FF:PAM145 Data visualization - Course Information
PAM145 Data analysis and visualization in educationFaculty of Arts
- Extent and Intensity
- 1/1/0. 4 credit(s). Type of Completion: z (credit).
Taught in person.
- Mgr. Bc. Libor Juhaňák, Ph.D. (lecturer)
doc. Mgr. Martin Sedláček, Ph.D. (lecturer)
- Guaranteed by
- doc. Mgr. Martin Sedláček, Ph.D.
Department of Educational Sciences - Faculty of Arts
Contact Person: Mgr. Kateřina Zelená
Supplier department: Department of Educational Sciences - Faculty of Arts
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 15 student(s).
Current registration and enrolment status: enrolled: 0/15, only registered: 0/15, only registered with preference (fields directly associated with the programme): 0/15
- fields of study / plans the course is directly associated with
- Course objectives
- The aim of the course is to acquire skills related to exploratory data analysis and data visualization. During this course, students will gain a basic insight into different kinds of data analysis. Students will be able to analyze different types of data using appropriate analytical methods and they will be able to describe analyzed data and their structure. Using appropriate visualisation techniques, students will be able to present analyzed data in a clear and comprehensible manner.
- Learning outcomes
- Upon completion of the course students will be able to:
- Realize an exploratory data analysis and write a data analysis report.
- Use several analytical software tools to perform an appropriate data analysis.
- Use different kinds of visualisation techniques to create effective visualizations of data.
- The course is divided into three main thematic parts:
- 1) An introduction to Exploratory Data Analysis (EDA).
- 2) The issue of visual representation and visualization of different types of data.
- 3) Selected analytical methods for specific types of data.
- Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer International Publishing. ISBN 978-3-319-24275-0.
- Wickham, H., Grolemund, G. (2016). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O'Reilly Media. ISBN 978-1-491-91039-9.
- Tukey, J. W. (1977). Exploratory Data Analysis. Addison-Wesley. ISBN 0-201-07616-0.
- Teaching methods
- Direct instruction, workshop, independent study, project and collaborative methods.
- Assessment methods
- Students deal with practical assignments in an online support of the course. They need to gain required number of points in the course.
- Language of instruction
- Further comments (probably available only in Czech)
- The course is taught annually.
The course is taught: every week.