- Class 30
- Practice 30
- Independent work 60
Visualization software tools
Lecturers and associates
The objectives of this module are to enable students to:
• choose a tool for visualization and exploratory analysis, and avoid the most common visualization mistakes.
• critically interpret techniques of analytical interaction and navigation, types of visualization and analytical patterns.
• effectively prepare data for visual analysis and create an interactive dashboard.
The module introduces students to the techniques of visualization and exploratory data analysis. It is a necessary theoretical and practical knowledge and skills in the field of digital marketing that is characterized by large volumes of data. Apart from technique, students are introduced to a variety of tools for visualization and exploratory data analysis.
This module has a goal to prepare students to be data driven in decision-making process. Students will gain confident in data visualization, using visualization in finding business insights and support decision making process. The knowledge students acquire in this module will contribute to the overall skillset for their future employment as digital marketing specialists.
1. Few, S. (2009) Now You See It: Simple Visualization Techniques For Quantitative Analysis, Analytics Press, 1st edn, [s.l.]: Analytics Press
2. Tableau (2021) Tableau Support [Online]. Available at: https://help.tableau.com/current/guides/get-started-tutorial/en-us/get-started-tutorial-home.htm (Accessed 10 May 2021)
1. Ware, C. (2021) Information Visualization: Perception for Design, 4th edn, Cambridge, MA: Elsevier
2. Knafic C. N. (2021) Storytelling with Data: A Data Visualization Guide for Business Professionals, 1st edn, Hoboken, NJ: Wiley and Sons
3. Tufte, E. (2001) Visual Display of Quantitative Information, Cheshire, CT: Graphics Press
1. Storytelling Data (2021) Storytelling Data [Online]. Available at: https://www.storytellingwithdata.com/ (Accessed 10 May 2021)
2. Ribecca, S. (2021) The Data Visualization Catalogue [Online]. Available at: https://datavizcatalogue.com/ (Accessed 10 May 2021)
Minimal learning outcomes
- Select a tool for visualization and exploratory analysis of a given problem and correct visualization errors in the given examples.
- Critically interpret the techniques of analytical interaction and navigation in given examples.
- Critically interpret analytical patterns used in the given examples.
- Prepare data from a single data source for visual analysis and implement an interactive dashboard.
Preferred learning outcomes
- Evaluate and critically interpret visualization design and achieve graphical integrity of given example by using the selected tool.
- Select and apply appropriate techniques of analytical navigation and interaction for a given problem.
- Select, apply and evaluate appropriate analytical patterns for a given problem.
- Link data from multiple sources for visual analysis and implement complex interactive dashboard.