Objectives and competences
The student will:
• Understand the meaning and role of data analytics with emphasis on data visualization
• Define the process of problem solving.
• Understands data visualization techniques and tools.
• Use appropriate data visualization and analytics methods.
• Use various data analytics software.
• Be able to critically evaluate the results and present the findings using data visualization.
Content (Syllabus outline)
1. Introduction to data analytics and visualization
2. Place and role of visualization in data analytics- understanding the problem and context
3. Use of visualization in organization and complex systems decision support
4. Theoretical basics on visualization (presentation of information, colours, symbols, cognitive load, perception)
5. Static and interactive data visualization methods, visualization of big data
6. Software tools and languages for data visualization
7. Storytelling with visualization and data analytics
8. Data pre-processing
9. Descriptive statistics use in data understanding
10. Visualization and data types
11. Choosing the effective visualization
12. Critical evaluation of data visualization
13. Cases of effective and ineffective visualizations
Learning and teaching methods
• Lectures
• Team work
• Case studies
• Excersises
Intended learning outcomes - transferable/key skills and other attributes
Knowledge and understanding of:
• At the end of the course, the students will be able to:Define the problem and its context.
• Identify opportunities for application of data visualization.
• Acquire data from available sources.
• Pre-process data.
• Choose appropriate methods with emphasis on data visualization.
• Conduct data analysis using data visualization tools SPSS, Excel, Orange and programming language Python (Matplotlib).
• Interpret the results.
• Critically evaluate the results of data analysis.
• Effectively communicate findings using visualization.
Readings
1. Kononenko, I., Robnik Šikonja, M.: Inteligentni sistemi. Založba FE in FRI, Ljubljana, 2010.
2. Munzner T. (2014): Visualization Analysis & Design, CRC Press, Taylor & Francis Group
3. Nussbaumer Knaflic, C. (2015): Storytelling with data – a data visualization guide for business professionals, Wiley
4. Provost, F. & Fawcett, T. (2013): Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking 1st Edition, O'Reilly
5. Packowski, W.R. (2021): Business Analytics: Data Science for Business Problems, Springer
6. Wilke, C.O. (2019): Fundamentals of Data Visualization, A Primer on Making Informative and Compelling Figures, O'Reilly
7. Tufte, E.R. (2001): The Visual Display of Quantitative Information, Second Edition, Graphics Press, Cheshire, Connecticut
Prerequisits
Basics computer skills.
Conditions for exam admission:
Completed assignments at lectures and tutorials.
Completed project work.
Additional information on implementation and assessment Completed assignements (20 %)
Project work (30 %)
Written exam (50 %)
