Data Science

Visualisation and analytical software tools

  • Class 15
  • Practice 30
  • Independent work 45
Total 90

Course title

Visualisation and analytical software tools

Lecture type


Course code






Lecturers and associates

Course objectives

The course objective is to introduce students with basic visualization techniques, explorative data analysis and predictive modeling. 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 modeling tools.


Introduction to data visualization. The visual apparatus. The most common errors in data visualization. Types of visualization tools. Analytical interactions: comparison, sorting and grouping, aggregation, change the definition of variables. Analysis of the structure, distribution and time series. Forecasting. Analysis of deviations and correlations. Spatial analysis. Dashboards for visualization and exploratory analysis. Data processing in SPSS Modeler. Feature engineering. Predictive modeling.

Required reading

Few, S. : Now You See It: Simple Visualization Techniques For Quantitative Analysis, Analytics Press, 2009.

Additional reading

Tufte, E. : Visual Display Of Quantitative Information, Graphics Pr, 2001.

Minimal learning outcomes

  • Select appropriate visualization tool, correct visualization errors and achieve graphical integrity of given example.
  • Prepare data from a single data source for visual analysis and create an interactive dashboard based on given dataset and requested granularity levels.
  • Join data from multiple sources, aggregate, filter and restructure tabular data, choose and apply appropriate method for handling missing values.
  • Apply and interpret the results of simple machine learning algorithms and statistical models.

Preferred learning outcomes

  • Select appropriate visualization tool, correct visualization errors, and critically interpret choice of analytical patterns and the techniques of analytical interaction used to achieve graphical integrity of given example.
  • Link data from multiple sources for visual analysis, create complex interactive dashboard - argue the choice of granularity and used dashboard elements.
  • Apply advanced data preprocessing techniques to create features (variables) for predictive models.
  • Apply and interpret the results of complex machine learning algorithms and statistical models, evaluate model performance and choose the best model using standard machine learning metrics.
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