Course Unit Code | 155-1327/01 |
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Number of ECTS Credits Allocated | 4 ECTS credits |
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Type of Course Unit * | Compulsory |
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Level of Course Unit * | First Cycle |
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Year of Study * | Third Year |
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Semester when the Course Unit is delivered | Winter Semester |
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Mode of Delivery | Face-to-face |
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Language of Instruction | Czech |
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Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
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Name of Lecturer(s) | Personal ID | Name |
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| NOV21 | Ing. Vítězslav Novák, Ph.D. |
Summary |
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The aim of the course is to familiarize students with the possibilities of Microsoft Excel spreadsheet for data analysis. The show will be applicable to the basic fuctions and advanced tools such as structured tables, pivot tables, Power Query tool for data transformations and Power Pivot add-in for managing data models. The Power BI tool for data visualization and report creation will also be presented. |
Learning Outcomes of the Course Unit |
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The aim of the course is to familiarize students with the possibilities of Microsoft Excel spreadsheet for data analysis. The show will be applicable to the basic fuctions and advanced tools such as structured tables, pivot tables, Power Query tool for data transformations and Power Pivot add-in for managing data models. The Power BI tool for data visualization and report creation will also be presented. |
Course Contents |
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1) Excel – repeating the necessary basics
2) Excel – work with lists
3) Excel – data analysis using formulas
4) Excel – data analysis using structured tables
5) Excel – data analysis using pivot tables and charts
6) Excel – data analysis using the Power Pivot add-on and the DAX language
7) Excel – data import and transformation using Power Query
8) Power BI – the philosophy of the tool
9) Power BI – creating reports in Power BI Desktop
10) Power BI – DAX language in Power BI
11) Power BI - relationships
12) Power BI – Power Query in Power BI |
Recommended or Required Reading |
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Required Reading: |
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WINSTON, Wayne. Microsoft Excel Data Analysis and Business Modeling (Office 2021 and Microsoft 365). San Francisco, Microsoft Press, 2021. ISBN 978-0137613663.
ALEXANDER, Michael and KUSLEIKA, Dick. Microsoft Excel 365 Bible. Indianapolis, John Wiley & Sons, 2022. ISBN 978-1119835103.
DECKLER, Greg and POWELL, Brett. Mastering Microsoft Power BI - Second Edition: Expert techniques to create interactive insights for effective data analytics and business intelligence. Birmingham, Packt Publishing, 2022. ISBN 978-1801811484. |
NOVÁK, Vítězslav. Analýza dat v Microsoft Excelu. Ostrava: VŠB - Technická univerzita Ostrava, 2023. ISBN 978-80-248-4695-8.
LASÁK, Pavel. Praktické použití funkcí v Excelu. Praha: Grada Publishing, 2021. ISBN 978-80-271-1303-3.
LAURENČÍK, Marek. Excel 2019: práce s databázemi a kontingenčními tabulkami. Průvodce (Grada). Praha: Grada, 2020. ISBN 978-80-271-1391-0. |
Recommended Reading: |
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FERRARI, Alberto and Marco RUSSO. Analyzing Data with Microsoft Power BI and Power Pivot for Excel. Redmond, Washington: Microsoft Press, 2017. ISBN 978-1-5093-0276-5.
RUSSO, Marco and Alberto FERRARI. The Definitive Guide to DAX: Business Intelligence for Microsoft Power BI, SQL Server Analysis Services, and Excel Second Edition. Redmond, Washington: Microsoft Press, 2019. ISBN 978-1509306978.
DECKLER, Greg. Learn Power BI - Second Edition: A comprehensive, step-by-step guide for beginners to learn real-world business intelligence. Birmingham, Packt Publishing, 2022. ISBN 978-1801811958. |
KLATOVSKÝ, Karel. Microsoft Excel 2021/365 nejen pro školy. Prostějov: Computer Media, 2023. ISBN 978-80-7402-451-1.
FERRARI, Alberto and Marco RUSSO. Analyzing Data with Microsoft Power BI and Power Pivot for Excel. Redmond, Washington: Microsoft Press, 2017. ISBN 978-1-5093-0276-5.
RUSSO, Marco and Alberto FERRARI. The Definitive Guide to DAX: Business Intelligence for Microsoft Power BI, SQL Server Analysis Services, and Excel Second Edition. Redmond, Washington: Microsoft Press, 2019. ISBN 978-1509306978. |
Planned learning activities and teaching methods |
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Lectures, Experimental work in labs, Project work |
Assesment methods and criteria |
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Task Title | Task Type | Maximum Number of Points (Act. for Subtasks) | Minimum Number of Points for Task Passing |
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Graded credit | Graded credit | 100 | 51 |