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Data Visualisation

Code 16680
Year 2
Semester S2
ECTS Credits 6
Workload PL(30H)/T(30H)
Scientific area Informatics
Entry requirements N/A
Learning outcomes The curricular unit introduces the strategies to explore, interpret and summarise a dataset using visual or textual elements. Considering this, the curricular unit has the following objectives:

• Expose the main problems, concepts and approaches in the field of data visualisation;
• Know the typical steps in the production chain of the analysis and visualisation process of a data set;
• Study data pre-processing techniques, which facilitate the understanding/visualisation of the data;
• Introduce the different types of graphical representations to summarise data.
• Introduce the techniques for interpreting the decision process of machine learning models;
• Master the techniques necessary to build visual stories through datasets.
Syllabus Introduction to data visualisation: main problems, concepts, and approaches in the field of data visualisation.

Introduction to the production chain of the analysis and visualisation process of a dataset.

Data pre-processing techniques
• Data cleaning techniques
• Data transformation techniques
• Data reduction techniques

Visual representations of data
• Representations of univariate and multivariate data
• Conditional, temporal and multi-aggregate value graphs
• Visual analytics for spatial and temporal datasets

Techniques for Interpreting the Decision Process of Machine Learning Models

Visual Data Storytelling
• Introduction to Tableau Software
• Analysis of a dataset through the techniques studied and the use of Tableau software to create visual representations.
Main Bibliography [1] Stephen Few. Now you see it: simple visualization techniques for quantitative analysis. Analytics Press, 2009.

[2] Roy Jafari, Hands-On Data Preprocessing in Python: Learn how to effectively prepare data for successful data analytics, 2022

[3] He, Xiangyang, et al. "Multivariate spatial data visualization: a survey." Journal of Visualization 22.5 (2019): 897-912.

[4] Christoph Molnar, Interpretable Machine Learning: A Guide For Making Black Box Models Explainable, 2018.

[5] Lindy Ryan. Visual data storytelling with tableau: story points, telling compelling data narratives. Addison-Wesley Professional, 2018.
Language Portuguese. Tutorial support is available in English.
Last updated on: 2025-03-25

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