Master in Big Data Analytics
The Master's Degree in Big Data will prepare you to transform large volumes of data into strategic insights for business decision-making. Through practical training alongside experts, you will develop the technical skills and practical experience needed to overcome the challenges of advanced data analysis.
With this Master’s Degree in Big Data in Madrid, you will be able to:
- Master the strategic analysis of big data, applying techniques from business intelligence, business analytics, data analytics, machine learning and data mining.
- Gain technical skills and practical experience in data science, big data infrastructures , and real-world projects.
- Complete 225 hours of professional internships at leading companies such as Accenture, Deloitte, Repsol, Indra or Telefónica.
- Obtain an Amazon Web Services (AWS) certification to enhance your professional profile for roles such as Big Data Architect, Data Scientist or Data Quality Engineer.
Official degree issued by Universidad Europea de Madrid
| Campus-based | Alcobendas | 10 months, 60 ECTS | Start: October 2026 | School of Architecture, Engineering, Science and Computing - STEAM |
AWS Academy member institution
You can obtain Cloud Practitioner and Machine Learning Specialty certification from the AWS Academy
These courses prepare you for the AWS Academy Cloud Computing and Artificial Intelligence certifications.
Tools that empower your Big Data journey
On the master's degree in Big Data Analytics you will learn how to use advanced tools and platforms to process and analyse large volumes of data. You will work with technologies such as AWS, Google Cloud, Apache Spark, Hadoop and Power BI, developing the skills required to apply advanced analytics and machine learning in real-world scenarios.
A vision for the future of Big Data
Manuel García Fernández
Director of the Master's Degree in Big Data Analytics
Study plan
What will you learn in the Master’s in Data Analytics?
On this Master's programme, you will learn to manage, analyse, process and visualise data using real-life scenarios, simulations and the cutting-edge tools employed by big data experts. You will choose between an internship and a research pathway, completing the programme with a final project that will allow you to apply your knowledge and earn an official qualification in Big Data Analytics.
1
1st semester - Fundamentals of big data processing and management
During the first semester, students develop a strong understanding of data management and large-scale processing. This module introduces the key technologies and concepts that define the Big Data ecosystem, covering data ingestion, distributed computing and next-generation databases, while developing the analytical mindset required for managing data-driven projects.
- In the Data Processing module, students deepen their Python skills to work with large datasets. They learn the complete data preprocessing lifecycle for machine learning, from ingestion to preparation, using professional tools and real-world examples.
- Distributed Systems Computing focuses on the fundamentals of distributed computing and the architectures used for managing large-scale data. This prepares students to design and operate efficient Big Data systems.
- New Generation Databases introduces distributed and NoSQL databases, enabling students to efficiently manage and store massive datasets across modern data infrastructures.
- Service Models combines applied analytics in key sectors (marketing, finance, HR, insurance and telecoms) with the principles of data governance to ensure students understand how organisations can manage and leverage their data strategically.
2
2nd semester - Advanced analytics, artificial intelligence and data strategy
The second semester focuses on applying advanced big data tools and methodologies to real-world problems. Students learn to design intelligent systems, visualize complex data, and manage large-scale projects. This prepares them to lead data-driven initiatives and support strategic decision-making in modern organizations.
- Students explore cloud computing architectures by studying cloud service models (IaaS, PaaS and SaaS), virtualization, containerization, and scalable infrastructures for efficient data management.
- They also learn the fundamentals of AI and machine learning, including supervised and unsupervised learning, deep learning, and model optimization. They then apply these techniques to real-world datasets.
- The program also trains students in data visualization, equipping them with the skills to transform complex data into actionable insights through effective visual communication and dashboards.
- Students develop project management skills, learn to plan and execute Big Data projects using industry methodologies and tools.
- The semester culminates in a research project, a Master's thesis (TFM) and an internship, in which students can apply their skills to real projects, gain hands-on experience, and strengthen their employability and professional development.
Collaborating entities
Students of the Master’s in Big Data Analytics will benefit from the collaboration agreements between Universidad Europea and prestigious companies with which students will be able to develop their final Master’s project on real projects proposed by the same companies, and receive help and advice from specialists in the sector.
Course programme structure
Programa de estudios
PRIMER CURSO
| Materia | ECTS | Tipo | Idioma de impartición |
|---|---|---|---|
| Cloud Computing Architectures | 6 | COMPULSORY | English (EN) |
| Data Processing | 6 | COMPULSORY | English (EN) |
| Machine Learning | 6 | COMPULSORY | English (EN) |
| Distributed Systems Computing | 6 | COMPULSORY | English (EN) |
| New Generation Databases | 6 | COMPULSORY | English (EN) |
| Service Models | 6 | COMPULSORY | English (EN) |
| Data Visualisation | 6 | COMPULSORY | English (EN) |
| Project Management | 3 | COMPULSORY | English (EN) |
| Master's Dissertation | 6 | COMPULSORY | English (EN) |
| Research | 9 | OPTIONAL | English (EN) |
| External Student Internship | 9 | OPTIONAL | English (EN) |
Internships
Gaining experience based on what you have learnt in your degree is the best way to enter the job market.
