Master in Big Data & Analytics
- Full Time
- 60 ECTS
- Young Professional
- Apr. 2023
- 10 months
This master's degree is taught in SPANISH
Nowadays, information has an immeasurable value. Companies want to capitalize on this fact and, to do so, they are implementing tools related to big data.
This implementation requires qualified professionals who extract and analyse data effectively to gather relevant information for the company. Currently, in view of this need, jobs associated with data analysis, data science and big data are in extremely high demand from companies.
The Master in Big Data & Analytics of EAE Business School Madrid is designed from a technical perspective, immersing you fully in the world of technological tools and elements related to big data (Python, Tableau and the Anaconda environment) and you will lead the way in obtaining maximum profitability and efficiency through data processing.
At the same time, this Master will give you a broad overview of the business world, to complement the technical side.
As a result, through the symbiosis between technology and management, you will be able to forge your own path professionally and develop projects with the aid of technology. Moreover, you will be able to make efficient decisions in any company, in a cutting-edge and innovative field, as well as having great professional prospects.
OFFICIAL MASTER'S DEGREE
On successful completion of your program, you will receive a double qualification: the Master in Big Data & Analytics from EAE Business School and the Master in Big Data & Analytics from the Universidad Internacional de la Empresa (UNIE)
*Official degree recognized by the Ministry of Education of Colombia and SUNEDU.
Enroll now and start enjoying your career boosting plan.*
From the moment of your enrollment we will start working together in the materialization of your professional project through a personalized development plan. Are you going to miss it? Request information and we will explain it to you.
*This service applies only to on-site programs.
Reasons to take this master
- In-house business incubator: You will have the support of EAE Lab, our own business incubator, which provides you with the learning resources, financing and guidance required to transform promising ideas into real business ventures.
- Specialist Software: You will have access to free licences and the opportunity to use Data Warehousing, Analytics and Machine Learning tools technologies, such as Microsoft and Qlik tools, and languages including SQL and Python.
- Business networking: You will have the opportunity to expand your network of professional contacts, building relationships with your classmates and meeting executives from leading companies in the sector, such as Cepsa, Cabify, Everis, Volvo and SAP, to name just a few.
- Expert faculty: Our lecturers are executives and professionals who really know the reality of the business world, and researcher who give you key insights through their studies and publications.
- Theory put into practice: You will tackle real situations and cases in which you have to make decisions, preparing your to succeed in modern professional and executive contexts, developing your critical thinking and problem-solving capacity.
QS Masters Ranking 2021/22
El Economista Ranking 2022
Eduniversal Best Masters Ranking 2021/22
- Foundations of Big Data Technology - 6 ECTS
- Introduction to Big Data. Data sources in Big Data environments.
- Data structures and technologies for selecting useful data. Data quality criteria in Big Data.
- Tracking, processing, indexing and retrieval techniques for structured and unstructured information. Key scraping and crawling strategies.
- Design and exploitation of warehousing systems and Big Data management.
- Warehousing systems for Big Data. Distributed systems. CAP theorem.
- Most widely used data modelling paradigms in the Big Data environment: SQL and NoSQL
- Big Data technological solutions available.
- Security risks and measures in Big Data. Legislation and Big Data
- Intellectual property in relation to the development of Big Data projects. Anonymization, privacy by design and risk analysis (Privacy Impact Assessment).
- Privacy and Big Data. Personal data protection in Spain.
- Ensuring the security of information in Big Data environments. Information leaks.
- Processing Big Data - 6 ECTS
- Introduction to cloud technologies and services in Big Data analysis.
- Development of scalable applications.
- Types of Big Data processing for modelling business logic: batch, streaming, Lambda and Kappa architectures.
- MapReduce processing model.
- High-level tools and languages for Big Data processing.
- Application of cloud solutions for Big Data processing.
- Design of a Big Data solution.
- Cloud Computing - 6 ECTS
- Infrastructure virtualization: Local vs Cloud infrastructure, Infrastructure as a Service (IaaS), Public vs Private Cloud, Platform as a service (PaaS).
- Hybrid clouds. Federated clouds. Cloud standards.
- Containers: Containers vs virtual machines, Standardized containers, Docker Containers.
- Application design in containers.
- Development and application deployment for the cloud: methodologies.
- Deployment automation tools.
- Advanced statistics and data mining - 6 ECTS
- Complex data description and modelling techniques.
- Regression models. Regularization: ridge and lasso.
- Core and spline methods. Loss function.
- Bayesian learning: Bayesian analysis, Bayesian inference, MCMC methods, Bayesian modelling and inference, Bayesian hierarchical models.
