+ 60 ECTS Credits

Master´s Degree in Artificial Intelligence. Model Management and Implementation + 60 ECTS Credits

Master´s Degree in Artificial Intelligence. Model Management and Implementation + 60 ECTS Credits

Do you want to lead AI initiatives and translate models into practical results for your organization? Learn to design, manage, and implement AI models end to end.

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Master´s Degree in Artificial Intelligence. Model Management and Implementation + 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 foundations

Understand the fundamentals and typologies of Artificial Intelligence, assessing capabilities and limits in business contexts.

Model architecture

Design AI system architectures and the project life cycle, from conception through deployment of intelligent solutions.

Data preparation

Master self-service data preparation with Excel, Talend, and Trifacta to ensure data quality and availability.

Data mining impact

Use Data Mining techniques and machine learning to extract trends and support informed technical and business decisions.

Course curriculum

9 modules covering everything from AI foundations and model architectures to self-service data prep, Data Mining, and deployment. Click to view the full content.

01

Module 1. Artificial Intelligence

Didactic Unit 1. Introduction To Artificial Intelligence

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

Didactic Unit 2. Types Of Artificial Intelligence

  • Machine learning
  • Deep learning
  • Transformers
  • Synthetic data generation
  • Hyperparameters in artificial intelligence models

Didactic Unit 3. Introduction To Machine Learning Algorithms

  • Linear regression
  • Non-linear Regression and Support Vector Machine (SVM)
  • Decision trees and random forests
  • Fuzzy logic and gradient descent
  • Recommender systems

Didactic Unit 4. Turnkey Project With Artificial Intelligence

  • Preparing the working environment: Anaconda, Visual Studio Code and Python
  • Data input and processing datasets
  • TensorHub, TensorFlow and Keras
  • Image processing
  • Artificial intelligence modelling
02

Module 2. Preparation Of Self-service Data. Excel, Talend And Trifacta

Didactic Unit 1. Data Preparation

  • Introduction
  • Data literacy
  • Working with data
  • Solutions and techniques for data processing
  • Data quality management

Didactic Unit 2. Data Preparation With Excel

  • Working with data in Excel
  • Data set (DATASET)
  • Data Cleasing with Excel
  • Data Wrangling with Excel
  • Data Blending in Excel

Didactic Unit 3. Data Preparation With Talend

  • Talend Data Preparation Desktop Installation
  • Working with data in Talend
  • Data Cleasing with Talend
  • Data Wrangling with Talend
  • Data Blending with Talend

Didactic Unit 4. Data Preparation With Dataprep By Trifacta

  • Registration in dataprep by Trifacta
  • Working with data with Dataprep by Trifacta
  • Data Cleasing with Trifacta
  • Data Wrangling with Dataprep by Trifacta
  • Data Blending with Dataprep by Trifacta
03

Module 3. Data Mining, Machine Learning And Deep Learning (big Data)

Didactic Unit 1. Supervised Learning (i)

  • Introduction
  • Linear, multiple and logistic regression (I)
  • Linear, multiple and logistic regression (II)
  • Support Vector Machine (SVM)
  • Decision trees

Didactic Unit 2. Supervised Learning (ii)

  • KNN (K-Nearest Neighbors)
  • Naive bayes
  • Evaluation of supervised models
  • Sample exercise
  • Proposed exercise

Didactic Unit 3. Unsupervised Learning

  • Introduction to clustering: purpose and metrics
  • K-means clustering
  • Hierarchical clustering, other techniques and examples
  • Principal Component Analysis (PCA)
  • Sample PCA exercise

Didactic Unit 4. Deep Learning

  • Artificial Neural Networks (ANN) (I)
  • Artificial Neural Networks (ANN) (II)
  • Artificial Neural Networks (ANN) (III)
  • Sample exercise
  • Proposed exercise
04

Module 4. Advanced Deep Learning

Didactic Unit 1. Supervised Deep Learning (i)

  • Introduction and Review of Artificial Neural Networks (ANN)
  • Convolutional Neural Networks (CNN): Introduction and Use Cases
  • CNN: Intuition
  • CNN: Mathematical description
  • CNN: Programming example with Python and TensorFlow
  • Exercise: Artificial vision with CNN

Didactic Unit 2. Supervised Deep Learning (ii)

  • Time Series Review
  • Recurrent Neural Networks (RNN): Introduction and Use Cases
  • RNN: Intuition
  • RNN: Mathematical description
  • RNN: Programming Example with Python and TensorFlow
  • Exercise: Time Series with RNN

Didactic Unit 3. Unsupervised Deep Learning (i)

  • Review of Recommender Systems
  • Deep Bolztmann Machines (DBM): Introduction and use cases [Video].
  • DBM: Intuition
  • DBM: Mathematical description
  • DBM: Programming Example with Python and TensorFlow
  • Exercise: Recommendation System with DBM

Didactic Unit 4. Unsupervised Deep Learning (ii)

  • Anomaly detection
  • Self-Organising Maps (SOM): Introduction and intuition
  • SOM: Mathematical description
  • AutoEncoders (AE): Introduction and intuition
  • AE: Mathematical description
  • Exercise: Anomaly detection with SOM and AE
05

Module 5. Power Bi. Data Visualisation Tool For Decision Making.

