The contents of this qualification are not classical subjects. This program specializes computer scientists in the application of the technologies of the future"

The Professional master’s degree in Research and Innovation in Information and Communication Technologies develops a highly specialized vision that will allow students to focus on advanced technological projects using the most innovative technologies in an appropriate way, generating a differential added value through their correct use and application.

The direct application of the knowledge acquired on Smart Cities, Blockchain, IoT, Digital twins in AI (artificial intelligence) in real projects is an added professional value that very few professionals specialized in Information and Communication Technologies can offer.

Professionals who successfully complete this program will have a global vision of the application of the different technologies involved in global digitalization and will have the ability to apply them, having been trained by accredited professionals who use them in their daily work.

Additionally, the student has the best study methodology 100% online, which eliminates the need to attend classes in person or have to comply with a predetermined schedule. In this way, in just 12 months, you will deepen your knowledge of the scope of application of each technology, understanding the competitive advantages they provide, so you will be positioned at the technological forefront and will be able to lead ambitious projects in the present and in the future.

Addresses the 6 most innovative technologies of today from a practical and innovative business perspective"

This Professional master’s degree in Research and Innovation in Information and Communication Technologies contains the most complete and up-to-date program on the market. The most important features include:

• The development of case studies presented by experts in Research and Innovation in Information and Communication Technology
• The graphic, schematic, and practical contents with which they are created, provide practical information on the disciplines that are essential for professional practice
• Practical exercises where self-assessment can be used to improve learning
• Its special emphasis on innovative methodologies
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

Contemplates the newest technologies and areas of study and the most disruptive and surprising practical applications that you can find in the field of information and communication"

The program’s teaching team includes professionals from the sector who contribute their work experience to this educational program, as well as renowned specialists from leading societies and prestigious universities.

Its multimedia content, developed with the latest educational technology, will allow professionals to learn in a contextual and situated learning environment, i.e., a simulated environment that will provide immersive education programmed to prepare in real situations.

The design of this program focuses on Problem-Based Learning, by means of which professionals must try to solve the different professional practice situations that are presented to them throughout the academic year. For this purpose, the student will be assisted by an innovative interactive video system created by renowned and experienced experts.

It addresses two of the fields with the highest development forecasts in the world of Artificial Intelligence, NLP and Computer Vision"

It deepens the Digital Twins, a highly competitive field with a high demand and for which there is a very high lack of qualified profiles"

If there is something that differentiates this program from any other on the market, it is that it addresses the six most innovative technologies of today: cloud computing; internet of things; digital twins; Smart Cities; Blockchain and artificial intelligence. In addition, it approaches them from a practical and innovative business perspective, thus giving an eminently practical approach to the contents. All this is oriented to experienced professionals with a great interest in the subjects of study, so the professional level is high, an important differential element of the qualification.

There is no other program on the market in the study of ICT that addresses so many disruptive technologies from a practical perspective and enables you to apply them directly at the end of your studies"

Module 1. Communication Innovation with Cloud Computing

1.1. Cloud Computing The State of Art in the Online Revolution

1.1.1. Cloud Computing
1.1.2. Suppliers
1.1.3. Microsoft Azure

1.2. Interaction Methods. Configuration and Management of the Tools. Cloud Services

1.2.1. Portal
1.2.2. App
1.2.3. Powershell
1.2.4. Azure CLI
1.2.5. Azure REST API
1.2.6. ARM Templates

1.3. Computing. Services Available on the Cloud

1.3.1. Virtual Machine
1.3.2. Containers
1.3.3. AKS / Kubernetes
1.3.4. Function (Serverless)

1.4. Computing Services Available on the Cloud. Web Apps

1.4.1. Web
1.4.2. Web Apps
1.4.3. Rest API
1.4.4. API Management

1.5. Cloud Storage Systems. Security and Communications

1.5.1. Storage
1.5.2. Data Lake
1.5.3. Data Factory
1.5.4. Data Services
1.5.5. Backup Copies

1.6. OnCloud Databases. Structured OnCloud Information. Unlimited Scalability

1.6.1. Azure SQL
1.6.2. PostgresSQL / MySQL.
1.6.3. Azure Cosmos DB.
1.6.4. Redis

1.7. IoT. OnCloud Device Data Management and Storage

1.7.1. Stram Nalytics
1.7.2. Digital Twins

1.8. OnCloud Artificial Intelligence

1.8.1. Machine Learning
1.8.2. Cognitive Services
1.8.3. Quantum Computing

1.9. OnCloud Computing. Advanced Features

1.9.1. Security/Safety
1.9.2. Monitoring. DataDog
1.9.3. Application Insights

1.10. OnCloud Computing Applications

1.10.1. LOB Scenario: Customer Relationship Management (CRM)
1.10.2. IoTScenario: Smart City
1.10.3. AI Scenario: Chat Bot

