| Code |
16680
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| Year |
2
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| Semester |
S2
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| ECTS Credits |
6
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| Workload |
PL(30H)/T(30H)
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| Scientific area |
Informatics
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Entry requirements |
N/A
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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.
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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.
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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.
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Teaching Methodologies and Assessment Criteria |
Teaching methodologies:
• Theoretical classes;
• Practical-laboratory classes;
• Individual project;
• Tutoring to clarify doubts and accompany the student in the development of his project.
Teaching methodologies:
- Lectures;
- Practical-laboratory classes;
- Individual or collective project;
- Tutoring to clarify doubts and accompany the student in the development of their project.
The assessment consists of three main elements:
- a written test (T) to assess knowledge, with a weight of 35% in the final grade;
- individual practical assignments (TP), with a weight of 35% in the final grade.
- a practical mini-project (MP) for individuals or groups of two, with an execution report and presentation, with a weight of 30 per cent in the final grade.
Teaching-Learning Classification (CEA)= 0.35T + 0.35TP + 0.3MP
The final exam consists of a written test, covering all the material taught, marked out of 20 marks.
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Language |
Portuguese. Tutorial support is available in English.
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