The course is organised into three independent blocks, each taught by a different professor. While the first block of the course (10 hours) introduces concepts related to data mining, which are generally applied to large datasets, the second block (18 hours) focuses on the statistical methods and knowledge required for modelling such large volumes of data. Particular emphasis is placed on methods of data aggregation, including sufficient statistics, meta-analysis and methods for combining different estimations, as well as scalable statistical methods, including divide and conquer methods, subsampling techniques and stochastic optimization such as Stochastic Gradient Descent. These methods will be studied in some real data application, specially related to linear and generalised linear models. Finally, the third block of the course (10 hours) introduces students to some of the most relevant methods in deep learning, with a special focus on neural networks and their applications.

Each block generally combines lecture sessions introducing theory and technical concepts and with laboratory and problem-solving sessions, which may be instructor-led or based on independent student work. During lecture sessions, professors may use slides, which will be shared via the Moodle course page. Similarly, examples in R and/or Python may be presented during laboratory and problem-solving sessions, and these will generally be made available via Moodle. Students are also expected to independently review supplementary materials shared via Moodle in order to deepen their understanding of the concepts introduced in the in-person sessions.

On the use of Artificial Intelligence (AI): In this course, the use of Artificial Intelligence (AI) technologies is permitted as an integral part of the development of coursework, provided that the final submission reflects a significant contribution by the student in terms of analysis and personal reflection. Students must clearly identify the parts that have been generated using AI, specify the tools used, and include a critical reflection on how these tools have influenced both the process and the final outcome of the assignment. Failure to disclose the use of AI will be considered a breach of academic integrity and may result in a reduction of the assignment grade or, in more serious cases, additional disciplinary sanctions.

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.

The course evaluation is carried out independently for each of the described blocks. Each block's weighting in the final grade corresponds to its proportion of the total course hours. Each block typically consists of a number of projects, which can be completed either individually or in groups. In some cases, these projects will require an oral presentation of the content and results. The projects in blocks 1 and 3 each account for 26% of the final grade of the course, while those in block 2 account for 48% of the final grade of the course.

For each project, a document will be published on the Moodle page, containing the project description and requirements, as well as all necessary materials for its completion (datasets, additional information sources, etc.), the submission deadline and procedure, and any other information that the professor considers relevant. Grades for each project, as well as the final grades for each block and for the course as a whole, will also be published on the Moodle page.