Thanks to this Hybrid professional master’s degree, you will gain expertise in cutting-edge areas such as IT project management, distributed systems, cloud computing and Artificial Intelligence”

In today's Computing landscape, Advanced Systems are undergoing a rapid evolution driven by the growth of Artificial Intelligence (AI), cloud computing and cybersecurity. This requires constant updating of knowledge and skills to stay ahead in a perpetually changing technological environment.

This is how this Hybrid professional master’s degree was created, thanks to which computer scientists will learn to differentiate between IT projects and processes, identifying success criteria and evaluating the scope and requirements to justify solid business cases. In addition, they will be trained in the selection and application of appropriate management methodologies, using specific tools and techniques for the evaluation and improvement of real projects.

Likewise, the characteristics and advantages of distributed systems and cloud computing, as well as the different types of distributed systems and Cloud First models will be discussed in depth. Integration architectures and emerging technologies, such as Blockchain, will also be analyzed, applying this knowledge to design and manage efficient and secure systems in distributed environments.

Finally, software engineering, IoT technology, development on mobile devices, Artificial Intelligence and computer security will be explored. In this sense, professionals will develop skills in application lifecycle, building IoT solutions and big data analysis, preparing and managing platforms for data exploitation.
In this way, TECH has implemented a comprehensive program, which will be divided into two distinct sections. First, the graduate will be able to study the theory completely online, only needing an electronic device with an Internet connection, with the support of the revolutionary Relearning learning methodology, consisting of the reiteration of key concepts for an optimal assimilation of the contents. Ultimately, the degree includes a 3-week internship in a prestigious IT company.

You will design robust security strategies and manage emerging technologies in governance and IT management contexts, through the best didactic materials, at the forefront of technology and education”

This Hybrid professional master’s degree in Advanced Systems Computing contains the most complete and up-to-date program on the market. The most important features include:

• Development of more than 100 case studies presented by IT professionals, experts in advanced systems and university professors with extensive experience in this field
• The graphic, schematic and eminently practical contents with which they are conceived, gather essential information on those techniques and tools that are indispensable for professional practice
• All of this will be complemented by theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is available from any fixed or portable device with an Internet connection
• Furthermore, you will be able to carry out an internship in one of the best companies

This multidisciplinary program will prepare you to face current and future technological challenges, with a comprehensive and updated vision, thanks to an extensive library of innovative multimedia resources”

In this Professional Master's Degree proposal, of a professionalizing nature and blended learning modality, the program is aimed at updating IT professionals who develop their functions to the development of advanced systems, and who require a high level of qualification. The contents are based on the latest evidence, and oriented in a didactic way to integrate theoretical knowledge in computer science practice, and the theoretical-practical elements will facilitate the updating of knowledge and allow decision making.

Thanks to its multimedia content elaborated with the latest educational technology, they will allow the education professional a situated and contextual learning, that is to say, a simulated environment that will provide an immersive learning programmed to specialize in real situations. This program is designed around Problem-Based Learning, whereby the physician must try to solve the different professional practice situations that arise during the course. For this purpose, students will be assisted by an innovative interactive video system created by renowned experts in the field of educational coaching with extensive experience.

Bet on TECH! You will immerse yourself in cloud computing, addressing topics such as deployment models, economic benefits and associated security capabilities and challenges"

You will take an intensive 3-week internship at a prestigious IT company, where you will acquire all the knowledge to grow personally and professionally"

The syllabus of this university program has been structured to offer comprehensive training in the most critical areas of modern technology. As such, it will cover a wide range of topics, from the management and direction of IT projects, to the design and administration of distributed systems and cloud solutions. In this sense, each module has been designed to provide both theoretical and practical knowledge, allowing professionals to apply what they have learned in real scenarios and keep up to date with the latest trends and emerging technologies.

This Hybrid professional master’s degree will provide you with a combination of flexibility, specialization and applicability, which perfectly matches the demands of the modern job market”

Module 1. IT Project Management and Direction

1.1. IT Project Management and Direction

1.1.1. IT Project
1.1.2. Project and Processes. Differences
1.1.3. IT Project. Success Criteria
1.1.4. IT Project Life Cycle
1.1.5. IT Project Management and Direction. Application

1.2. IT Project Requirements Management

1.2.1. Project Requirements Management
1.2.2. Requirements Management and Traceability
1.2.3. Requirements Management Tools
1.2.4. IT Project Requirements Management. Application

