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Data visualization: Techniques and tools

  • Class 30
  • Practice 30
  • Independent work 60
Total 120

Course title

Data visualization: Techniques and tools

Lecture type

Elective

Course code

21-02-528

Semester

3

ECTS

4

Lecturers and associates

Course overview

The objectives of this module are to enable students to:
• Interpret the fundamental problems, concepts, and approaches in the design and analysis of data visualization systems.
• Interpret the stages of the visualization pipeline, including data modelling, mapping data attributes to graphical attributes, perceptual issues, existing visualization paradigms, techniques, and tools, and evaluating the effectiveness of visualizations for specific data, task, and user types.
• Explain basics of physiology of the human visual system, perceptual processing, human cognition framework for data visualization
• Interpret characteristics of the most relevant data visualization tools

Students learn visualization techniques, explorative data analysis and predictive modelling. It is a necessary theoretical and practical knowledge and skills for all business areas that are characterized by a large amount of data. Besides the technique, students are introduced to various visualization tools, exploratory data analysis tools and predictive modelling tools.

It is important for students to take this module in order to develop the appropriate and relevant visualization techniques and tools that has evolved into a discipline, drawing from such fields as computer graphics, human-computer interaction, perceptual psychology, and art. The emphasis of the module will be on exposing students to the current research issues and on identifying potential research topics in data visualization as it applies to large-scale data systems.

Literature

Essential reading:
1. Algebra University College (2020), Data visualization techniques and tools handbook, Zagreb: Algebra University College
2. Ward, M., Grinstein, G. G., and Keim, D. (2015). Interactive Data Visualization Foundations, Techniques, and Applications, Second Edition (Vol. Second edition). Boca Raton: A K Peters/CRC Press, available at http://search.ebscohost.com/login.aspx?direct=trueandsite=eds-liveanddb=edsebkandAN=1763678, Boca Raton, Florida, United States

Recommended reading:
1. Kimball, R., Ross, M., Thornthwaite, W., Mundy, J., Becker, B. (2008) The Data Warehouse Lifecycle Toolkit, 2nd Edition, Hoboken: Wiley
2. Laberge, R. (2011) The Data Warehouse Mentor: Practical Data Warehouse and Business Intelligence Insights, New York: McGraw-Hill Education

Minimal learning outcomes

  • Identify and explain visualization tools
  • Explain selection/options of analytical patterns and techniques of analytical interactions.
  • Describe process of connecting data from different data sources using visualization tools.
  • Describe steps in application of advanced data preparation methods for visualization.
  • Describe complex interactive control panel
  • Identify complex methods of machine learning and statistical modelling to data for data analysis.
  • Identify scenario to be tackled with machine learning methods and statistical modelling based on a business / scientific problem.
  • Explain most common machine learning models in data analysis.

Preferred learning outcomes

  • Select the appropriate visualization tool to analyze the data depending on the data requirements.
  • Critically interpret the choice of analytical patterns and techniques of analytical interactions.
  • Connect data from different data sources using visualization tools.
  • Apply advanced data preparation methods for visualization.
  • Create a complex interactive control panel.
  • Apply complex methods of machine learning and statistical modelling to data for data analysis.
  • Interpret the results of machine learning methods and statistical modelling in a business / scientific problem.
  • Evaluate machine learning models for their adaptation in data analysis.
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