The Master’s in Artificial Intelligence & Big Data is an advanced specialization designed for professionals in the technology field who seek to update and expand their skills, overcoming the limitations of traditional data processing methods. This program equips you with innovative strategies and tools to develop IT architectures and applications capable of managing and processing large volumes of heterogeneous data—often in real time—while addressing the challenges inherent in Big Data. You will delve into the evolution of Business Intelligence toward this new paradigm, exploring the most advanced technological architectures and large-scale data analytics methodologies, including machine learning techniques. You’ll master the fundamentals of relational databases with SQL—from advanced commands to the design of a robust data warehouse—and gain an introduction to NoSQL databases for the efficient management of unstructured data. With this training, you will transform your organization’s ability to optimize operational processes and enhance customer acquisition and retention through predictive and prescriptive analytics, integrating software project management with a strategic vision of Artificial Intelligence applied to real-world, complex business scenarios.
Objectives
• You will design advanced data architectures for managing large volumes of information, integrating big data processing solutions and intelligent systems. • You will implement large-scale data analysis strategies to extract predictive and prescriptive value, optimizing business decision-making. • You will manage the full lifecycle of software projects focused on the implementation of intelligent systems, from conception to impact assessment. • You will make strategic decisions based on in-depth knowledge of data utilization and technological innovation, driving competitiveness. • You will design and optimize data warehouses, mastering relational (SQL) and non-relational (NoSQL) databases for efficient information management. • You will evaluate the impact of advanced analytics solutions on improving operational processes and strategies for attracting and retaining customers. • You will apply data governance and security principles to Big Data architectures, ensuring data integrity and regulatory compliance. • You will integrate machine learning algorithms into artificial intelligence solutions to automate and enhance analytical and predictive capabilities.
Methodology
At Educa PHAROS, we use a highly practical methodology focused on the direct application of knowledge in the student’s professional environment, combining theoretical content with real-world cases, digital tools, and support from a team of specialized instructors.
Program
Data Information
Knowledge and Wisdom
Data Management (I)
Data Management (II)
Corporate Performance Management
Databases
Business Intelligence
Data Warehousing
Big data
Hadoop Spark
Hadoop Ecosystem (I)
Hadoop Ecosystem (II)
Hadoop Ecosystem (III)
Spark Ecosystem
Installation and Configuration of Big Data Architectures
Analytics
Major Algorithms (I)
Major Algorithms (II)
Machine Learning and Deep Learning
Internet of Things
Introduction to SQL
Database Management
Data Types
Standardization
Creating Tables in SQL
Working with Tables
Querying Tables in SQL
Joining Tables in SQL
Combinations of Tables and Views
Other SQL Commands
Functions for Strings and Numeric Functions (I)
Numerical Functions (II)
Date and Time Functions
Other features
Loops, Conditionals, and Triggers in SQL
Introduction to Data Warehousing
Databases in a Data Warehouse. Stage
Databases in a Data Warehouse: ODS (I)
Data in a Data Warehouse. ODS (II)
Databases in a Data Warehouse. DDS
Concepts of NoSQL Databases
Advantages and Disadvantages of NoSQL Databases
Key Features of NoSQL Databases
Document Databases
Column Databases
Key-Value Databases
Graph Database
Introduction to MongoDB
MongoDB Features and Architecture
Data Modeling in MongoDB
Queries and Operations in MongoDB
Scalability and Performance in MongoDB
Apache Cassandra
CouchDB
Redis
Amazon DynamoDB
NeoJS
Data Structure Design
Setting Up the Development Environment
Installing and Configuring MongoDB
Creating and Manipulating Collections in MongoDB
Importing and Exporting Data in MongoDB
Indexes and Query Optimization in MongoDB
Data Aggregation in MongoDB
Transactions in MongoDB
Replication and High Availability in MongoDB
Backups and Recovery in MongoDB
Web and Mobile Apps
Big Data and Data Analysis
Internet of Things (IoT)
Recommendation Systems
Social media and social networks
Introduction to Data Integration
Integration with programming languages (Python, Java, etc.)
