+ 60 ECTS Credits

Master´s Degree in Artificial Intelligence and Big Data + 60 ECTS Credits

Master´s Degree in Artificial Intelligence and Big Data + 60 ECTS Credits

Do you want to update your skills to manage and analyze massive data, overcoming the limits of traditional processing systems? Specialize in AI, Machine Learning, and Deep Learning for real decisions.

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Master´s Degree in Artificial Intelligence and Big Data + 60 ECTS Credits

Verifiable program data for this master's degree

  • 60 ECTS Credits
  • Faculty of Innovación Tecnológica
  • Internships at real companies
  • STRUCTURALIA qualification

What will you achieve by completing this master's degree?

AI and Big Data basics

Build a solid foundation in artificial intelligence and the technologies behind large-scale data handling.

Machine Learning

Implement supervised and unsupervised Machine Learning algorithms to extract value and patterns from data.

Deep Learning models

Design and apply advanced Deep Learning solutions to tackle complex business problems and improve decisions.

Scalable data ecosystems

Integrate AI in advanced technological ecosystems, enabling scalable architectures and actionable insights.

Course curriculum

9 modules covering everything from AI and big data foundations to scalable Deep Learning models and data ecosystems. Click on each module to see the full content.

01

Module 1. Artificial Intelligence

Unit 1. Introduction To Artificial Intelligence

  • State of the art of artificial intelligence
  • Philosophy of artificial intelligence
  • Future of artificial intelligence
  • Project development process with artificial intelligence
  • Data, your greatest asset

Unit 2. Types Of Artificial Intelligence

  • Machine learning
  • Deep learning
  • Transformers
  • Generation of synthetic data
  • Hyperparameters in artificial intelligence models

Unit 3. Introduction To Machine Learning Algorithms

  • Linear regression
  • Non-linear regression and support vector machines (SVM)
  • Decision trees, random forests
  • Fuse logic and gradient down
  • Recommendation systems

Unit 4. Turnkey Project

  • Preparation of the working environment: Anaconda, Visual Studio Code and Python
  • Input dataset and data preprocessing
  • TensorHub, TensorFlow and Keras
  • Image processing
  • Generation of artificial intelligence models
02

Module 2. Data Mining, Machine Learning And Deep Learning

Unit 1. Supervised Learning (i)

  • Introduction
  • Simple, multiple and logistic linear regression (I)
  • Simple, multiple and logistic linear regression (II)
  • Support vector machines (SVM)
  • Decision trees

Unit 2. Supervised Learning (ii)

  • KNN (k-nearest neighbours)
  • Naive Bayes
  • Evaluation of supervised models
  • Example exercise
  • Proposed exercise

Unit 3. Unsupervised Learning

  • Introduction to clustering. purconsider and metrics
  • K-means clustering
  • Hierarchical clustering, other techniques and examples
  • Principal component analysis (PCA)
  • PCA example exercise

Unit 4. Deep Learning

  • Artificial Neural Networks (ANN) (I)
  • Artificial Neural Networks (ANN) (II)
  • Artificial Neural Networks (ANN) (III)
  • Example exercise
  • Proposed exercise
03

Module 3. Advanced Deep Learning

Unit 1. Supervised Deep Learning (i)

  • Introduction
  • Review. Artificial neural network (ANN)
  • Review. ANN exercises
  • Convolutional Neural Networks (CNN)
  • CNN Exercises

Unit 2. Supervised Deep Learning (ii)

  • Natural language processing (I)
  • Recurrent neural networks (RNN) (I)
  • Recurrent neural networks (RNN) (II)
  • Natural language processing (II)
  • RNN Exercise

Unit 3. Unsupervised Deep Learning (i)

  • Boltzmann Machines (BM)
  • Restricted Boltzmann Machines (RBM)
  • Recommender systems
  • Recommender systems. metrics
  • RBM exercise

Unit 4. Unsupervised Deep Learning (ii)

  • Self-organising maps (SOM)
  • SOM exercises
  • Autoencoders (AE)
  • AE exercises
  • Proposed exercise
04