If you would like to improve your work experience before finishing your university studies, you can do an extracurricular internship. You can do one in any academic year, but we would remind you that internships are a training complement to your studies. Therefore, the more knowledge you’ve already acquired over the course of your programme, the more you’ll benefit from the internship experience.
Faculty
The faculty is made up of active professionals who bring real-world experience into the classroom. They connect their daily industry insights directly to the subjects you’ll study. Many of them have the highest academic qualifications, so you will learn from recognized experts in the field.
Our distinguished faculty
- Manuel García Fernández
Director of the Master's Degree Program in Big Data Analytics. He has extensive professional experience consulting in the field of data and AI and has implemented numerous projects in real environments across various sectors. Previously, he worked in research in Spain and internationally as a consultant. - Óscar Marbán Gallego
Doctorate in Computer Science from the Polytechnic University of Madrid. He has taught 22 different subjects over the course of 19 academic years in various degree programs, and he has published 43 articles in international and national journals and conferences. He has participated in and managed several competitive and non-competitive research projects. Two of his research projects were recognized by CNAI. He is the IT Director and BI Manager at a travel wholesaler. - Pablo del Saz-Orozco
Technical Director of Modeling and Simulation at Invesyde and a Senior Data Scientist with extensive experience in artificial intelligence research and teaching. - Samuel García Saboya
As a software, data and mobile architect, with experience in the design and development of scalable and robust architectures for applications and systems. - Joaquín García Onrubia
He is the Head of Data Architecture at the Center of Excellence for Data Governance and Advanced Analytics within Enagás's Digitalization and Information Technology Department. He reports directly to the CDO and CIO. He leads cross-functional teams of technology and business experts in implementing advanced analytics projects and formulating the company's global data strategy. - Carlos Jaime Pages
He is a trainer with several years of experience in data science, artificial intelligence, and AWS. He also has a master's degree and experience in business coaching. He has over 10 years of management experience at multinational financial companies and extensive experience creating startups in various sectors and countries. - Javier Pérez Piñeiro
Doctorate in Applied Information Technology (cum laude); Master's degrees in Big Data Innovation and Technology and Business Administration; and Senior Computer Engineer specializing in Artificial Intelligence. - Enrique Puertas Sanz
Computer engineer and a Doctor of applied information technology. He lectures on artificial intelligence and big data and is the director of the “Artificial Intelligence and Human-Machine Interaction” research group. He is also the co-director of the “IASalud” group. - Francisco Damián Maleno
He holds bachelor's degrees in Computer Engineering and Biotechnology, as well as a master's degree in Statistical Methods for Big Data Analysis from UC3M. He has professional experience as a Data Scientist and AI Engineer for companies such as IBM, Capgemini, and Accenture, and is a certified Cloud Solutions Architect (AWS and Google) and Cloud Solutions Architect for Data Science (AWS and Microsoft). He is also an expert in developing generative AI solutions. - Juan Vallés Álvarez
He is a senior telecommunications engineer with 20 years of experience in the telecom sector. He has built his career working for leading multinational companies such as Orange, Ericsson, and Telefónica and has completed an advanced program in big data at the Telefónica Technology Institute. He is a certified cloud architect in AWS and Oracle Cloud and a senior expert in data technologies and platforms, including Elastic and Splunk. He holds a master's degree in telecommunications networks from UPM.
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1
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2
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3
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Frequently Asked Questions
What can I do after MSc Big Data Analytics?
After studying the master in Big Data Analytics, you’ll have a profile that many companies are looking for today. Many of those who graduate from this programme enjoy successful careers in a wide variety of industries in roles including:
- Data Scientist.
- Data Architect.
- Expert in data analysis and machine learning.
Is big data analytics in-demand?
Yes. Professional with experience in big data analytics are in high demand today. With the explosion of digital data generated by various sources such as social media, e-commerce, and the Internet of Things (IoT), organisations are looking for ways to extract valuable insights and make data-driven decisions. Industries such as healthcare, finance, retail, and manufacturing are all utilizing big data analytics to improve their operations and gain a competitive edge.
What does a master in Big Data involve?
Our master's degree in Big Data is structured around a curriculum in which you will learn in depth about data management, analysis, processing and visualisation. The programme covers many areas, and you will get a broad understanding of several areas including:
- Data Mining
- Machine Learning
- Data Visualisation
- Data Management
- Big Data Analytics
- Statistics and Probability
How long does it take to study an Msc in Big Data?
The master in Big Data Analytics at Universidad Europea takes 10 months to complete and is made up of 60 ECTS. As part of the programme, you will work on real case studies thanks to our academic model which is based on experiential learning. In addition, you will attend masterclasses given by experts in big data working at some of the most prestigious companies in Spain.