Integration with Business Intelligence (BI) Tools
Integration with cloud storage systems
Security Concepts in NoSQL Databases
Authentication and Authorization in MongoDB
Data Encryption in NoSQL Databases
Auditing and Access Control in NoSQL Databases
The State of the Art in Artificial Intelligence
Philosophy of Artificial Intelligence
The Future of Artificial Intelligence
Project Development Processes Using Artificial Intelligence
Data: Your Greatest Asset
Machine Learning
Deep Learning
Transformers
Synthetic Data Generation
Hyperparameters in Artificial Intelligence Models
Linear Regression
Nonlinear Regression and Support Vector Machines (SVM)
Decision Trees and Random Forests
Fuzzy Logic and Gradient Descent
Recommendation Systems
Setting Up the Development Environment: Anaconda, Visual Studio Code, and Python
Input Dataset and Data Processing
TensorHub, TensorFlow, and Keras
Image Processing
Generation of Artificial Intelligence Models
Introduction
Linear, Multiple, and Logistic Regression (I)
Linear, Multiple, and Logistic Regression (II)
Support Vector Machine (SVM)
Decision Trees
KNN (K-Nearest Neighbors)
Naive Bayes
Evaluation of Supervised Models
Sample Exercise
Suggested Exercise
Introduction to Clustering: Purpose and Metrics
K-means clustering
Hierarchical clustering, other techniques, and examples
Principal Component Analysis (PCA)
PCA Example Exercise
Artificial Neural Networks (ANN) (I)
Artificial Neural Networks (ANN) (II)
Artificial Neural Networks (ANN) (III)
Sample Exercise
Suggested Exercise
Introduction
Classification of Machine Learning Algorithms
Examples of Machine Learning
Differences Between Machine Learning and Deep Learning
Types of Machine Learning Algorithms
The Future of Machine Learning
Introduction
Algorithms
Introduction
Collaborative filtering
Clustering
Hybrid Recommendation Systems
Classifiers
Algorithms
Components
Learning
Introduction
The Process of Transitioning from DSS to IDSS
Use Cases
Deep Learning
Deep Learning Environment with Python
Machine Learning and Deep Learning
Neural Networks
Deep networks and shallow networks
Single-layer and multilayer perceptrons
Example of a perceptron
Types of Deep Networks
Data Input and Output
Training a Neural Network
Computer Graphics
Implementation of a Deep Network
The Direct Propagation Algorithm
Multilayer deep neural networks
Introduction
Key Elements for AI Project Management
Characteristics of AI Projects
Introduction to the Main Agile and Ideation Methodologies
Integration of Different Methodologies
Introduction
Phase I: Empathize
Phase II: Define
Phase III: Brainstorming
Phase IV: Prototyping
Lean Startup: Basic Concepts
Lean Startup. Tools
Scrum: An Introduction
Scrum. Roles
Scrum: Ceremonies and Artifacts
Introduction
Developing the Project
Implementing the Project
Some tips for implementing these methodologies
Summary and Conclusions
Financial Sector
Retail sector
Industrial Sector
Agriculture Sector
Healthcare Sector
Logistics and Operations
Marketing
Sales and Customer Service
Finance and Control
People Analytics
The Current Landscape of a Booming Industry
Funding
Notable Startups
The Future of the AI Ecosystem
Starting an AI Company
Ethics. General Notes
Examples of Biases
Global Initiatives
Public Agencies and Regulation
AI and the SDGs
Fourth Industrial Revolution
Digital Transformation in Businesses
Fundamentals and Key Points
Benefits
Enabling Technologies
Big Data
Cloud Computing
Blockchain
Artificial Intelligence
Virtual and Augmented Reality
BIM
Collaborative robots
Additive Manufacturing
Hyperconnectivity
IoT
Manufacturing Execution System (MES)
Process Integration and Efficiency
Use Cases
New methodologies: Agile, Lean Startup, or Design Thinking
Educa PHAROS is a training solution for businesses that centralizes learning, simplifies user management, and allows you to track progress through dashboards and reports.
It is designed for organizations that want to develop internal skills, standardize role-based training, and have traceability and learning metrics.
It includes learning paths by profile, user and permission management, progress reports, certification, and access to masterclasses or live sessions (depending on the plan).
It is based on best practices in security, access control, learning traceability, and implementation support. Customize this text according to your commitments (SLA, compliance, etc.).
Educa PHAROS is a next-generation training model that places a company’s human capital at the forefront. Through a platform that adapts to each company’s corporate identity and offers a total of more than 900 courses, it provides tailored training for each organization. The unlimited flat-rate plan provides each company with the number of courses that best suits its needs, as well as the ability to determine which employees will have access.
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Digital Transformation and Smart Infrastructure 
Spanish 
Credits: 60 ECTS
Leadership and Management 
Spanish
60 ECTS
Architecture and Construction