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Big Data and Data Visualisation

Code: 106675
Credits: 6
2026/2027
Degree programme Type Course
Communication in Organisations OP 4

Contact lecturer

Name :
Miguel Angel Martin Pascual
Email :
miguelangel.martin@uab.cat

Group languages

You can consult this information at the end of the document.

Prerequisites

Basic knowledge of statistics. Design rudiments. Knowledge of English for practices, readings and viewings.

Objectives

Understand the importance of big data with examples. Acquire data visualization analysis criteria. Being able to establish narratives with creative, attractive and truthful graphics.

Learning outcomes

  1. Find what is substantial and relevant in documents within the subject.
  2. Present a summary of the studies made, orally and in writing.
  3. Generate creative ideas in the workplace.
  4. Accept disagreement and show no disrespect to other persons, groups or institutions for reasons of race, gender, disability, etc.
  5. Submit high-quality coursework on time, which requires attention to both individual and group work.
  6. Work independently , on the basis of the knowledge acquired, to resolve the exercises set and interpret the data.
  7. Apply knowledge of research mechanisms to produce sound academic work, presenting and accounting for the results obtained.
  8. Propose new methods or well-founded alternative solutions.
  9. Propose projects and actions that incorporate the gender perspective.
  10. Communicate using language that is not sexist or discriminatory.
  11. Propose viable projects and actions to boost social, economic and environmental benefits.
  12. Weigh up the impact of any long- or short-term difficulty, harm or discrimination that could be caused to certain persons or groups by the actions or projects.
  13. Explain the explicit or implicit code of practice of one's own area of knowledge.
  14. Plan and execute academic projects in the field of big data.
  15. Share experiences with the group as a path to learning, in order to work subsequently in multidisciplinary groups.
  16. Adapt to information production processes and the professional routines of the organisation in relation to big data and data visualisation.
  17. Produce all types of messages and documents to be included in the organisation's media for internal and external audiences using big data and data visualisation.
  18. Establish communication objectives using big data.
  19. Prepare communications actions for internal and external audiences using big data and data visualisation.
  20. Apply big data and data visualisation to a communication plan that includes the internal and external information of the organisation.
  21. Apply big data to the analysis of specific cases for Planning the internal and external communication of organisations.
  22. Produce communications projects of different kinds using big data tacking into account the characteristics of the organisation.
  23. Apply the deontological code of the profession when carrying out the tasks of the course subject.
  24. Demonstrate ethical awareness in the application and management of big data and data visualisation to improve the activities of the organisation in its geographical area of operation.

Contents

Course content includes:

  • Data, Big Data, Big Big Data.
  • History, sources, types, tools.
  • Perception of data, color and visual attention.
  • Narrative, art and data analysis.
  • Practices, resources and projects.


The content of the subject will be sensitive to aspects related to the gender perspective and the use of inclusive language.

Learning activities and methodology

Title Hours ECTS Learning outcomes
Projects, viewings and readings 81 3.24 2, 5, 6, 7, 14, 15, 18, 24
Tutorials 12 0.48 2, 5, 6, 9, 13, 14, 18
Classes 30 1.2 4, 8, 9, 10, 11, 13, 15, 16, 18, 23
Seminars 15 0.6 1, 2, 3, 4, 5, 6, 7, 10, 14, 15, 24

Content presentation classes, seminars with specific cases and practical projects will be held.

The calendar will be available on the first day of class. Students will find all information on the Virtual Campus: the description of the activities, teaching materials, and any necessary information for the proper follow-up of the subject.

Annotation: within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

Assessment

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Practical jobs 50% 3 0.12 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 19, 20, 21
Test 30% 2 0.08 1, 2, 7, 10, 12, 13, 23
Seminars 20% 7 0.28 2, 5, 6, 7, 8, 10, 15, 17, 18, 22, 24

Evaluation activities description:

• Exam (30%)

• Seminars (20%)

• Practical exercises (50%)

It is mandatory to pass the exam and the practical exercises to pass the subject.

Students will be entitled to the revaluation of the subject. They should present a minimum of activities that equals two-thirds of the total grading. To have access to revaluation, the previous grades should be 3.5. The activities that are excluded from the revaluation process are seminars.


This course/module does not provide for a single-assessment system.

In this course, the use of Artificial Intelligence (AI) technologies is permitted as an integral part of assignment development, provided that the final outcome demonstrates a significant contribution from the student in terms of analysis and personal reflection. Students must clearly identify any content generated using AI, specify the tools employed, and include a critical reflection on how these technologies have influenced both the process and the final result of the assignment. Failure to disclose the use of AI in this assessed activity will be considered a breach of academic integrity and may result in a partial or total penalty to the assignment grade, or more serious sanctions in severe cases.


The performance of any irregularity in an evaluation act (academic fraud, plagiarism or improper use of AI, unless this use is expressly authorized by the teaching guide), which may lead to a significant variation in the grade, assumes that this act will be graded with a 0. In the event that the teaching guide foresees that in order to pass the subject it is an essential requirement to have obtained a minimum grade in this evaluation act or that there are several irregularities in the evaluation acts of the same subject, the final grade of this subject is 0. Apart from this, a disciplinary process may be instructed to the student that incurs any of these irregularities.


Any student suspected of submitting assignments that have been generated by AI, written by others or copied; include unattributed AI-generated content, or fall outside the permitted scope, may be asked to provide the preliminary work or other materials that can demonstrate it is original work and the result of their own authorship. They may also be asked to separately explain or justify their work. Teachers may also use AI detection systems or carry out any verification tasks they deem appropriate. If, after review, the instructor detects irregularities, the assignment may be graded zero, and the student may be subject to further disciplinary action.

Bibliography

Throughout the course other resources will be added to this bibliography.

CAIRO, Alberto. (2011). El arte funcional: infografía y visualización de información. Alamut.

KNAFLIC, Cole Nussbaumer (2015). Storytelling with Data: A Data Visualization Guide for Business Professionals. John Wiley & Sons.

ONTIVEROS, Emilio, LÓPEZ SABATER, Verónica, ed. Economía de los datos. Madrid: Fundación Telefónica; Barcelona: Ariel, D.L. 2018. [Consulta 11-05-2019].

https://www.fundaciontelefonica.com/arte_cultura/publicaciones-listado/pagina-itempublicaciones/itempubli/624/  

TORRES I VIÑALS, Jordi (2012). Del cloud computing al big data: visión introductoria para jóvenes emprendedores. Barcelona: UOC. [Consulta 24-07-2019]. https://campusvirtual.ull.es/ocw/mod/resource/view.php?id=6168&forceview=1

TUFTE, Edward R. (2001) 2nd ed. The visual display of quantitative information. Graphics Press

Software

In this subject, students are free to use the software that best suits their needs and technical capabilities. In the cases in which the work with a specific software is proposed, it will be with free software, which will be presented in the teaching sessions.

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE

Type of teaching Group Language Semester Shift
(TE) Theory 7 Spanish first semester afternoon
(PLAB) Practical laboratories 71 Spanish first semester afternoon