The course is structured around three teaching methodologies: lectures, theoretical-practice seminars, and the development of a data visualization project.

• Lectures aim to deliver the core content of the syllabus.
• Theoretical-practical seminars focus on bridging theoretical concepts and their real-world applications.
• The project involves analyzing a dataset and creating a series of visualizations and an infographic.

A detailed schedule outlining the content of each session will be presented on the first day of the course and will be available on the course’s Virtual Campus, where students will find the various teaching materials deemed appropriate by the instructors and necessary information for effective course monitoring. Should the teaching modality change for reasons of force majeure according to the competent authorities, the teaching staff will inform students of any modifications to the course schedule and teaching methodologies.

Class attendance and participation in the sessions dedicated to the project and seminars is mandatory.

Note: The course content will be sensitive to issues related to gender perspective and the use of inclusive language.

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.

This course evaluation is based on continuous assessment (AC).

Specifically, the continuous assessment breakdown is as follows:

• Written exam (40%)
• Theoretical-practical seminars (40%)
• Project (20%)

The final grade will be the weighted average of the three graded components. However, to calculate this average, students must achieve a minimum score of 5 out of 10 on the written exam.

Given the practical nature of the seminars, active participation is required for grading. If a student is absent from a theoretical-practical seminar, they must complete an alternative assignment to make up the grade.

Reassessment: Students will be entitled to reassessment in the course if they have been evaluated through a set of activities accounting for at least two-thirds of the total course grade.

Only the written exam and the theoretical-practical seminars are eligible for reassessment. Consequently, the project is excluded from the reassessment process.

Single-assessment: This course does not provide for a single-assessment system.

Repeat students (second sitting or beyond): Assessment will consist of a synthesis test covering the learning outcomes set out in the course guide. In this case, the course grade will correspond solely to the result of that exam.

Plagiarism: In the event that the student commits any irregularity that could lead to a significant variation in the grade of an assessment act, this assessment act will be graded with 0, regardless of the disciplinary process that may be instituted . In the event that several irregularities occur in the evaluation acts of the same subject, the final grade for this subject will be 0.

Unassessable: Will be considered not assessable when the total weight of submitted assessment activities falls below 1/3 of the total course grade. Unexcused absences from graded activities may contribute to this determination.

Artificial intelligence: For this course, the use of Artificial Intelligence (AI) technologies is permitted exclusively for tasks explicitly indicated in class. Students must clearly identify any parts generated with these technologies, specify the tools used, and include a critical reffection on how AI has inffuenced the process and final outcome 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.

Irregularities: 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 7 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.