Module 4. Technology Ecosystems

Unit 1. Introduction To Technology Ecosystems

  • The fourth industrial revolution
  • Digital transformation in companies
  • Fundaments and key points
  • Benefits
  • Enabling technologies

Unit 2. Enabling Technologies (i)

  • Big data
  • Cloud computing
  • Blockchain
  • Artificial intelligence
  • Augmented and virtual reality

Unit 3. Enabling Technologies (ii)

  • BIM
  • Collaborative robots
  • Additive manufacturing
  • Hyperconnectivity
  • IoT

Unit 4 Enabling Technologies (iii)

  • Manufacturing execution system (MES)
  • Process integration and efficiency
  • Use cases
  • New methodologies. agile, lean startup or design thinking
  • Business change management
05

Module 5. Ideation Methodologies And Techniques And Ai Project Management

Unit 1. Introduction

  • Introduction
  • Key elements in AI project management
  • AI project characteristics
  • Introduction to the main agile and ideation methodologies
  • Methodology integration

Unit 2. Design Thinking

  • Introduction
  • Phase I. Empathize
  • Phase II. Define
  • Phase III. Devise
  • Phase IV. Prototype

Unit 3. Lean Start-up And Scrum

  • Lean start-up. Basic concepts
  • Lean start-up. Tools
  • Scrum. Introduction
  • Scrum. Roles
  • Scrum. Ceremonies and artifacts

Unit 4. Application To Ai Projects

  • Introduction
  • Project ideation
  • Project implementation
  • Advise on implementing methodologies
  • Summary and conclusions
06

Module 6. The Impact Of Ai On Business

Unit 1. Ai Applied To Different Sectors

  • Financial sector
  • Retail sector
  • Industrial sector
  • Agricultural sector
  • Health sector

Unit 2. Ai Applied To Different Business Areas

  • Logistics and operations
  • Marketing
  • Sales and customer service
  • Finance and control
  • People analytics

Unit 3. Ai And Entrepreneurship

  • Current scenario of a booming sector
  • Financing
  • Featured start-ups
  • Future of the AI ecosystem
  • Starting an AI company

Unit 4. Ethics. Business And Society

  • Ethics. General remarks
  • Bias examples
  • Global initiatives
  • Public Institutions and regulations
  • AI in the SDGs
07

Module 7. Introduction To Big Data

Unit 1. Data In Companies

  • Data information knoledge wisdom
  • Data management (i)
  • Data management (ii)
  • Corporate performance management
  • Databases

Unit 2. From Business Intelligence To Big Data

  • Business intelligence
  • Datawarehousing
  • Big data
  • Hadoop
  • Spark

Unit 3. Big Data Technology Architectures

  • Hadoop ecosystem (I)
  • Hadoop ecosystem (II)
  • Hadoop ecosystem (III)
  • Spark ecosystem
  • Installation and configuration of big data architectures

Unit 4. Big Data Analytics

  • Analytics
  • Main algorithms (I)
  • Main algorithms (II)
  • Machine learning and deep learning
  • Internet of things
08

Module 8. Relational Databases. Sql. Data Warehouse

Unit 1. First Steps In Sql

  • Introduction to SQL
  • Database manipulation
  • Data types
  • Normalization
  • Creating tables in SQL

Unit 2. Sql Commands

  • Table manipulation
  • SQL table query
  • Table joining in SQL
  • Table combinations and views
  • Other SQL commands

Unit 3. Sql Functions

  • String functions and numeric functions (I)
  • Numeric function (II)
  • Date and time functions
  • Other functions
  • Loops, conditionals and triggers in SQL

Unit 4. Data Warehouse Design

  • Data warehousing introduction
  • Databases in a data warehouse. Stage
  • Databases in a data warehouse. ODS (I)
  • Databases in a data warehouse. ODS (II)
  • Databases in a data warehouse. DDS
09

Module 9. Nosql Databases

Unit 1. Introduction To Nosql Databases

  • Introduction
  • Polyglot persistence
  • ACID model
  • New trends
  • Comparison between SQL and NOSQL

Unit 2. Nosql Data Models

  • Data models
  • Aggregation models
  • Key-value aggregation models
  • Document-oriented data models
  • Column-oriented aggregation models
  • Graph data model

Unit 3. Distributed Databases

  • Distributed databases
  • Strategies for the design of distributed DBS
  • NOSQL database design
  • Hadoop distributed file system (HDFS)

Unit 4. Nosql Databases Examples

  • Example of a NOSQL aggregation database
  • Riak. Example of a key-value database
  • MongoDB. Example of a document database
  • Neo4J. Example of a graph NOSQL database
  • HBASE. Example of a columnar database

Teaching faculty

Teaching team with professional experience in Master´s Degree in Artificial Intelligence and Big Data + 60 ECTS Credits.

