| Teaching Module | Hourly volume | ECTS |
| Scientific Computing Project Management (SCPM) UE1 – Software engineering for scientific computing (24h) – Development environments (8h) – Oriented-object programming (24h) | 56 H | 6 |
| Methods for Scientific computing (SC) UE2 – Convex optimization (16h) – Linear algebra for data science and scientific computing (24h) – Algorithm complexity and scalability (16h) | 56 H | 6 |
| High performance architecture and programming (HPC) UE3 – Technologies, hardware (20h) – Parallel programming techniques on the CPU (18h) – Parallel programming techniques on the GPU (18h) | 56 H | 6 |
| Machine Learning algorithms (ML) UE4 – Statistics and probabilities (24h) – Numerical algorithms for machine learning (32h) | 56 H | 6 |
| High Performance Algorithms and Deep Learning (HPDA) UE5 – Deep Learning algorithms and architectures (20h) – Data processing platforms (Hadoop, Spark) (20h) – Massive data visualization techniques and tools (16h) | 56 H | 6 |
| Numerical modelling and simulation (NMS) UE6 – Discrete systems (12h) – Continuous systems (12h) – Optimization and inverse problems (12h) – Stochastic and statistical methods (12h) – Signal and image processing (12h) – Scientific Machine Learning (12h) – NLP with Deep Learning (12h) – Randomized Linear Algebra (12h) | 24 H | 3 |
| Digital Society (NS) UE7 – HPC society (6h) – Digital and intellectual property (6h) – Data ethics (6h) – Virtualization and security (6h) | 24 H | 3 |
| Projects and case study (CSP) UE8 | 56 H | 6 |
| Conferences | 20 H | 3 |
