Aspire to lead IT projects as ambitious as those of digital banking with this professional master’s degree in TECH" 

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Project management, distributed systems design or software engineering are just some of the fields where specialization is no longer an advantage, but a sine qua non requirement to reach positions of responsibility and greater prestige. For this reason, the work of updating and specialization of IT professionals must be continuous, promoting their knowledge of the most advanced and innovative systems. 

Focusing on optimal project management strategies, cloud computing and adaptation of classical computing into physical information systems, this university program offers comprehensive training in the Advanced Systems that the professional needs to master to further advance his or her career. 

The teaching staff, composed of professionals with recognized experience in the field of Systems Engineering, have integrated their own personal experience into the contents of the entire syllabus. This guarantees that the professional master’s degree is not only limited to a theoretical aspect, but covers the most efficient and current practice used in all kinds of projects. 

This is a great advantage for the IT professional, as he/she will not only be able to acquire top-level theoretical knowledge, but will also learn the practical keys to direct his/her career towards IT project management, development of mobile applications or control of security systems and Big Data, among many other professional opportunities provided by such an advanced degree. 

All this with the flexibility needed to not have to sacrifice any aspect of your personal or professional life. In TECH there are no presential classes or fixed schedules, but it is the student himself who establishes his own study time and teaching load. The entire syllabus is available for download from the first day of the degree, and can be studied from any device with an internet connection. 

Enroll now in the professional master’s degree that will take your IT career to the next level of knowledge and career success" 

This professional master’s degree in Advanced Operating Systems Computing contains the most complete and up-to-date scientific program on the market. Its features are:

  • The development of case studies presented by experts in Advanced Systems Informatics Advanced Systems
  • The graphic, schematic, and eminently practical contents with which they are created, provide scientific and practical information on the disciplines that are
  • essential for professional practice
  • Practical exercises where the self-assessment process can be carried out to improve learning 
  • Its special emphasis on innovative methodologies 
  • Theoretical lectures, questions to the expert and individual reflection assignments 
  • Access to content from any fixed or portable device with an Internet connection

Choose where, when and how. You decide the distribution of your course load, giving you the flexibility you need to combine this degree with your daily professional and work activities"

The program’s teaching staff includes professionals from sector who contribute their work experience to this training program, as well as 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 immersive training programmed to train in real situations. 

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the academic year. This will be done with the help of an innovative system of interactive videos made by renowned experts. 

It delves into the most sought-after professional skills in the advanced computing sector, including information systems security, solutions for uploading data and applications to the cloud or management of distributed systems"

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You will be supported by the technical and teaching staff of the world's largest online academic institution, TECH”


To guarantee the maximum profitability of the teaching material, TECH uses a teaching methodology in which it is a pioneer: relearning. Through the gradual repetition of the most important terms and concepts throughout the program, the computer professional acquires a much more progressive and natural learning process. This saves you numerous hours of study time, which you can in turn devote to the many supplementary readings provided or practical exercises based on real cases.

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You will be able to access a huge amount of high-quality audiovisual material, created by the teachers themselves to deepen or even summarize each of the proposed topics"

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. 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 &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. Communication 
2.4.3. Software 
2.4.4. Security/Safety 

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/safety

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 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. 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. Backend Development of Software Applications 

4.4.1. Backend Development of Software Applications 
4.4.2. Backend Architecture of Software Applications 
4.4.3. Backend 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 Solutions 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 Solutions Platforms. Advanced Concepts 

5.5. Smart Things 

5.5.1. Smart Buildings 
5.5.2. Smart Cities 
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. 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. 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. Communication 
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. Training 
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. 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. Architecture Big Data

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. Framework COBIT 
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

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This is the Opportunity You've Been Waiting For to Take Your IT Career to its Zenith. Don't wait any longer and finalize your registration today"