Become an expert in Cybersecurity by mastering Computer Science and Data Analysis, thereby greatly improving your employability in an increasingly booming sector" 

Driven by the continuous advances in information technology, it is not only technology that has benefited from great improvements, but also the digital tools themselves with which many tasks are performed today. The other side of the coin is that these advances have also led to an increase in computer vulnerabilities. For this reason, more and more companies are looking for professionals specialized in Cybersecurity who can provide them with adequate protection against all types of cyber-attacks.  

In this advanced master’s degree, computer scientists will be able to delve into aspects such as security in the development and design of systems, the best cryptographic techniques or security in Cloud Computing environments. In addition, this program focuses on programming fundamentals and data structure, algorithmics and complexity, as well as advanced algorithm design, advanced programming, language processors and computer graphics, among others. All this, with numerous multimedia teaching resources, taught by the most prestigious and specialized faculty in the field. 

On the other hand, this educational program addresses data science from both a technical and business perspective, offering students all the skills they require to the knowledge hidden within said data. As such, computer scientists will be able to analyze the most current algorithms, platforms and tools for data exploration, visualization, manipulation, processing and analysis in great detail. All of the above is complemented by the executive business skills required to make key decisions in a company.   

This program provides the professional with the specific tools and skills to successfully develop their professional activity in the broad environment of computing. Working on key competencies such as knowledge of the reality and daily practice in different IT fields and developing responsibility in the monitoring and supervision of their work, as well as specific skills within each field. 

With this program, computer scientists will be able to specialize in Computer Science, Cybersecurity and Data Analysis, making it the perfect opportunity to enhance their professional career. All this will be tangible thanks to a 100% online program, which adapts to the daily needs of professionals, so that they only require a device with an Internet connection to start working toward developing a comprehensive professional profile with international projection. 

In a comfortable and simple way, acquire the necessary knowledge in Computer Science, Cybersecurity and Data Analysis to perform quality computer programming"

This advanced master’s degree in Computer Science, Cybersecurity and Data Analysis contains the most complete and up-to-date educational program on the market. The most important features include:

• The development of case studies presented by IT experts
• 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
• Its special emphasis on innovative methodologies for Cybersecurity and Data Analysis
• 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 

TECH puts at your service a wide and clear educational material, which incorporates all the current topics of interest, so that you can continue to advance in computing"

Its teaching staff includes professionals from the field of Computer Science, who contribute their work experience to this program, as well as renowned specialists from prestigious universities and leading societies. 

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.  

Empower your career by determining the creation of dashboards and KPIs according to the department in which you work"

Learn, first hand, the best security techniques applied to Cloud Computing environments or Blockchain technology"

The advanced master’s degree in Computer Science, Cybersecurity and Data Analysis has been created specifically for the computer scientist looking to advance in this field quickly and with real quality. For this reason, a program has been organized based on realistic and high-value objectives that will propel you to another level of work in this field. The professional will focus on the study of the different techniques, technologies and phases necessary for computing, from a disruptive, complete and up to date perspective. 

TECH puts a high-quality program at your service, one that will allow you to intervene with solvency in computing, guaranteeing the security of your company" 

General Objectives

• Being up to date scientifically and technologically, as well as to prepare for the professional practice of computing and languages in a transversal and versatile way adapted to new technologies and innovations in this field
• Generate specialized knowledge about an information system, types and security aspects that must be taken into account
• Identify the vulnerabilities of an information system
• Develop the legal regulation and typification of the crime attacking an information system
• Evaluate the different models of security architecture to establish the most suitable model for the organization
• Identify the regulatory frameworks of application and their regulatory bases
• Analyze the organizational and functional structure of an information security area (the CISO's office)
• Analyze and develop the concept of risk, uncertainty within the environment in which we live
• Examine the risk management model based on Iso 31.000 
• Examine the science of cryptology and the relationship to its branches: cryptography, cryptanalysis, steganography and stegoanalysis 
• Analyze the types of cryptography according to the type of algorithm and according to its use 
• Examine digital certificates 
• Examining the Public Key Infrastructure (PKI) 
• Develop the concept of identity management 
• Identify authentication methods 
• Generate specialized knowledge of the IT security ecosystem 
• Evaluate the knowledge in terms of Cybersecurity
• Identify the areas of Cloud security 
• Analyze the services and tools in each of the security areas
• Develop the security specifications of each LPWAN technology
• Analyze comparatively the security of LPWAN technologies
• Analyze the benefits of applying data analysis techniques in each department of the company
• Develop the basis for understanding the needs and applications of each department  
• Generate specialized knowledge to select the right tool  
• Propose techniques and objectives in order to be as productive as possible according to the department 