1.3. IT Project Business Cases

1.3.1. IT Project Business Cases
1.3.2. Building the Business Case for the Project
1.3.3. Project Success Criteria
1.3.4. Financial Analysis and Monitoring of the Business Case Throughout the Life of the Project
1.3.5. IT Project Business Cases. Application

1.4. IT Project Management and Direction

1.4.1. Waterfall Project Management
1.4.2. Tools of the Classic Management Methodology
1.4.3. Phases of Classic Project Management: Initiation, Planning, Execution, Follow-up and Closure
1.4.4. Classic IT Project Management and Direction. Application

1.5. AGILE Project Management and Direction

1.5.1. Agile Project Management: Roles, Artifacts
1.5.2. Scrum Planning
1.5.3. Agile Estimation
1.5.4. Planning and Execution of Sprints
1.5.5. Effective Use of Scrum. Application
1.5.6. Agile Project Management and Leadership. Application

1.6. Lean IT and Kanban Project Management and Leadership

1.6.1. Lean IT and Kanban. Application
1.6.2. Lean IT and Kanban Advantages and Disadvantages
1.6.3. Control Panels. Use
1.6.4. Lean IT and Kanban Project Management and Leadership. Application

1.7. Risks in the Management and Direction of IT Projects

1.7.1. Risk Types of Risk: Probability
1.7.2. Risk Mitigation. Common IT Techniques
1.7.3. Risk Management and Communication
1.7.4. Risks in the Management and Direction of IT Projects. Application

1.8. IT Project Monitoring and Control

1.8.1. Monitoring of Project Progress
1.8.2. Project Cost Control
1.8.3. Project Change Management
1.8.4. Project Communications Management. Application
1.8.5. Reporting and Tracking Metrics
1.8.6. IT Project Monitoring and Control. Application

1.9. IT Project Office

1.9.1. Projects, Project Portfolio and Programs
1.9.2. Types of Project Offices: Functions
1.9.3. Project Office Management Processes
1.9.4. Management of a Project Office Application

1.10. Software Tools for IT Projects

1.10.1. Requirements Management
1.10.2. Configuration Management
1.10.3. Project Planning and Monitoring
1.10.4. Change Management
1.10.5. Cost Management
1.10.6. Risk Management
1.10.7. Communication Management
1.10.8. Closure Management
1.10.9. Examples of Tools. Templates

Module 2. Design and Management of Distributed Systems and Networks

2.1. Distributed Systems

2.1.1. Distributed Systems
2.1.2. Distributed Systems Features
2.1.3. Distributed Systems Advantages

2.2. Type of Distributed Systems

2.2.1. Cluster
2.2.2. Grid
2.2.3. Cloud

2.3. Distributed System Architectures

2.3.1. Functional Architecture (Business)
2.3.2. Application Architecture
2.3.3. Management Architecture (Government)
2.3.4. Technological Architecture

2.4. Infrastructure in a Distributed System

2.4.1. Hardware
2.4.2. Communications
2.4.3. Software
2.4.4. Security

2.5. Cloud Computing in Distributed Systems

2.5.1. Cloud Computing
2.5.2. Systems Cloud Computing. Types
2.5.3. Systems Cloud Computing. Advantages

2.6. Client-Server Communications

2.6.1. Transmission Types
2.6.2. Communication Models
2.6.3. Event-Driven Communication

2.7. Integration Architectures

2.7.1. APIs
2.7.2. Microservice Architectures
2.7.3. Event-Driven Architectures
2.7.4. Reactive Architectures

2.8. Distributed Registration Technologies

2.8.1. Distributed Registration Technologies
2.8.2. Distributed Registration Technologies. Typology
2.8.3. Distributed Registration Technologies. Advantages

2.9. Blockchain as a Distributed System

2.9.1. Blockchain as a Distributed System
2.9.2. Blockchain Networks. Typology
2.9.3. Tokens and Redes Blockchain. Typology
2.9.4. Blockchain Technologies
2.9.5. Use Case

2.10. Blockchain. Decentralized Blockchain Paradigm

2.10.1. Consensus Systems
2.10.2. Mining
2.10.3. Hashing
2.10.4. Security

Module 3. Cloud Computing in Computer and Information Systems Engineering

3.1. Cloud Computing

3.1.1. State of the Art of the IT Landscape
3.1.2. Cloud
3.1.3. Cloud Computing

3.2. Security and Resilience in the Cloud

3.2.1. Regions, Availability and Failure Zones
3.2.2. Tenant or Cloud Account Management
3.2.3. Cloud Identity and Access Control

3.3. Cloud Networking

3.3.1. Software-Defined Virtual Networks
3.3.2. Network Components of Software-Defined Network
3.3.3. Connection with other Systems

