1. Fundamentals of Big Data: processes and use cases 2. Big Data architecture and business applications 3. Infrastructure: storage and processing techniques in Big Data 4. Analysis in Big Data 5. Digital strategy and business models for Big Data startups 6. Big Data applications and domains: ERP, CRM, IoT, FinTech, etc. 7. Big Data in the era of Language AI

AF1. THEORETICAL AND PRACTICAL CLASSES. These classes will present the background knowledge students must acquire. Students will receive class contents and basic reference texts to facilitate class follow-up and subsequent work. Students will solve exercises and practice problems, and workshops and assessment tests will be held to acquire the necessary skills. AF2. TUTORIALS. Individualized (individual tutorials) or group (collective tutorials) assistance to students provided by the instructor. AF3. INDIVIDUAL OR GROUP STUDENT WORK. MD1. THEORY CLASS. In-class presentations by the instructor, supported by computer and audiovisual media, in which the main concepts of the subject are developed and materials and bibliography are provided to complement student learning. MD2. PRACTICAL WORK. Resolution of practical cases, problems, etc. posed by the instructor, individually or in groups. MD3. TUTORING. Individualized (individual tutoring) or group (collective tutoring) assistance for students provided by the instructor. For 6-credit courses, 4 hours will be allocated, with 100% face-to-face attendance.