AR

Adrián Rodríguez Porres

Faculty of Gestión de Proyectos en Arquitectura

metodología BIM

VG

Vidal Gascón Culebras

Inteligencia Artificial aplicada

JM

Julia Molina Virués

Faculty of Gestión de Proyectos en Arquitectura

arquitectura sostenible

Charo Rey Zabalza

Charo Rey Zabalza

Faculty of Medioambiente y Sostenibilidad

economía circular

ÁS

Ángel Sanz Bernabé

control de calidad

Isabella Sánchez Bermúdez

Isabella Sánchez Bermúdez

Faculty of Dirección de Proyectos e Innovación Tecnológica

comunicación estratégica

Rogelio Delgado Mingorance

Rogelio Delgado Mingorance

Faculty of Ingeniería Industrial

gestión y dirección de proyectos

Miguel Ángel Aparicio Jiménez

Miguel Ángel Aparicio Jiménez

What jobs could you get?

With this master's degree you could apply for roles such as:

  • Data Strategy Director
  • AI Architecture Manager
  • Senior Predictive Analytics Consultant
  • Data and Analytics Team Lead
  • Machine Learning Solutions Manager
  • Digital Transformation Project Lead

Methodology

Our teaching methodology

Equipo diverso de ingeniería trabajando con portátiles en un laboratorio maker con impresoras 3D
Learn whenever you want

Progress at your own pace, from wherever you are,
with close support.

Realistic

The EDUCA LXP methodology avoids excessively theoretical knowledge and inefficient practical methods. The combination of constantly updated content with personalized guidance throughout the learning process makes EDUCA LXP a unique methodology.

Student First

The EDUCA LXP methodology and EDUCA EDTECH Group's training place the student at the center of the learning experience, drawing on their feedback. Their feedback drives our continuous improvement.

Artificial Intelligence

Personalized learning would not be possible without a precise combination of academic experience, technology research, and Artificial Intelligence. That is why we rely on in-house AI tools, tailored to each school in the group.

Active industry professionals

Our teaching team, besides being specialists in their field, has specific training in the technology tools that make up the EDUCA EDTECH ecosystem.

Rankings and recognitions

Assessed by independent online higher-education organizations.

Organization of American States (OAS)

Since 2010, Structuralia has run a scholarship program for master's degrees in Spanish and English together with the Organization of American States (OAS), aimed at outstanding professionals from the Americas as part of the Partnerships for Education and Training Program (PAEC), which has already benefited more than 3,500 students from the region.

APICE

Structuralia and the Pan-American Association of Educational Credit Institutions (APICE) offer a specialized training scholarship program for professionals from Latin America and the Caribbean in master's degrees specialized in the STEM sector.

Educa Edtech

The Educa Edtech Foundation was created to foster personal and professional growth while championing knowledge transfer as a way to change the world, overseeing the rollout of solutions and granting aid to those who deserve it.

Financial Magazine

This outlet's 2025 Ranking has included up to 10 Structuralia master's programs across several categories, among them: Data Science, Big Data, Artificial Intelligence, Urban Planning, Energy and the Environment.

Mundo Posgrado

In the latest edition of its regular annual rankings, Structuralia's master's programs made the Top 10 best master's degrees in Spain in the Renewable Energy and Integrated Management Systems categories.

Escudo Digital

The Master's program in Cybersecurity and Information Risk is included in the Top 25 best cybersecurity training programs according to "Escudo Digital".

Our numbers

92%of our students would recommend us
30+countries with active students
60%of our faculty are active industry professionals
15 yearstraining specialists in engineering and architecture

STRUCTURALIA Scholarships

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