| Study program / study programs: Advanced data analytics |
| Course: Data Visualization |
| Teacher(s): Dragan O. Đurić, Antun Balaž, Aleksandra Alorić, Marija Mitrović Dankulov, Andrej Korenić |
| Course status: Elective |
| ECTS points: 10 |
| Prerequisites: none |
| Course objective: Detailed introduction to current visualization tools, methods and techniques in advanced data analytics. |
| Learning outcomes: Students will master appropriate programming methods and techniques for data visualization using state-of-the-art programming languages in advanced data analysis. |
| Course structure and content: Most of the classes are focused on practical data visualization skills. High level concepts are introduced through practical work, regardless of the tools used in the course. Different programming languages and visualization tools will be used in classes, according to the development of the field so that it always works with state-of-the-art tools. Introduction. Programming environments. Data visualization libraries and tools. Using the documentation. Introduction to selected data visualization tools. Getting started. Plot components. Aesthetics. Geometries. Toolbox Grammar of Graphics. Plot Layers. Scales, Axes and Legends. Positioning. Themes. Using visualization in data analysis. Data Analysis. Data transformation. Modeling for visualization. Programming with selected data visualization tools. |
| Literature/Readings: Hadley Wickham, „ggplot2, Elegant Plotting for Data Analysis“, Springer; 2nd ed. 2016. Winston Chang, „R Graphics Cookbook: Practical Recipes for Visualizing Data 2nd Edition“ O‘Reilly, 2018 Matplotlib user’s guide. Online. Avalilable: https://matplotlib.org/users/index.html |
| The number of class hours per week: Lectures: 4 Labs: 0 Workshops: 0 Research study: 3 Other classes: 0 |
| Teaching methods: Individual and group work; lectures and labs |
| Evaluation/Grading (maximum 100 points): Pre-exam requirements (Project): 50 Final exam (Data visualization problem): 50 |
