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Lecturer(s)
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Nedbal Jiří, doc. Ing. Ph.D.
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Murgaš Jiří, Ing. Ph.D.
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Course content
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The lectures will focus on possible ways to evaluate industrial processes. A data related to environmental aspects will be also processed and evaluated in the exercise course. Students will thus acquire practical knowledge of working with data in the environmental field as well. 1. Introduction, conditions of graduation, what are enterprise information systems. 2. Business Intelligence. The essence of Business Intelligence, components and tools for creating solutions. 3. Acquisition and collection of production data. Principles and recording of documentation and production data in an industrial enterprise. 4. Analysis and evaluation of production data. Evaluation and conclusions from documentation and production data in an industrial enterprise. 5. GDPR in production data. Data sharing and publishing in the industrial enterprise. 6. Presentation and visualisation of data. Tools for visualisation, reporting and communication of information. 7. Visualisation of production outputs. Working with presented data and examples from practice. 8. Industrial process analysis steps. Basic criteria and metrics in industrial process analysis. 9. Industrial process efficiency and production efficiency in relation to sustainability. Basic ways in which industrial process and production efficiency can be assessed (KPIs, production efficiency, quality, waste, etc.), efficiency and energy efficiency. 10. Lecture by a practitioner. 11. Trends in Industry 4.0 and data. 12. Examples and demonstrations of Industry 4.0 trends. 13. Discussion, consultation.
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Learning activities and teaching methods
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Lecture with practical applications, E-learning, One-to-One tutorial, Seminar classes, Individual study
- unspecified
- 26 hours per semester
- Individual project (40)
- 23 hours per semester
- Preparation for an examination (30-60)
- 26 hours per semester
- Contact hours
- 52 hours per semester
- Presentation preparation (report) (1-10)
- 3 hours per semester
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| prerequisite |
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| Knowledge |
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| The subject does not require any special prerequisite knowledge. |
| Skills |
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| Ability to present independent work. |
| Ability to work in a team. |
| Ability to understand data. |
| Competences |
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| N/A |
| N/A |
| N/A |
| N/A |
| N/A |
| N/A |
| learning outcomes |
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| Knowledge |
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| gain an understanding of the Power BI environment and features for data analysis and visualization |
| ability to import, transform and prepare different types of data for analysis in Power BI |
| use basic Power BI functions using the DAX language |
| understand basic statistical principles and their application in business data |
| Skills |
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| effective work with data in Power BI during import, transformation and visualization |
| creating interactive visualisations in Power BI |
| Competences |
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| N/A |
| N/A |
| N/A |
| teaching methods |
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| Knowledge |
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| E-learning |
| Lecture |
| Lecture supplemented with a discussion |
| Practicum |
| Multimedia supported teaching |
| Self-study of literature |
| Students' portfolio |
| Skills |
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| Lecture |
| Lecture supplemented with a discussion |
| Practicum |
| Students' portfolio |
| Competences |
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| Lecture |
| Lecture supplemented with a discussion |
| Practicum |
| Students' portfolio |
| assessment methods |
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| Knowledge |
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| Written exam |
| Test |
| Seminar work |
| Skills |
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| Written exam |
| Test |
| Seminar work |
| Competences |
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| Written exam |
| Test |
| Seminar work |
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Recommended literature
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Deckler, Greg. Learn power BI: a comprehensive, step-by-step guide for beginners to learn real-world business intelligence. Nakladatelství Packt, 2022. ISBN 978-1801811958.
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Chmelár, Michal. Reporting v Power BI, PowerPivot a jazyk Dax. Smart People, 2018. ISBN 9788097307806.
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Mičudová, Kateřina; Gangur, Mikuláš; Svoboda, Milan; Říhová, Pavla. Základy statistiky a pravděpodobnosti. Západočeská univerzita v Plzni, 2016. ISBN 978-80-2610-660-9.
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Pour, Jan; Maryčka, Miloš; Stanovská, Iva; Šedivá, Zuzana. Self Service Business Intelligence: Jak si vytvořit vlastní analytické, plánovací a reportingové aplikace. Grada Publishing, 2018. ISBN 978-80-271-0616-5.
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Svoboda, Milan; Gangur, Mikuláš; Mičudová, Kateřina. Statistické zpracování dat. Západočeská univerzita v Plzni. 2019.
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