Specific Objectives

Module 1. Programming Fundamentals 

• Understand the basic structure of a computer, software and general purpose programming languages
• Learn to design and interpret algorithms, which are the necessary basis for developing computer programs
• Understand the essential elements of a computer program, such as the different types of data, operators, expressions, statements, I/O and control statements
• Understand the different data structures available in general purpose programming languages, both static and dynamic, and to acquire the essential knowledge for file handling
• Know the different testing techniques in computer programs and the importance of generating good documentation together with good source code
• Learn the basic concepts of the C++ programming language, one of the most widely used languages in the world

Module 2. Data Structure 

• Learn the basics of programming in the C++ language, including classes, variables, conditional expressions and objects
• Understand abstract data types, linear data structure types, simple and complex hierarchical data structures, as well as their implementation in C++ 
• Understand the operation of advanced data structures other than the usual ones
• Know the theory and practice related to the use of priority heaps and queues
• Learn the operation of Hash tables, such as abstract data types and functions 
• Understand Graph theory, as well as advanced Graph algorithms and concepts

Module 3. Algorithm and Complexity 

• Learn the main strategies for algorithm design, as well as the different methods and measures for algorithm computation
• Know the main sorting algorithms used in software development
• Understand the operation of the different algorithms with trees, Heaps and Graphs 
• Understand the operation of Greedy algorithms, their strategy and examples of their use in the main known problems. We will also learn about the use of Greedy algorithms on graphs
• We will learn the main strategies of minimum path search, with the approach of essential problems of the field and algorithms for their resolution
• Understand the Backtracking technique and its main uses, as well as other alternative techniques

Module 4. Advanced Algorithm Design 

• Delve into advanced algorithm design, analyzing recursive and divide-and-conquer algorithms, as well as performing amortized analysis
• Understand dynamic programming concepts and algorithms for NP problems
• Understand the operation of combinatorial optimization, as well as the different randomization algorithms and parallel algorithms
• Know and understand the operation of the different local and candidate search methods
• Learn the mechanisms of formal verification of programs and iterative programs, including first-order logic and Hoare's formal system
• Learn the operation of some of the main numerical methods such as the bisection method, the Newton-Raphson method and the secant method

Module 5. Advanced Programming 

• In-depth knowledge of programming, especially as it relates to object-oriented programming, and the different types of relationships between existing classes
• Know the different design patterns for object-oriented problems
• Learn about event-driven programming and the development of user interfaces with Qt
• Acquire the essential knowledge of Concurrent Programming, processes and threads
• Learn how to manage the use of threads and synchronization, as well as the resolution of common problems within Concurrent Programming 
• Understand the importance of documentation and testing in software development

Module 6. Theoretical Computer Science 

• Understand the essential theoretical mathematical concepts behind Computer Science, such as propositional logic, set theory and numerable and non-numerable sets
• Understand the concepts of formal languages and grammars, as well as Turing machines in their different variants
• Learn about the different types of undecidable problems and intractable problems, including the different variants of them and their approaches 
• Understand the operation of different kinds of randomization-based languages and other kinds of classes and grammars 
• Learn about other advanced computing systems such as membrane computing, DNA computing and quantum computing

Module 7. Automata Theory and Formal Languages 

• Understand the theory of automata and formal languages, learning the concepts of alphabets, strings and languages, as well as how to perform formal demonstrations
• Delve into the different types of finite automata, both deterministic and non-deterministic
• Learn the basic and advanced concepts related to regular languages and regular expressions, as well as the application of the pumping lemma and the closure of regular languages
• Understand context-independent grammars, as well as the operation of stack automata
• Delve into normal forms, the pumping lemma of context-independent grammars and properties of context-independent languages

Module 8. Language Processors 

• Introduce the concepts related to the compilation process and the different types of analysis: lexical, syntactic and semantic
• Learn how a lexical analyzer works, its implementation and error recovery
• Delve into the knowledge of syntactic analysis, both top-down and bottom-up, but with special emphasis on the different types of bottom-up syntactic parsers 
• Understand the functioning of semantic parsers, the syntax-driven tradition, the symbol table and the different types 
• Learn the various mechanisms for code generation, both in runtime environments and for intermediate code generation
• Lay the groundwork for code optimization, including expression reordering and loop optimization 

