MBA in Computer Science and Artificial Intelligence (Level 7)
Duration: 10 months
Mode: Online
Entry requirements: Minimum BSc in IT and verifiable professional experience
This MBA is designed to train leaders and experts capable of leading digital and AI projects, combining technical expertise, strategic vision, and management skills.
Competency blocks:
Block 1 – Advanced Foundations of Computer Science Objective: Acquire the solid technical foundations required for architecting and developing complex IT systems.
- Architecture of IT systems and networks
- Advanced programming (Python, Java, C++, modern frameworks)
- Operating systems and virtualization
- Advanced databases (SQL, NoSQL, Big Data)
- Information security and basic cryptography
Block 2 – Artificial Intelligence, Data Science and Machine Learning Objective: Design, develop and evaluate performant and responsible AI solutions.
- Mathematical foundations for AI (statistics, linear algebra, probability)
- Data science and data engineering
- Supervised and unsupervised machine learning
- Deep learning and neural networks
- Natural language processing (NLP) and computer vision
Block 3 – AI Development, Deployment and Industrialization Objective: Convert AI prototypes into production-grade, scalable and reliable solutions.
- Developing AI applications (APIs, microservices)
- MLOps and DevOps for AI
- Cloud computing (AWS, Azure, GCP)
- Containerization and orchestration (Docker, Kubernetes)
- Model testing, validation and monitoring
Block 4 – Management, Strategy and Technological Innovation Objective: Train leaders able to drive digital transformation and AI-driven innovation.
- Management of technical and data teams
- IT and AI project management (Agile, Scrum, SAFe)
- Digital strategy and organizational transformation
- Innovation, entrepreneurship and intrapreneurship
- Data-driven and AI-based decision support
Block 5 – AI Ethics, Governance and Societal Impact Objective: Develop a responsible, lawful and sustainable approach to artificial intelligence.
- AI ethics and algorithmic bias
- Data governance and compliance (GDPR, AI Act)
- Advanced cybersecurity and data protection
- Social and environmental responsibility of digital technology
- Audit, compliance and AI-related risk management
Assessment methods:
- Preparation and defense of a 100-page thesis before an examination committee