Module 2. IoT. Service Applications and I 4.0. (Industry 4.0)

2.1. IoT. Internet of Things

2.1.1. IoT
2.1.2. Internet 0 & IoT
2.1.3. Privacy and Object Control

2.2. Applications of IoT

2.2.1. IoT Applications Consumption
2.2.2. EIoT & IIoT
2.2.3. IoT Administration

2.3. IoT & IIoT. Differences

2.3.1. IIoT. IoT Differences
2.3.2. IIot. Application
2.3.3. Industries

2.4. Industry 4.0, Big Data & Business Analytics

2.4.1. Industry 4.0. Big Data and Business Intelligence.
2.4.2. Industry 4.0. Big Data & Business Intelligence Analytics. Contextualization
2.4.3. Decisions and CRISP_DM Methodology

2.5. Predictive Maintenance

2.5.1. Predictive Maintenance. Application
2.5.2. Predictive Maintenance. Model Development Approach

2.6. IoT from Solutions Implementation Tool I

2.6.1. Ethos Micro NPU
2.6.2. End-to-End Products
2.6.3. Eclipse IoT Application Examples

2.7. IoT from Solutions Implementation Tool Advanced II

2.7.1. Architecture
2.7.2. End-to-End
2.7.3. Environment Analytics

2.8. Composition IIoT Architecture

2.8.1. Sensors and Actuators
2.8.2. Internet Ports and Data Acquisition Systems
2.8.3. Data Pre-Processing
2.8.4. Cloud Data Analysis and Modeling

2.9. End-to-End Open and Modular Architecture

2.9.1. End-to-End Open and Modular Architecture
2.9.2. Modular Architecture. Key Components
2.9.3. Modular Architecture. Benefits

2.10. Machine Learning at the Core and Edge

2.10.1. PoC
2.10.2. Data Pipeline
2.10.3. Edge to Core & Demo

Module 3. Digital Twins. Innovative Solutions

3.1. Digital Twins

3.1.1. Digital Twins. Basic Concepts
3.1.2. Digital Twins Technological Evolution
3.1.3. Digital Twins. Typology

3.2. Digital Twins. Applicable Technologies

3.2.1. Digital Twins Platforms
3.2.2. Digital Twins Interfaces
3.2.3. Digital Twins. Typology

3.3. Digital Twins. Sectors and Examples of Use

3.3.1. Digital Twins: Techniques and Uses
3.3.2. Industries
3.3.3. Architecture and Cities

3.4. Industry 4.0. Digital Twin Applications

3.4.1. Industry 4.0
3.4.2. Environment
3.4.3. Digital Twin Applications in Industry 4.0

3.5. Smart Cities based on Digital Twins

3.5.1. Models
3.5.2. Categories
3.5.3. Future of Smart Cities based on Digital Twins

3.6. IoT Applied to Digital Twins

3.6.1. IoT. Link with Digital Twins
3.6.2. IoT. Relationship with Digital Twins
3.6.3. IoT. Problems and Possible Solutions

3.7. Digital Twin Environment

3.7.1. Companies
3.7.2. Organization
3.7.3. Implications

3.8. Digital Twin Market

3.8.1. Platforms
3.8.2. Suppliers
3.8.3. Associated Services

3.9. Future of Digital Twins

3.9.1. Immersiveness
3.9.2. Augmented Reality
3.9.3. Biointerfaces

3.10. Digital Twins. Present and Future Results

3.10.1. Platform
3.10.2. Technologies
3.10.3. Sectors

Module 4. Smart Cities as Innovation Tools

4.1. From Cities to Smart Cities

4.1.1. From Cities to Smart Cities
4.1.2. Cities Over Time and Cultures in Cities
4.1.3. Evolution of City Models

4.2. Technologies

4.2.1. Technological Application Platforms
4.2.2. Services/Citizen Interfaces
4.2.3. Technological Typologies

4.3. City as a Complex System

4.3.1. Components of a City
4.3.2. Interactions between Components
4.3.3. Applications: Products and Services in the City

4.4. Intelligent Safety Management

4.4.1. Current State
4.4.2. Technological Management Environments in the City
4.4.3. Future: Smart Cities in the Future

4.5. Intelligent Cleaning Management

4.5.1. Application Models in Intelligent Cleaning Services
4.5.2. Systems: Application of Intelligent Cleaning Services
4.5.3. Future of Intelligent Cleaning Services

