Programs

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

Obligatory

Course code

20-02-065

Semester

3

ECTS

3

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.

Content

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.
Share: Facebook Twitter

Excel at what you love doing. Light the spark.

Apply now!

Why is Algebra a safe choice for your future?

A Strong
Tailwind

Here you will learn all about information technologies and prepare for a career that is constantly in demand. We offer you a platform for personal growth that makes you a prime target for employers.

Modern Methodology

We refuse to stand still in a rapidly changing world. Our programs stay relevant and keep up with modern trends.

Matchless
Quality

We take pride in numerous accolades and our title of The best professional study program in Croatia and constantly strive to justify that trust. We do not take our task lightly, knowing that your future depends on it.

Newsletter

Stay informed about everything that goes on at the University. Subscribe to our newsletter.