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Lecturer(s)
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Ženčák Pavel, RNDr. Ph.D.
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Course content
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1. Introduction to visualization and a brief overview of history 2. Coordinate systems and color scales in data visualization 3. Overview of basic types of graphs and their use 4. Visualization of amounts 5. Visualization of proportions 6. Visualization of distributions 7. Visualization of associations among two quantitative variables, time series and trends 8. Visualization of associations among several quantitative variables, dimension reduction 9. Visualization of geospatil data 10. Visualization of uncertainty 11. Overview of options of selected programs 1. Basic types of graphs (scatter, line, bar, area, etc.) 2. What are the individual types of graphs suitable for? 3. Overview of options offered by selected programs 4. Visualization of 1D data 5. Visualization of 2D data 6. Visualization of multidimensional data 7. More advanced methods of data analysis - dimension reduction (PCA, SVD) 8. More advanced methods of data analysis - cluster search 9. Graphic methods of cluster visualization 10. Approximation and smoothing of data 11. Visualization of categorical data
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Learning activities and teaching methods
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Monologic Lecture(Interpretation, Training), Demonstration
- Attendace
- 39 hours per semester
- Homework for Teaching
- 40 hours per semester
- Preparation for the Course Credit
- 30 hours per semester
- Preparation for the Exam
- 40 hours per semester
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Learning outcomes
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The course introduces students to different ways of data visualization.
Knowledge Get to know the basic ways of data visualization and the possibilities of selected programs.
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Prerequisites
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Basic computer skills.
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Assessment methods and criteria
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Student performance
Colloquium: active participation in the exercise, create a visualization of the selected data, defense in the form of a presentation.
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Recommended literature
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A. C. Telea, Data Visualization. (2014). Principles and Practice, Second Edition. A. K. Peters, Ltd., Natick, MA.
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Alberto Ferrari, Marco Russo. (2016). Introducing Microsoft Power BI. Microsoft Press.
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Claus O. Wilke. (2019). Fundamentals of Data Visualization. O'Reilly Media, Inc.
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Ch. Chen, W. Hrdle, A. Unwin, Ch.Chen, W. Hrdle, A. Unwin. (2008). Handbook of Data Visualization (Springer Handbooks of Computational Statistics). Springer-Verlag TELOS, Santa Clara, CA.
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Chandraish Sinha. (2016). QlikView Essentials. Packt Publishing.
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James D. Miller. (2017). Big Data Visualization. Packt Publishing.
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Joshua N. Milligan. (2015). Learning Tableau. Packt Publishing Ltd.
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Kieran Healy. (2018). Data Visualization: A Practical Introduction. Princeton University Press.
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Kirthi Raman. (2015). Mastering Python Data Visualization. Packt Publishing.
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Nivedita Majumdar , Swapnonil Banerjee. (2012). MATLAB Graphics and Data Visualization Cookbook. Packt Publishing.
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Rob Kabacoff. Data Visualization with R.
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Wendy L. Martinez, Angel R. Martinez, Jeffrey Solka. (2017). Exploratory Data Analysis with MATLAB, 3rd Edition. Chapman and Hall/CRC.
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