3.4. Cloud Services

3.4.1. Infrastructure as a Service
3.4.2. Platform as a Service
3.4.3. Serverless Computing
3.4.4. Software as a Service

3.5. High-Performance Computing

3.5.1. High-Performance Computing
3.5.2. Creation of a High-Performance Cluster
3.5.3. Application of High-Performance Computing

3.6. Cloud Storage

3.6.1. Block Storage in the Cloud
3.6.2. Block Storage in the Cloud
3.6.3. Block Storage in the Cloud

3.7. Block Storage in the Cloud

3.7.1. Cloud Monitoring and Management
3.7.2. Interaction with the Cloud: Administration Console
3.7.3. Interaction with Command Line Interface
3.7.4. API-Based Interaction

3.8. Cloud-Native Development

3.8.1. Cloud-Native Development
3.8.2. Containers and Container Orchestration Platforms
3.8.3. Continuous Cloud Integration
3.8.4. Use of Events in the Cloud

3.9. Infrastructure as Code in the Cloud

3.9.1. Management and Provisioning Automation in the Cloud
3.9.2. Terraform
3.9.3. Scripting Integration

3.10. Creation of a Hybrid Infrastructure

3.10.1. Interconnection
3.10.2. Interconnection with Datacenter
3.10.3. Interconnection with other Clouds

Module 4. Software Engineering

4.1. Software Applications in Information Technology

4.1.1. Software Applications
4.1.2. Life Cycle
4.1.3. Architecture
4.1.4. Methods

4.2. Project Management and IT Methodologies

4.2.1. Project Management
4.2.2. Agile Methodologies
4.2.3. Tools

4.3. Front-End Development and Mobile Applications

4.3.1. Front-End Development and Mobile Applications
4.3.2. HTML, CSS
4.3.3. JavaScript, jQuery
4.3.4. Angular
4.3.5. React

4.4. Back-End Development of Software Applications

4.4.1. Back-End Development of Software Applications
4.4.2. Back-End Architecture of Software Applications
4.4.3. Back-end Programming Languages
4.4.4. Application Servers in Software Architecture

4.5. Data Storage, Databases and Caching

4.5.1. Data Management of Software Applications
4.5.2. File System
4.5.3. Relational Databases
4.5.4. Non-Relational Databases
4.5.5. Cache

4.6. Container Management in Cloud Computing

4.6.1. Container Technology
4.6.2. Containers with Docker and Docker-Compose Technology
4.6.3. Container Orchestration with Kubernetes
4.6.4. Containers in Cloud Computing

4.7. Testing and Continuous Integration

4.7.1. Testing and Continuous Integration
4.7.2. Unit Tests
4.7.3. Test e2e
4.7.4. Test Driven Development (TDD)
4.7.5. Continuous Integration

4.8. Software-Oriented Blockchain

4.8.1. Software-Oriented Blockchain
4.8.2. Cryptocurrencies
4.8.3. Types of Blockchain

4.9. Big Data Software, Artificial Intelligence, IoT

4.9.1. Big Data, Artificial Intelligence, IoT
4.9.2. Big Data
4.9.3. Artificial Intelligence
4.9.4. Neural Networks

4.10. IT Software Security

4.10.1. IT Software Security
4.10.2. Servers
4.10.3. Ethical Aspects
4.10.4. European Data Protection Regulation (GDPR)
4.10.5. Risk Analysis and Management

Module 5. IoT Technologies Architecture

5.1. The Art of the Internet of Things (IoT)

5.1.1. Internet of Things IoT
5.1.2. IoT Technologies
5.1.3. Internet of Things. Advanced Concepts

5.2. IoT Solution Architecture

5.2.1. IoT Solution Architecture
5.2.2. Design of an IoT Architecture
5.2.3. Operation and Data Management of an IoT Solution

5.3. IoT and Other Technology Trends

5.3.1. Cloud Computing
5.3.2. Machine/Deep Learning
5.3.3. Artificial Intelligence

5.4. IoT Solution Platforms

5.4.1. Development Platforms
5.4.2. IoT Solutions
5.4.3. IoT Solution Platforms. Advanced Concepts

5.5. Smart Things

5.5.1. Smartbuildings
5.5.2. Smartcities
5.5.3. Intelligent Networks

5.6. Sustainability and IoT

5.6.1. Sustainability and Emerging Technologies
5.6.2. Sustainability in IoT
5.6.3. Sustainable IoT use Cases

5.7. IoT. Case Uses

5.7.1. Cases of use in the Healthcare Sector
5.7.2. Use Cases in Industrial Environments
5.7.3. Use Cases in the Logistics Sector
5.7.4. Cases of use in the Agriculture and Livestock Sector
5.7.5. Other use Cases