Didactic Unit 1. Getting Started With Power Bi

  • Introduction to Power BI
  • Different types of Power BI: is it really free?
  • Let's dive in: simple first report
  • Power Query: data source
  • Data transformation

Didactic Unit 2. Data Modelling And Dax

  • Data modelling
  • Starting with DAX (I)
  • Getting started with DAX (II)
  • Mastering DAX (I)
  • Mastering DAX (II)

Didactic Unit 3. Data Visualisation

  • Table and matrix
  • Trends
  • How to filter your data properly
  • Scoreboards
  • Obtaining details

Unit Didactic Unit 4. Taking Power Bi To The Next Level

  • Understanding Power BI Service
  • Sharing content in Power BI Service
  • Comparing Power BI Service and Power Report Service
  • Integrating Python and R in Power BI Desktop
  • Introducing Bravo for Power BI Desktop
06

Module 6. Practical Applications Of Machine Learning, Deep Learning And Data Science

Didactic Unit 1. Machine Learning. Implementation Of Algorithms In Python And Machine Learning Tools And/or Libraries

  • - Linear Regression.
  • - Logistic Regression.
  • - Neural Networks.
  • - Clustering.
  • Principal Component Analysis (PCA).

Didactic Unit 2. Deep Learning. Implementation Of Algorithms In Python And Deep Learning Tools And/or Libraries

  • - Deep neural networks.
  • - Optimisation of algorithms.
  • - Convolutional neural networks.
  • - Recurrent neural networks.
  • - NPL. Natural language processing.

Didactic Unit 3. Data Science. Data Analysis And Visualisation Using The Powerbi Tool

  • - Creation of tables and reports.
  • - Data transformation and filtering.
  • - Data visualisation.
  • - Calculation. Relationships between data tables, metrics and indicators.
  • - Dynamic and interactive control panel.

Didactic Unit 4. Development Of Real Applications

  • - Application. Classification of objects in images.
  • - Application. Detection of objects in images.
  • - Application. Facial recognition.
  • - Application. Word detection for voice assistants.
  • - Application. Business Intelligence.
07

Module 7. Technology Ecosystems. Introduction To New Disruptive Technologies

Didactic Unit 1. Introduction To The Technology Ecosystem

  • Fourth industrial revolution
  • Digital transformation in business
  • Fundamentals and key points
  • Benefits
  • Enabling technologies

Didactic Unit 2. Enabling Technologies (i)

  • Big Data
  • Cloud Computing
  • Cybersecurity
  • Artificial intelligence
  • Virtual and augmented reality

Unit Didactic Unit 3. Enabling Technologies (ii)

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

Didactic Unit 4. Enabling Technologies (iii)

  • Manufacturing Execution System (MES)
  • Process integration and efficiency
  • Use cases
  • New methodologies: Agile, Lean Startup or Design Thinking.
  • Change management in the company
08

Module 8. Methodologies For The Ideation And Management Of Artificial Intelligence Projects

Didactic Unit 1. Introduction

  • Introduction
  • Key elements for AI project management
  • Characteristics of AI projects
  • Introduction to the main agile and ideation methodologies
  • Integration of different methodologies

Didactic Unit 2. Design Thinking

  • Introduction
  • Phase I: Empathising
  • Phase II: Define
  • Phase III: Devising
  • Phase IV: Prototyping

Didactic Unit 3. Lean Start-up And Scrum

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

Didactic Unit 4. Application To Ia Projects

  • Introduction
  • Devising the project
  • Implementing the project
  • Some tips on how to implement the methodologies
  • Summary and conclusions
09

Module 9. The Impact Of Artificial Intelligence In Business

Didactic Unit 1. Artificial Intelligence Applied To Different Sectors

  • Finance and Insurance
  • Retail
  • Industry
  • Agriculture
  • Health

Didactic Unit 2. Applications In The Different Areas Of a Company

  • Logistics and operations
  • Marketing
  • Sales and Customer Service
  • Finance and Control
  • People Analytics

Didactic Unit 3. Undertaking In Ia

  • Current scenario of a booming sector
  • Financing and funding
  • Featured Startups
  • Future of the ecosystem
  • Starting an AI company

Didactic Unit 4. Ethics. Company And Society

  • Ethics. General notes.
  • Examples of biases.
  • Global initiatives.
  • Public bodies and regulation.
  • AI in the Sustainable Development Goals

Teaching faculty

Teaching team with professional experience in Master´s Degree in Artificial Intelligence. Model Management and Implementation + 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:

  • AI project lead
  • Senior AI data strategy consultant
  • Responsible for AI Operations
  • AI solutions implementation specialist
  • Data Mining team coordinator
  • AI deployment program manager

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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