Checking date: 30/04/2024

Course: 2024/2025

Data Visualization
Master in Computational Social Science (Plan: 472 - Estudio: 375)

Coordinating teacher: UCAR MARQUES, IÑAKI

Department assigned to the subject: Statistics Department

Type: Compulsory
ECTS Credits: 6.0 ECTS


Requirements (Subjects that are assumed to be known)
Introduction to Programming with R (19151)
- Knowledge of the general principles of analytical design, graphical elements and their visual perception. - Ability to select the type of representation and graphic elements most appropriate to the type of data and the result to be communicated. - Ability to read, understand, analyze and elaborate graphic representations with social data. - Ability to produce automated reports and dashboards with reproducible visualizations.
Skills and learning outcomes
Description of contents: programme
1. Fundamentals of graphical practice 1.1. Why graphics 1.2. Graphical integrity 1.3. Graphical perception 1.4. Principles of graphical representation 2. The grammar of graphs in R 2.1. Building graphs layer by layer 2.2. Guides and scales 2.3. Coordinate systems 2.4. Facets 2.5. Themes 3. Data visualization in R 3.1. Distribution 3.2. Correlation 3.3. Ranking 3.4. Part of a whole 3.5. Evolution 3.6. Maps 3.7. Flow 3.8. Other techniques 4. Data communication in R 4.1. R Markdown 4.2. Documents and notebooks 4.3. Presentations 4.4. Other formats 4.5. Dashboards
Learning activities and methodology
Training Activities: - Theoretical-practical classes - Tutorials - Group work - Individual student work Teaching Methods: - Presentations in the professor's lecture room with computer and audiovisual support, in which the main concepts of the subject are developed and a bibliography is provided to complement the students' learning. - Critical reading of texts recommended by the subject professor: Press articles, reports, manuals and/or academic articles, either for later discussion in class, or to expand and consolidate knowledge of the subject. - Resolution of practical cases, problems, etc. raised by the professor, either individually or in a group. - Presentation and discussion in class, under the moderation of the professor, of topics related to the content of the subject, as well as practical case studies. - Developing pieces of work and reports, individually or in group.
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100

Basic Bibliography
  • Munzner, T.. Visualization analysis and design. CRC Press. 2014
  • Tufte, E. R.. The visual display of quantitative information. Graphics Press. 2018
  • Wickham, H., & Sievert, C.. ggplot2: Elegant graphics for data analysis. Springer. 2016
Additional Bibliography
  • Cleveland, W. S.. The elements of graphing data. Wadsworth Inc. 1985
  • Meirelles, I.. Design for information. Rockport Publishers. 2013
  • Rahlf, T.. Data Visualisation with R: 111 examples. Springer. 2019
  • Tufte, E. R.. Envisioning information. Grahpics Press. 2018
  • Tufte, E. R.. Visual explanations: Images and quantities, evidence and narrative. Graphics Press. 2019
  • Tufte, E. R.. Beautiful evidence. Graphics Press. 2019
  • Ware, C.. Information visualization: Perception for design. Elsevier. 2021
  • Wilkinson, L.. The grammar of graphics. Springer New York. 2005

The course syllabus may change due academic events or other reasons.