5.8. IoT Business Ecosystem

5.8.1. Solution Providers
5.8.2. IoT Consumers
5.8.3. IoT Ecosystem

5.9. The Role of the IoT Engineer

5.9.1. IoT Engineer Role. Skills
5.9.2. The Role of the IoT Specialist in Companies
5.9.3. Recognized Certifications in the Market

5.10. IoT Challenges

5.10.1. IoT Adoption Targets
5.10.2. Main Barriers to Adoption
5.10.3. LoT Applications Future of IoT

Module 6. Technology and Development in Mobile Devices

6.1. Mobile Devices

6.1.1. Mobility
6.1.2. Management
6.1.3. Operability

6.2. Types of Mobile Devices

6.2.1. Smartphones
6.2.2. Tablets
6.2.3. Smart Watches

6.3. Mobile Device Components

6.3.1. Screens
6.3.2. Touch Keypads
6.3.3. Processors
6.3.4. Sensors and Connectors
6.3.5. Batteries

6.4. Wireless Communication

6.4.1. Wireless Communication
6.4.2. Wireless Communication Advantages
6.4.3. Wireless Communication Limitations

6.5. Wireless Communication Classification

6.5.1. Personal Networks
6.5.2. Local Networks
6.5.3. Powerful Networks
6.5.4. Standards

6.6. Mobile Application Development

6.6.1. Hybrid and Native Applications
6.6.2. Environment
6.6.3. Programming Languages
6.6.4. Distribution and Business

6.7. Android Application Development

6.7.1. Android Application Development
6.7.2. Android System Kernel
6.7.3. Android Software Tools

6.8. IOS Application Development

6.8.1. IOS Application Development
6.8.2. IOS Application Core
6.8.3. IOS Application Tools

6.9. Security on Mobile Devices

6.9.1. Safety Layers
6.9.2. Communications
6.9.3. Users
6.9.4. Applications
6.9.5. Operating System

6.10. Mobile Application Development. Tendencies Use Cases

6.10.1. Augmented Reality
6.10.2. Artificial Intelligence
6.10.3. Payment Solutions
6.10.4. Advantages of Blockchain

Module 7. Artificial Intelligence in Systems Engineering and Computer Science

7.1. Artificial Intelligence

7.1.1. Intelligence in Systems Engineering
7.1.2. Artificial Intelligence
7.1.3. Artificial Intelligence. Advanced Concepts

7.2. Importance of Data

7.2.1. Data Ingestion
7.2.2. Analysis and Profiling
7.2.3. Data Refinement

7.3. Machine Learning in Artificial Intelligence

7.3.1. Machine Learning
7.3.2. Supervised Learning
7.3.3. Unsupervised Learning

7.4. Machine Learning in Artificial Intelligence

7.4.1. Deep Learning vs. Machine Learning
7.4.2. Neural Networks

7.5. Robotic Process Automation (RPA) in Artificial Intelligence

7.5.1. RPA in Artificial Intelligence
7.5.2. Process Automation. Good Practices
7.5.3. Process Automation. Continuing Improvement

7.6. Natural Language Processing (NLP) in Artificial Intelligence

7.6.1. NLP in Artificial Intelligence
7.6.2. NPL Applied to Software
7.6.3. NLP. Application

7.7. Image Recognition in Artificial Intelligence

7.7.1. Models
7.7.2. Algorithms
7.7.3. Applications

7.8. Neural Networks in Artificial Intelligence

7.8.1. Models
7.8.2. Learning Algorithms
7.8.3. Applications of Neural Networks in Artificial Intelligence

7.9. Artificial Intelligence (AI) Model Life Cycle

7.9.1. Development of the Artificial Intelligence Model
7.9.2. Education
7.9.3. Putting into Production

7.10. New Application of Artificial Intelligence

7.10.1. Ethics in IA systems
7.10.2. Bias Detection
7.10.3. New Artificial Intelligence Applications

Module 8. Security Systems

8.1. Information Technology Security Systems

8.1.1. Information Systems Security Challenges
8.1.2. Types of Threats
8.1.3. Network and Internet Systems

8.2. Information Security Governance and Management

8.2.1. Security Governance. Safety Regulations
8.2.2. Risk Analysis
8.2.3. Security Planning

8.3. Cryptography and Certificate Technologies

8.3.1. Cryptographic Techniques
8.3.2. Cryptographic Protocols
8.3.3. Digital Certificates. Applications

8.4. Network and Communications Security

8.4.1. Security in Communication Systems
8.4.2. Firewall Security
8.4.3. Intrusion Detection and Prevention Systems

8.5. Identity and Permission Management Systems

8.5.1. Authentication Management Systems
8.5.2. Authorization Management System: Access Policies
8.5.3. Key Management Systems

