Master in Big Data & Analytics

1ST MODULEBusiness TransformationData Governance & Data Strategy - 5 ECTS
Data Analytics and specializations (Business Intelligence, Big Data and Data Science)Prioritizing business needs in technological decisionsData organization, management and governanceStrategic elements of a Data-Driven organizationKey factors in successful data lifecycle management
Business Analytics - 5 ECTS
Key management indicators: metrics, objectives, benchmarks and action plansDemand and sustainable solutionsData Analytics Business Case Study: Justifying investment in a projects. Convincing a CEO. Why invest in Data Analytics?Key management indicators: different areasSelf-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 TransformationCorporate innovation and key concepts: Open innovation and exploration vs. exploitationDesign and application of the Security Master PlanGDPR: personal data protection as an inspirational figure in Data Analytics business modelsOrganizational models: Data Compliance models and structures

2ND MODULEBusiness Intelligence & Data AnalyticsData Management for BI - 5 ECTS
Business Intelligence integrationFunctional processes and ETL techniciansData Warehousing models and cases of useThe importance of data and how it facilitates decision-makingHybrid Data Warehousing and Data Lakes
Data Visualization - 5 ECTS
Data Visualization and the technologies involvedData structures and models for visualizationDashboard design and developmentProject development on PowerBIApplications and examples of Data Visualization
Introduction to programming languages for Data - 5 ECTS
Basic programming conceptsIntroduction to PythonIntroduction to SQLIntroduction to R
Big Data - 5 ECTS
Foundations and technologies of Big DataImplementation of scalable architecturesPython applied to Big DataIntroduction to SparkCases of use and the different architectures involved
Data Science - 5 ECTS
Artificial Intelligence and its applicationsCharacteristics of Supervised and Unsupervised Machine LearningInterpretability of the resulting modelsAI toolsMost innovative AI techniques for high-level understanding (Deep Learning, model explainability, etc.)

3RD MODULEProfessional SkillsDigital Leadership & Data Talent - 5 ECTS
Innovation process, methods and techniquesDesign Thinking process, methods and techniquesAgile mindset and methodologiesEffective communication methods and techniquesStrategies for incorporating, developing and retaining people and their capacities in order to transform into a Data-Driven organization

Master’s Thesis15 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.