Module 9. Computer Graphics and Visualization 

• Introduce the essential concepts of computer graphics and computer visualization, such as color theory and its models and the properties of light
• Understand the functioning of output primitives and their algorithms, both for line drawing and for drawing circles and fills
• Delve into the study of the different transformations, both 2D and 3D, and their coordinate systems and computer visualization
• Learn how to make projections and cuts in 3D, as well as the elimination of hidden surfaces
• Learn the theory related to interpolation and parametric curves, as well as Bézier Curves and B-Splines

Module 10. Bio-Inspired Computing 

• Introduce the concept of bio-inspired computing, as well as to understand the functioning of the different types of social adaptation algorithms and genetic algorithms
• Study of the different models of evolutionary computation, knowing their strategies, programming, algorithms and models based on estimation of distributions
• Understand the main space exploration-exploitation strategies for genetic algorithms
• Understand the operation of evolutionary programming applied to learning problems and multi-objective problems
• Learn the essential concepts related to neural networks and understand the operation of real use cases applied to fields as diverse as medical research, economics and artificial vision

Module 11. Security in System Design and Development 

• Assess the security of an information system in all its components and layers
• Identify current security threat types and trends 
• Establish security guidelines by defining security and contingency policies and plans
• Analyze strategies and tools to ensure the integrity and security of information systems
• Apply specific techniques and tools for each type of attack or security vulnerability
• Protect sensitive information stored in the information system
• Have the legal framework and typification of the crime, completing the vision with the typification of the offender and his victim

Module 12. Information Security Architectures and Models 

• Align the Safety Master Plan with the organization's strategic objectives
• Establish a continuous risk management framework as an integral part of the Master Security Plan
• Determine appropriate indicators for monitoring ISMS implementation 
• Establish a policy-based security strategy 
• Analyze the objectives and procedures associated with the employee, supplier and partner awareness plan
• Identify, within the regulatory framework, the regulations, certifications and laws applicable to each organization
• Develop the fundamental elements required by the ISO 27001:2013 standard
• Implement a privacy management model in line with the European GDPR/RGPD regulation

Module 13. IT Security Management 

• Identify the different structures that an information security area can have
• Develop a security model based on three lines of defense
• Present the different periodic and extraordinary committees in which the Cybersecurity area intervenes
• Specify the technological tools that support the main functions of the security operations team (SOC)
• Evaluate vulnerability control measures appropriate to each scenario 
• Develop the security operations framework based on the NIST CSF 
• Specify the scope of the different types of audits (RedTeam, Pentesting, Bug Bounty, etc.)
• Propose the activities to be carried out after a security incident
• Set up an information security command center encompassing all relevant actors (authorities, customers, suppliers, etc.)

Module 14. Risk Analysis and IT Security Environment 

• Examine, with a holistic view, the environment in which we operate
• Identify the main risks and opportunities that may affect the achievement of our objectives
• Analyze the risks based on the best practices at our disposal 
• Evaluate the potential impact of these risks and opportunities 
• Develop techniques that will enable us to address risks and opportunities in a way that maximizes our value contribution 
• Examine in depth the different techniques for transferring risk and value 
• Generate value from the design of proprietary models for agile risk management
• Examine the results to propose continuous improvements in project and process management based on Risk-Driven management models
• Innovate and transform general data into relevant information for risk-based decision making

Module 15. Cryptography in IT 

• Compile the fundamental operations (XOR, large numbers, substitution and transposition) and the various components (One-Way functions, Hash, random number generators) 
• Analyze cryptographic techniques 
• Develop the different cryptographic algorithms 
• Demonstrate the use of digital signatures and their application in digital certificates 
• Evaluate key management systems and the importance of cryptographic key lengths 
• Examine key derivation algorithms 
• Analyze key life cycle 
• Evaluate block cipher and stream cipher modes 
• Determine pseudorandom number generators 
• Develop real-world cryptography application cases, such as Kerberos, PGP or smart cards 
• Examine related associations and organizations, such as ISO, NIST or NCSC 
• Determine the challenges in quantum computing cryptography  

Module 16. Identity and Access Management in IT security 

• Develop the concept of digital identity 
• Evaluating physical access control to information 
• Fundamentals of biometric authentication and MFA authentication 
• Evaluate attacks related to information confidentiality 
• Analyze identity federation 
• Establish network access control 

