Master in Big Data & Analytics

Business Transformation

Data Governance & Data Strategy - 5 ECTS

• Data Analytics and specializations (Business Intelligence, Big Data and Data Science)
• Prioritizing business needs in technological decisions
• Data organization, management and governance
• Strategic elements of a Data-Driven organization
• Key factors in successful data lifecycle management

Business Analytics - 5 ECTS

• Key management indicators: metrics, objectives, benchmarks and action plans
• Demand and sustainable solutions
• Data Analytics Business Case Study: Justifying investment in a projects. Convincing a CEO. Why invest in Data Analytics?
• Key management indicators: different areas
• Self-service vs. Pre-defined reporting: Applying security. What challenges, impacts and costs are involved?

Digital Transformation & Challenges of Cybersecurity and Cyberlaw - 5 ECTS

• Technological innovation and Digital Transformation
• Corporate innovation and key concepts: Open innovation and exploration vs. exploitation
• Design and application of the Security Master Plan
• GDPR: personal data protection as an inspirational figure in Data Analytics business models
• Organizational models: Data Compliance models and structures

Business Intelligence & Data Analytics

Data Management for BI - 5 ECTS

• Business Intelligence integration
• Functional processes and ETL technicians
• Data Warehousing models and cases of use
• The importance of data and how it facilitates decision-making
• Hybrid Data Warehousing and Data Lakes

Data Visualization - 5 ECTS

• Data Visualization and the technologies involved
• Data structures and models for visualization
• Dashboard design and development
• Project development on PowerBI
• Applications and examples of Data Visualization

Introduction to programming languages for Data - 5 ECTS

• Basic programming concepts
• Introduction to Python
• Introduction to SQL
• Introduction to R

Big Data - 5 ECTS

• Foundations and technologies of Big Data
• Implementation of scalable architectures
• Python applied to Big Data
• Introduction to Spark
• Cases of use and the different architectures involved

Data Science - 5 ECTS

• Artificial Intelligence and its applications
• Characteristics of Supervised and Unsupervised Machine Learning
• Interpretability of the resulting models
• AI tools
• Most innovative AI techniques for high-level understanding (Deep Learning, model explainability, etc.)

Professional Skills

Digital Leadership & Data Talent - 5 ECTS

• Innovation process, methods and techniques
• Design Thinking process, methods and techniques
• Agile mindset and methodologies
• Effective communication methods and techniques
• Strategies for incorporating, developing and retaining people and their capacities in order to transform into a Data-Driven organization

Master’s Thesis

15 ECTS

This is far more than just a research project required to gain a Master qualification. It is the optimal time and place to combine all your academic experience, professional ambition, personal vision and creativity. It is your opportunity to put all the knowledge and skills you have acquired into practice and work towards the goals you set yourself.