4.6. Intelligent Traffic Management

4.6.1. Traffic Evolution: Complexity and Factors Hindering Traffic Management
4.6.2. Problems
4.6.3. E-Mobility
4.6.4. Solutions

4.7. Sustainable City

4.7.1. Energy
4.7.2. The Water Cycle
4.7.3. Management Platform

4.8. Intelligent Leisure Management

4.8.1. Business Models
4.8.2. Urban Leisure Evolution
4.8.3. Associated Services

4.9. Large Social Event Management

4.9.1. Movement
4.9.2. Capacities
4.9.3. Health

4.10. Conclusions on the Present and Future of Smart Cities

4.10.1. Technology Platforms and Problems
4.10.2. Technologies, Integration in Heterogeneous Environments
4.10.3. Practical Applications in Different City Models

Module 5. R&D in Complex Software Systems. Blockchain. Public and Private Nodes

5.1. Blockchain and Distributed Data

5.1.1. Information Communications. New Paradigm
5.1.2. Privacy and Transparency
5.1.3. Information Exchange. New Models

5.2. Blockchain

5.2.1. Blockchain
5.2.2. Blockchain. Technological Base
5.2.3. Blockchain. Components and Elements

5.3. Blockchain. Public Nodes

5.3.1. Blockchain. Public Nodes
5.3.2. Working Algorithms in Public Nodes

5.3.2.1. Proof of Work
5.3.2.2. Proof of Stake
5.3.2.3. Proof of Authority

5.3.3. Use Cases and Application

5.3.3.1. Smart Contracts
5.3.3.2. Dapps

5.4. Blockchain. Private Nodes

5.4.1. Blockchain. Private Nodes
5.4.2. Working Algorithms in Private Nodes

5.4.2.1. Proof of Work
5.4.2.2. Proof of Stake
5.4.2.3. Proof of Authority

5.4.3. Use Cases and Application

5.4.3.1. Crypto Economy
5.4.3.2. Game Theory
5.4.3.3. Market Modeling

5.5. Blockchain. Work Frameworks

5.5.1. Blockchain. Work Frameworks
5.5.2. Types

5.5.2.1. Ethereum
5.5.2.2. Hyperledger Fabric

5.5.3. Application Examples (Ethereum)

5.5.3.1. C#
5.5.3.2. Go

5.6. Blockchain in Finance

5.6.1. The Impact of Blockchain on the Financial World
5.6.2. Advanced Technologies
5.6.3. Use Cases and Application

5.6.3.1. Information Assurance
5.6.3.2. Follow-Up and Monitoring
5.6.3.3. Certified Transmissions
5.6.3.4. Examples within the Financial Sector

5.7. Blockchain in the Industrial Environment

5.7.1. Blockchain and Logistics
5.7.2. Advanced Technologies
5.7.3. Use Cases and Application

5.7.3.1. Smart Contracts between Suppliers and Customers
5.7.3.2. Support in Automation Processes
5.7.3.3. Real-Time Product Traceability
5.7.3.4. Examples within the Industrial Sector

5.8. Blockchain. Transaction Tokenization

5.8.1. Tokenizing the World
5.8.2. Smart Contracts Platforms (Smart Contracts)

5.8.2.1. Bitcoin
5.8.2.2. Ethereum
5.8.2.3. Other Emerging Platforms

5.8.3. Communication: The Oracle Problem
5.8.4. Uniqueness: NFTs
5.8.5. Tokenization: STOs

5.9. Blockchain. Examples of Use

5.9.1. Use Case Description
5.9.2. Practical Implementation (C#/Go)

5.10. Distributed Data. Blockchain Applications, Present and Future

5.10.1. Distributed Data. Present and Future Applications of Blockchain
5.10.2. The Future of Communication
5.10.3. Next Steps

