Úvod do vizualizácie dát
- Credits: 3
- Ending: Examination
- Range: 2C
- Semester: winter
- Year: 1
- Faculty of Economics and Finance
Teachers
Included in study programs
Teaching results
Knowledge - Graduates of the course will learn to modify data into a form suitable for exploratory data analysis and visualisation. They will learn the basic techniques of graphical data representation and learn to use the principles of data presentation. At the same time, the most common errors associated with the visual presentation of data are identified.
Skills - By completing the course, the student will improve analytical and presentation skills through data visualization.
Competences - Graduates of the course will be able to process data into a suitable for analysis and identify a suitable visualization technique for convincing presentation of trends, structures or. extreme values in data. These competencies will serve for better decision-making at the level of companies as well as in public administration.
Indicative content
The course is focused on practical skills in the field of data analysis through their visualization and effective communication of results to the public. The course is organized in three parts. In the first part, students will learn to modify data into a form suitable for analysis (eg data organization, data cleaning, data pivoting). In the second part, they will learn the principles and acquire practical skills of visualizing data distribution (data distribution through histogram, box plot graph, maps) and relationships between data (eg scatter plot, scatter plot matrix, bubble chart, parallel coordinate plot). All skills will be explained on practical examples in specialized software. In the last part, students will learn the principles of graphic presentation and how to avoid the common mistakes.
1. Preparation of data for visualization. Data formats. Data types.
2. Getting acquainted with the Geoda environment. Exploratory data analysis
3. Entering data into Geoda. Data transformation in Geoda
4. Techniques of data distribution visualization (histogram, boxplot)
5. Data distribution visualization techniques (map, cartogram)
6. Techniques of visualization of relationships between data (scatterplot, scatterplot matrix)
7. Techniques of visualization of relationships between data (buble chart, conditional plot, parallel coordinate plot)
8. Techniques of time series visualization (averages chart)
9. The most common visualization errors
10. Interactive visualizations in Flourish
11. Presentation of student projects
12. Presentation of student projects
Support literature
1. Kirk, A. (2016). Data Visualisation. A Handbook for Data Driven Design. Sage, Los Angeles. ISBN 978-1-4739-1213-7
2. Anselin, L. et al. (2020). GeoDa Workbook. University of Chicago. Dostupná on line https://geodacenter.github.io/documentation.html
Requirements to complete the course
individual work, mid term tests
written / combined exam
40 % quality of the assignments
60 % quality of the final project
Student workload
Student workload 78 hours (participation in seminars 26 h, preparation for assignments 13 h, elaboration of final project 39 h)
Date of approval: 11.03.2024
Date of the latest change: 21.12.2021