8.6. Data Security

8.6.1. Securing of Storage Systems
8.6.2. Protection of Database Systems
8.6.3. Securing Data in Transit

8.7. Operating Systems Security

8.7.1. Linux
8.7.2. Windows
8.7.3. Vulnerability Scanning and Patching

8.8. Detection of Threats and Attacks

8.8.1. Auditing, Logging and Monitoring Systems
8.8.2. Event and Alarm Systems
8.8.3. SIEM Systems

8.9. Incident Response

8.9.1. Incident Response Plan
8.9.2. Ensuring Business Continuity
8.9.3. Forensic Analysis and Remediation of Incidents of the Same Nature.

8.10. Security in Cloud Environments

8.10.1. Security in Cloud Environments
8.10.2. Shared Management Model
8.10.3. Security Management Systems Application

Module 9. Big Data in Systems Engineering and Computer Science

9.1. Big Data Applied to IT

9.1.1. Big Data Applied to IT
9.1.2. Big Data. Opportunities
9.1.3. Big Data. Application

9.2. Information and Data

9.2.1. Information Sources
9.2.2. Quality
9.2.3. Transformation

9.3. Processing Big Data

9.3.1. Big Data Processing Hadoop
9.3.2. Big Data Processing. Spark
9.3.3. Streaming Processing

9.4. Data Storage

9.4.1. Data Storage. Databases
9.4.2. Data Storage. Cloud
9.4.3. Data Storage. Information Use

9.5. Big Data Architecture

9.5.1. Big Data Architecture. Data Lake
9.5.2. Big Data Architecture. Process Monitoring
9.5.3. Big Data Architecture. Cloud Computing

9.6. Data Analysis

9.6.1. Data Analysis. Predictive Modeling
9.6.2. Data Analysis. Machine Learning
9.6.3. Data Analysis. Deep Learning

9.7. Data Visualization

9.7.1. Types
9.7.2. Visualization Tools
9.7.3. Reporting Tools

9.8. Information Use

9.8.1. Business Intelligence
9.8.2. Business Analytics
9.8.3. Data Science

9.9. Privacy and Data Protection

9.9.1. Sensitive Data
9.9.2. Consent
9.9.3. Anonymization

9.10. Data Governance

9.10.1. Data Governance
9.10.2. Data Lineage
9.10.3. Data Catalog

Module 10. IT (Information Technology) Governance and Management

10.1. IT Governance and Management

10.1.1. IT Governance and Management
10.1.2. Advanced IT Governance
10.1.3. IT Governance: Security and Risk

10.2. Reference Sources for IT Governance

10.2.1. Frameworks and Models
10.2.2. IT Governance Standards
10.2.3. IT Governance Quality Systems

10.3. IT Governance. Structures and Management

10.3.1. Role of IT Governance
10.3.2. IT Governance Structures
10.3.3. Implementation of IT Governance

10.4. Key Elements in IT Governance

10.4.1. Enterprise Architecture
10.4.2. Data Governance
10.4.3. Relationship of IT Governance and AI

10.5. COBIT. Control Objectives for Information and Related Technologies

10.5.1. COBIT. Control Objectives
10.5.2. COBIT Framework
10.5.3. Areas, Domains and Processes

10.6. ITIL v4 Framework

10.6.1. ITIL v4 Framework
10.6.2. Service Value System
10.6.3. Dimensions and Principles

10.7. IT Governance Performance Measurement

10.7.1. IT Governance Monitoring and Control Principles
10.7.2. IT Governance Control Metrics
10.7.3. Integral Control Panel

10.8. IT Management

10.8.1. IT Management
10.8.2. IT Service Provider Procurement and Management
10.8.3. IT Performance Monitoring
10.8.4. IT Quality Assurance

10.9. Acquisition and Development of Information Systems

10.9.1. Project Management Structure
10.9.2. Product Development Methodology
10.9.3. Implementation and Exploitation of Information Systems

10.10. Governance, IT Management and Cloud Computing

10.10.1. IT Governance and Management in Cloud Computing Environments
10.10.2. Shared Security Management Model
10.10.3. Enterprise Cloud Architectures

Make the most of this opportunity to surround yourself with expert professionals and learn from their work methodology"