Module 6. Data Operations in Blockchain. Innovation in Information Management

6.1. Information Management

6.1.1. The Information Society
6.1.2. Management Applied to Knowledge

6.2. Blockchain in Information Management

6.2.1. Blockchain in Information Management

6.2.1.1. Secure Data
6.2.1.2. Data Quality
6.2.1.3. Traceability of Information
6.2.1.4. Other Additional Benefits

6.2.2. Additional considerations

6.3. Secure Data

6.3.1. Secure Data
6.3.2. Security and privacy
6.3.3. Use Cases and Application

6.4. Data Quality

6.4.1. Quality Data
6.4.2. Reliability and Consensus
6.4.3. Use Cases and Application

6.5. Traceability of Information

6.5.1. Traceability Data
6.5.2. Blockchain in Data Traceability
6.5.3. Use Cases and Application

6.6. Analytics of Information

6.6.1. Big Data
6.6.2. Blockchain and Big Data
6.6.3. Real-Time Data Accessibility
6.6.4. Use Cases and Application.

6.7. Application of BC (I). Information Security

6.7.1. The Information Society
6.7.2. Use Cases
6.7.3. Practical implementation

6.8. Application of BC (II). Information Quality

6.8.1. The Information Society
6.8.2. Cases to Use
6.8.3. Practical implementation

6.9. Application of BC (III). Traceability of Information

6.9.1. The Information Society
6.9.2. Use Cases
6.9.3. Practical implementation

6.10. Blockchain. Practical Applications

6.10.1. Blockchain in Practice

6.10.1.1. Data Centers
6.10.1.2. Sectorial
6.10.1.3. Multisectoral
6.10.1.4. Geographic

Module 7. R&D and AI. NLP/NLU. Embeddings and Transformers

7.1. Natural Language Processing (NLP)

7.1.1. Natural Language Processing. Uses of NLP
7.1.2. Natural Language Processing (NLP). Libraries
7.1.3. Stoppers in NLP Application

7.2. Natural Language Understanding/Natural Language Generation. (NLU/NLG)

7.2.1. NLG. I.A. NLP/NLU. Embeddings and Transformers
7.2.2. NLU/NLG. Uses
7.2.3. NLP/NLG. Differences

7.3. Word Embeddings

7.3.1. Word Embeddings
7.3.2. Word Embeddings Uses
7.3.3. Word2vec. Libraries

7.4. Embeddings. Practical Application

7.4.1. Word2vec Code
7.4.2. Word2vec. Real Cases
7.4.3. Corpus for Word2vec Use. Examples

7.5. Transformers

7.5.1. Transformers
7.5.2. Models Created with Transformers
7.5.3. Pros and Cons of Transformers

7.6. Sentiment Analysis

7.6.1. Sentiment Analysis
7.6.2. Practical Application of Sentiment Analysis
7.6.3. Uses of Sentiment Analysis

7.7. GPT Open AI

7.7.1. GPT Open AI
7.7.2. GPT 2. Free Disposal Model
7.7.3. GPT 3. Payment Model

7.8. Hugging Face Community

7.8.1. Hugging Face Community
7.8.2. Hugging Face Community Possibilities
7.8.3. Hugging Face Community Examples

7.9. Barcelona Super Computing Case

7.9.1. BSC Case
7.9.2. MARIA Model
7.9.3. Existing Corpus
7.9.4. Importance of Having a Large Spanish Language Corpus

7.10. Practical Applications

7.10.1. Automatic Summary
7.10.2. Text Translation
7.10.3. Sentiment Analysis
7.10.4. Speech Recognition

Module 8. R&D and AI. Computer Vision. Object Identification and Tracking

8.1. Computer Vision

8.1.1. Computer Vision
8.1.2. Computational Vision
8.1.3. Interpretation of the Machines in an Image

8.2. Activation Functions

8.2.1. Activation Functions
8.2.2. Sigmoid
8.2.3. RELU
8.2.4. Hyperbolic Tangent
8.2.5. Softmax

8.3. Construction of Convolutional Neural Networks

8.3.1. Convolution Operation
8.3.2. ReLU Layer
8.3.3. Pooling
8.3.4. Flattering
8.3.5. Full Connection

8.4. Convolution Process

8.4.1. How a Convolution Works
8.4.2. Convolution Code
8.4.3. Convolution. Application

8.5. Transformations with Images

8.5.1. Transformations with Images
8.5.2. Advanced Transformations
8.5.3. Transformations with Images. Application
8.5.4. Transformations with Images. Use Case

8.6. Transfer Learning

8.6.1. Transfer Learning
8.6.2. Transfer Learning. Typology
8.6.3. Deep Networks to Apply Transfer Learning

8.7. Computer Vision. Use Case

8.7.1. Image Classification
8.7.2. Object Detection
8.7.3. Object Identification
8.7.4. Object Segmentation

8.8. Object Detection

8.8.1. Convolution-Based Detection
8.8.2. R-CNN, Selective Search
8.8.3. Rapid Detection with YOLO
8.8.4. Other Possible Solutions

8.9. GAN. Generative Adversarial Networks

8.9.1. Generative Adversarial Networks
8.9.2. Code for a GAN
8.9.3. GAN. Application

8.10. Application of Computer Vision Models

8.10.1. Content Organization
8.10.2. Visual Search Engines
8.10.3. Facial Recognition
8.10.4. Augmented Reality
8.10.5. Autonomous Driving
8.10.6. Fault Identification at each Assembly
8.10.7. Pest Identification
8.10.8. Health

