The course combines theoretical sessions with practical and applied activities.

Theoretical content will be complemented by in-class exercises, manipulation of real data, and hands-on practice with statistical software.

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.

Statistics Problem-Solving Exercises (33.3%): Regular completion and the quality of the practical exercises proposed during the sessions will be assessed. These exercises will be aimed at developing methodological and technical skills related to linguistic data analysis and the use of R software. The final exercise will be graded, while the remaining exercises will be assessed only as submitted or not submitted.

Research Project Preparatory Document (33.3%): This consists of the development of a research project for a funding application aimed at carrying out a doctoral thesis. In this final assignment for the course, students must design and present an academic research project. The work must include a preliminary section presenting the research topic, as well as the research questions and hypotheses. Secondly, the methodological aspects and the suitability of the methodology for the proposed objectives must be developed. This section must specify the variables, define the methodological approach, and justify its selection. It must also include the expected characteristics of the population and sample, as well as the limitations and difficulties associated with sample selection, and provide a plan for data collection. Finally, the assignment must address the interpretation of the expected data (data processing, software, statistical tests, etc.) and include a final reflection on the possibility of replicating the method in order to validate the procedure. The writing of the work must conform to academic standards.

Formal Data Analysis Exercise (33.3%): Based on a set of phonological data, the student must identify the relevant descriptive generalizations and propose a formal analysis within the framework of Optimality Theory. The final assignment must adopt the format of a short scientific article.

In this course, the use of Artificial Intelligence (AI) technologies will be permitted or restricted depending on the assessment activity. The permitted and prohibited uses will be specified in the course presentation. Students must clearly identify which parts of their work have been generated using AI tools, specify the tools employed, and include a critical reflection on how these tools have influenced the process and the final outcome of the activity. A lack of transparency regarding the use of AI in an assessed activity will be considered a breach of academic integrity and may result in a partial or total penalty to the grade for the activity, or in more serious sanctions where applicable. In addition, this course will make transversal use of Artificial Intelligence in data processing. At the time of each assessment, the instructor will inform students (via Moodle) of the assessment procedure and the date for reviewing grades.

To pass the course, students must pass all three assessment activities. If any of these activities is not passed, it may be retaken during the resit period. A student will receive a grade of “Not assessable” if they have not submitted more than 30% of the assessment activities. Any irregularity in an assessed activity (plagiarism, fraud, or similar misconduct) will result in a grade of 0 for that activity and will exclude the possibility of retaking it. Likewise, the commission of multiple irregularities will result in a grade of 0 for the course.