- Probabilistic graphical models: Bayesian, Markov chains, Kalman filters, belief networks.
- Modelling probability density functions.
- Time series: introduction, decomposition, moving averages, ARIMA, stationarity, prediction
- Optimization for large volumes of data: linear programming. Quadratic programming.
- Non-linear programming. Heuristics. Metaheuristics.
- Data mining processes.
- KDD process.
- Data pre-processing techniques.
- Classification methods.
- Recommendation systems.
- Cube data analysis and mining models.
- Model evaluation and selection: confusion matrix, metrics, costs. ROC curves.
- Data visualization - 6 ECTS
- Tools for processed data visualization.
- Dynamic data visualization.
- Types of data visualization according to analysis needs.
- Detecting outliers. Structuring and characterizing distributions. Locating anomalies.
- Detecting groupings and correlations.
- Dashboard design using visualization tools.
- Visualization tools.
- Examples of map visualizations.
- Layouts and exporting the result of visualization to a PDF file, Bitmaps and SVG.
- Business Intelligence solutions - 6 ECTS
- Analysis of the business landscape
- Information formats for strategic and tactical decisions.
- Business Intelligence systems.
- Scope of Business Intelligence
- ETL tools and techniques.
- Indicators for business modelling and selecting indicators.
- Business Intelligence system conceptualization and design.
- Methodology for the development and administration of the life cycle of Business Intelligence solutions.
- Architecture and components of Business Intelligence solutions.
- Data warehouse design. Data marts and Data warehousing.
- Data extraction and exploitation processes.
- Reporting with Business Intelligence. Pre-defined reports, ad-hoc reports, queries (Query Tools), OLAP cubes (Online Analytic Processing) and alerts.
- Executive Information Systems (EIS).
- Decision Support Systems (DSS).
- Enterprise Project Management.
- Data Science for strategic decision-making - 6 ECTS
- Statistics for business and Business Intelligence.
- Information as the base for strategic decision-making.
- Analysis of the competitive environment. Competitive intelligence.
- Strategy design and alternative simulation.
- Indicator, report and dashboard design.
- Strategic management control indicators. Generating KPIs (Key Performance Indicators).
- The corporate Dashboard concept.
- Design and implantation of a Dashboard. Strategic maps.
- Financial analysis with Big Data.
- Customer Relationship Management (CRM).
- Business Process Management (BPM).
- Internships in a company - 6 ECTS
- Choose the course that you want to take and which will expand your range of future professional prospects - 10 ECTS
- Master’s Thesis - 6 ECTS
At EAE Business School Madrid, as a school committed to innovation and transformation, we have created an optional specialization program, in an online format, that you can take at the end of your Master.
This educational model gives you the opportunity to acquire new skills and an individualized qualification to set yourself apart in the labour market by enhancing your professional profile.
You will have the option to choose the Minor that best suits your needs.
3 Territories to conquer
1. Technological and business innovation
You will discover the technological and business application of Big Data in depth.
2. Business Intelligence based on Data Analytics
You will develop the technological and management competencies required to lead teams and projects in the field of Data Analytics.
3. Big Data Tools
You will become proficient in the use of key tools for data analysis in the modern business world.
María Victoria Rivas
PhD in Actuarial Sciences.
José Luis Martínez
Co-Founder of Ensaco Energy Efficiency.
Miguel Ángel Bravo
Professor at EAE Business School.
Professor at EAE Business School.
Professional prospects and entry profile
What you study here and now will have an impact on your career tomorrow. Start imagining your future and take a look at some of the professional prospects that await you.
- IT Business Partner
- Big Data Consultant
- Business Intelligence & Data
- Analytics Consultant
- Business Analyst
- Project Management for BI Projects
- Data Architect
- Data Scientist
- Digital Transformation Manager
The profile of students who take the Master in Big Data & Analytics is very diverse, although candidates tend to be graduates of certain qualifications, preferably from the fields of engineering or science, such as those listed below, who will have acquired certain knowledge, attitudes and initial capacities that ensure their suitability for the Master:
- Bachelor Degree in Data Science or equivalent.
- Bachelor Degree in Computer Engineering and equivalent qualifications from the former system of higher education.
- Bachelor Degree in Telecommunications Engineering and equivalent qualifications from the former system of higher education.
Students with other qualifications will be considered for access, such as graduates with a Bachelor Degree, University Diploma or other undergraduate qualification in Mathematics or Physics (or equivalent), and Bachelor Degree or Advanced Diploma in Engineering not related to Information and Communication Technologies. In order to rectify any educational gaps that there may be in each case, based on the students’ previous qualification and professional experience, the students will be required to complete supplementary training courses before starting the Master.