Module 9. Quantum Computing. A New Model of Computing

9.1. Quantum Computing

9.1.1. Differences with Classical Computing
9.1.2. Need for Quantum Computing
9.1.3. Quantum Computers Available: Nature and Technology

9.2. Applications of Quantum Computing

9.2.1. Quantum Computing vs. Classical Computing Applications
9.2.2. Contexts of Use
9.2.3. Application in Real Cases

9.3. Mathematical Foundations of Quantum Computing

9.3.1. Computational Complexity
9.3.2. Double Slit Experiment. Particles and Waves
9.3.3. Intertwining

9.4. Geometric Foundations of Quantum Computing

9.4.1. Qubit and Complex Two-Dimensional Hilbert Space
9.4.2. Dirac’s General Formalism
9.4.3. N-Qubits States and Hilbert Space of Dimension 2n

9.5. Mathematical Fundamentals of Linear Algebra

9.5.1. The Domestic Product
9.5.2. Hermitian Operators
9.5.3. Eigenvalues and Eigenvectors

9.6. Quantum Circuits

9.6.1. Bell States and Pauli Matrices
9.6.2. Quantum Logic Gates
9.6.3. Quantum Control Gates

9.7. Quantum Algorithms

9.7.1. Reversible Quantum Gates
9.7.2. Quantum Fourier Transform
9.7.3. Quantum Teleportation

9.8. Algorithms Demonstrating Quantum Supremacy

9.8.1. Deutsch´s Algorithm
9.8.2. Shor´s Algorithm
9.8.3. Grover´s Algorithm

9.9. Quantum Computer Programming

9.9.1. My First Program on Qiskit (IBM)
9.9.2. My First Program on Ocean (Dwave)
9.9.3. My First Program on Cirq (Google)

9.10. Application on Quantum Computers

9.10.1. Creation of Logical Gates

9.10.1.1. Creation of a Quantum Digital Adder

9.10.2. Creation of Quantum Games
9.10.3. Secret Key Communication between Bob and Alice

Module 10. Quantum Machine Learning. Future Artificial Intelligence

10.1. Classical Machine Learning Algorithms

10.1.1. Descriptive, Predictive, Proactive and Prescriptive Models
10.1.2. Supervised and Unsupervised Models
10.1.3. Feature Reduction, PCA, Covariance Matrix, SVM, Neural Networks
10.1.4. ML Optimization: Gradient Descent

10.2. Classic Deep Learning Algorithms

10.2.1. Boltzmann Networks. The Machine Learning Revolution
10.2.2. Deep Learning Models. CNN, LSTM, GANs
10.2.3. Encoder-Decoder Models
10.2.4. Signal Analysis Models. Fourier Analysis

10.3. Quantum Classifiers

10.3.1. Quantum Classifier Generation
10.3.2. Amplitude Coding of Data in Quantum States
10.3.3. Encoding of Data in Quantum States by Phase/Angle
10.3.4. High-Level Coding

10.4. Optimization Algorithms

10.4.1. Quantum Approximate Optimization Algorithm (QAOA)
10.4.2. Variational Quantum Eigensolvers (VQE)
10.4.3. Quadratic Unconstrained Binary Optimization (QUBO)

10.5. Optimization Algorithms Examples

10.5.1. PCA with Quantum Circuits
10.5.2. Optimization of Stock Packages
10.5.3. Optimization of logistics routes

10.6. Quantum Kernels Machine Learning

10.6.1. Variational Quantum Classifiers. QKA
10.6.2. Quantum Kernels Machine Learning
10.6.3. Classification Based on Quantum Kernel
10.6.4. Clustering Based on Quantum Kernel

10.7. Quantum Neural Networks

10.7.1. Classical Neural Networks and Perceptron
10.7.2. Quantum Neural Networks and Perceptron
10.7.3. Quantum Convolutional Neural Networks

10.8. Advanced Deep Learning (DL) Algorithms

10.8.1. Quantum Boltzmann Machines
10.8.2. General Adversarial Networks
10.8.3. Quantum Fourier Transformation, Quantum Phase Estimation and Quantum Matrix

10.9. Machine Learning Use Case

10.9.1. Experimentation with VQC (Variational Quantum Classifier)
10.9.2. Experimentation with Quantum Neural Networks
10.9.3. Experimentation with GANS

10.10. Quantum Computing and Artificial Intelligence

10.10.1. Quantum Capacity in ML Models
10.10.2. Quantum Knowledge Graphs
10.10.3. The Future of Quantum Artificial Intelligence

You will be technically immersed in the most relevant technologies that will play a major role in the technological advances of the coming years"