A comprehensive and 100% online program, exclusive to TECH, with an international perspective supported by our membership with the Association for Computing Machinery" 

Currently, technological development has established new environments where process optimization, information security, and connectivity are essential for the competitiveness of any organization. In this context, Advanced Systems Computing enables the integration of complex solutions, the automation of processes, and data-driven decision-making. Thanks to this discipline, it is possible to build robust, adaptable, and scalable infrastructures. 

Aware of this reality, TECH Global University will deepen current knowledge through an academic plan focused on IT project management and leadership, as well as the administration of distributed systems and networks. Additionally, cloud computing environments, essential for service virtualization, massive data storage, and on-demand technological solutions, will be precisely addressed. Through this approach, students will be guaranteed technical preparation aligned with market demands and the most globally used technological development models.
 
Through this university program, professionals will acquire tools to lead technological initiatives, coordinate multidisciplinary teams, and make strategic decisions in high-demand environments. They will also develop competencies to manage complex infrastructures, oversee cloud migration projects, and optimize technological resources sustainably. In fact, this academic path will expand professional horizons and open doors to key positions in companies that require profiles with a solid technical foundation and a global vision of computer systems.  

On the other hand, TECH Global University methodology adapts to the real needs of the professional environment. Moreover, its 100% online study system allows students to progress at any time of the day, seven days a week, and from any device with an internet connection. This model incorporates the Relearning method, a strategy that enhances knowledge retention through contextualized repetition and active experience, promoting a deeper and more lasting mastery of the content.
Furthermore, thanks to TECH's membership in the Association for Computing Machinery (ACM), students will have access to exclusive and up-to-date resources, such as scientific publications, specialized courses, and international conferences. Additionally, they will have the opportunity to expand their network by

connecting with experts in technology, artificial intelligence, data science, and other key disciplines in the sector.  

You will gain comprehensive knowledge of the technical and security standards that govern current development"

This Master's Degree in Advanced Systems Computing contains the most complete and up-to-date university program on the market. Its most notable features are:

• The development of practical cases presented by experts in Advanced Systems Computing 
• The graphic, schematic, and practical contents with which they are created, provide scientific and practical information on the disciplines that are essential for professional practice 
• Practical exercises where self-assessment can be used to improve learning 
• Special emphasis on innovative methodologies in Computing 
• Theoretical lessons, questions for experts, discussion forums on controversial issues and individual reflection work 
• Content that is accessible from any fixed or portable device with an Internet connection

You will deepen your understanding of the fundamentals and applications of Computer Systems, addressing everything from advanced architecture to the management of complex infrastructures” 

The program includes faculty members from the field of Advanced Systems Computing, who bring their work experience to this program, along with renowned specialists from leading societies and prestigious universities. 

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide an immersive learning experience designed to prepare for real-life situations. 

This program is designed around Problem-Based Learning, whereby the student must try to solve the different professional practice situations that arise throughout the program. For this purpose, the professional will be assisted by an innovative interactive video system created by renowned and experienced experts. 

You will enhance your competencies in IT project management, effectively overseeing each phase"

You will perfect your skills in the use of cloud computing, adapting its technological solutions to the most demanding standards"

This innovative academic path, which complements this university program, will address key concepts in Advanced Systems Computing. It will also delve into the design of architectures for IoT technologies, incorporate the analysis of large data volumes through Big Data, and allow exploration of innovative solutions in mobile devices. Additionally, it will focus on the implementation of security systems, essential to ensuring the integrity of information in distributed environments. This thematic structure, articulated with an applied approach, will enhance the development of technical competencies necessary to lead technological projects in highly specialized sectors.  

You will manage modern Big Data tools to handle large data volumes” 

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. Difference
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. Sprints Planning and Execution
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 Direction

1.6.1. Lean IT and Kanban. Application
1.6.2. Lean IT and Kanban Advantages and Disadvantages
1.6.3. Control Panels. Usage
1.6.4. Lean IT and Kanban Project Management and Direction. 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. Characteristics
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 Communication

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 in Blockchain Networks. Types
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. The 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 a 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. Native Development in the Cloud
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. Backend 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. Architecture of IoT Technologies

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

5.1.1. The Internet of Things (IoT)
5.1.2. IoT Technologies
5.1.3. The 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 (Internet of Things). Use Cases

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. Competences
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 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. Securitization 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. Big Data Processing

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. The 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. IT Governance, 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

You will drive progress in Systems Engineering through adaptive solutions”