065-2. (CS)
NISBET, R.., J. F. ELDER a G. MINER. Handbook of statistical analysis and data mining applications. Boston: Academic Press/Elsevier, 2009. ISBN 978-0-12-374765-5. (EN)
LACKO, Ľ.. Databáze: datové sklady, OLAP a dolování dat s příklady v Microsoft SQL Serveru a Oracle. Brno: Computer Press, 2003. ISBN 80-722-6969-0. (CS)
SHMUELI, G., N. R. PATEL a P. C. BRUCE. Data mining for business intelligence: concepts, techniques, and applications in Microsoft Office Excel with XLMiner. Hoboken, N.J.: Wiley, 2010. ISBN 978-0-470-52682-8. (EN)

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Czech

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The aim of the subject is to provide new knowledge from the field of informatics, with a focus on information systems that support complex decision-making tasks in the areas of administration and management, economics, trading and business intelligence, with the help of modern methods and software tools that support the analytical, planning and decision-making activities of companies. Students’ knowledge will be expanded to include the principles governing the architecture of these systems, their design and implementation from the managerial point of view, as well as the essence of system integration. The basic legal framework for the protection of data is presented as part of the taught course.

1. Developmental trends and trends in ICT (basic terms, trends in IS/IT) - (lessons per week 2/0)
2. Principles of business intelligence (essence, main components, approaches to solutions) - (lessons per week 2/0)
3. System integration (principles, development and nature, components and levels, system integrators and their functions - (lessons per week 2/0)
4. Data and their processing (types and formats of data, data collection, data warehouses, fundamentals of data processing, import and export, internal and external data sources) - (lessons per week 2/2)
5. Data, information, knowledge (their representation and hierarchical relationship, interpretation and visualisation) - (lessons per week 2/0)
6. The data mining process (data mining and knowledge from data, their advanced OLAP analysis) - (lessons per week 2/2)
7. Issues concerned with data mining from real data (internal and external data sources, options for solutions) - (lessons per week 2/1)
8. Reporting (static - dynamic, standard - ad-hoc, interpretation and visualization of OLAP analysis) - (lessons per week 2/2)
9. Data mining (theoretical introduction, process diagram of data mining, typical tasks) - (lessons per week 2/0)
10. Algorithms and their principles (classification and regression) - (lessons per week 2/2)
11. Algorithms and their principles (segmentation and sequential) - (lessons per week 2/0)
12. Preparation of data for various software systems and tools, interpretation and evaluation - (lessons per week 2/0)
13. Legal aspects of data protection - (lessons per week 2/0)

The aim of the subject is to provide new knowledge from the field of informatics, with a focus on information systems that support complex decision-making tasks in the areas of administration and management, economics, trading and business intelligence, with the help of modern methods and software tools that support the analytical, planning and decision-making activities of companies.

• Programme RRTES_P Master's

  specialization RRES , 1. year of study, summer semester, 4 credits, compulsory-optional
  

13 hours, optionally

Ing. Jiří Kříž, Ph.D.