Module 17. Security in Communications and Software Operation 

• Develop expertise in physical and logical security 
• Demonstrate knowledge of communications and networks 
  Identify major malicious attacks 
• Establish a secure development framework 
• Demonstrate knowledge of key information security management system regulations 
• Demonstrate the operation of a cybersecurity operations center 
• Demonstrate the importance of having cybersecurity practices for organizational disasters

Module 18. Security in Cloud Environments 

• Identify risks of a public cloud infrastructure deployment 
• Define security requirements 
• Developing a security plan for a cloud deployment 
• Identify the cloud services to be deployed for the execution of a security plan 
• Determine the operations necessary for the prevention mechanisms 
• Establish guidelines for a Logging and monitoring system
• Propose incident response actions 

Module 19. Security in IoT Device Communications 

• Introduce the simplified IoT architecture 
• Explain the differences between generalist connectivity technologies and connectivity technologies for the IoT
• Establish the concept of the iron triangle of IoT connectivity
• Analyze the security specifications of LoRaWAN technology, NB-IoT technology and WiSUN technology
• Justify the choice of the appropriate IoT technology for each project

Module 20. Business Continuity Plan Associated with Security 

• Present the key elements of each phase and analyze the characteristics of the business continuity plan (BCP)
• Justify the need for a Business Continuity Plan 
• Determine the success and risk maps for each phase of the business continuity plan 
• Specify how to establish an action plan for implementation
• Evaluating the completeness of a Business Continuity Plan (BCP) 
• Develop the plan for the successful implementation of a business continuity plan for our business

Module 21. Data Analysis in a Business Organization 

• Develop analytical skills in order to make quality decisions  
• Examine effective marketing and communication campaigns  
• Determine the creation of scorecards and kpi's according to the department
• Generate specialized knowledge to develop predictive analytics  
• Propose business and loyalty plans based on market research
• Develop the ability to listen to the customer  
• Apply statistical, quantitative and technical knowledge in real situations 

Module 22. Data and Information Management and Manipulation for Data Science 

• Perform Data Analysis 
• Unify diverse data: Achieving consistency of information 
• Producing relevant, effective information for decision making 
• Determine the best practices for data management according to its typology and uses
• Establish data access and reuse policies 
• Ensure security and availability: information availability, integrity and confidentiality 
• Examine data management tools using programming languages 

Module 23. Devices and IoT platforms as a Foundation for Data Science 

• Define what is meant by IoT (Internet of Things) and IIoT (Industrial Internet of Things) 
• Examining the Industrial Internet Consortium 
• Analyze what is the IoT reference architecture  
• Address IoT sensors and devices and their classification
• Identify communications protocols and technologies used in IoT
• Examine the different Cloud platforms in IoT: general purpose, industrial, open source
• Develop data exchange mechanisms  
• Establish security requirements and strategies  
• Present the different IoT and IIoT application fields 

Module 24. Graphical Representation of Data Analysis 

• Generate specialized knowledge in data analysis and representation 
• Examine the different types of grouped data 
• Establish the most-used graphic representations  in different fields 
• Determine the design principles in data visualization 
• Introduce graphic narrative as a tool 
• Analyze the different software tools for graphing and exploratory data analysis 

Module 25. Data Science Tools 

• Develop the skills to convert data into information from which knowledge can be extracted 
• Determine the main features of a dataset, its structure, components and the implications of its distribution in the modeling 
• Supporting decision making by performing comprehensive data analysis in advance 
• Develop skills to solve practical cases using data science techniques
• Establish the most appropriate general tools and methods for modeling each Dataset based on the preprocessing performed 
• Evaluate the results in an analytical way, understanding the impact of the chosen strategy on the various metrics 
• Demonstrate critical analysis of the results obtained after applying preprocessing or modeling methods 

Module 26. Data Mining: Selection, Pre-Processing and Transformation 

• Generate specialized knowledge about the statistical prerequisites for any data analysis and evaluation 
• Develop the necessary skills for data identification, preparation and transformation 
• Evaluate the various methodologies presented and identify advantages and drawbacks 
• Examine the problems in high dimensional data environments 
• Implement algorithms used for data preprocessing 
• Demonstrate the ability to interpret data visualization for descriptive analysis 
• Develop advanced knowledge of the different existing data preparation techniques for data cleaning, normalization and transformation 

Module 27. Predictability and Analysis of Stochastic Phenomena 

• Analyze time series 
• Develop the formulation and basic properties of univariate time series models 
• Examine the methodology of modeling and prediction of real time series 
• Assess univariate models including outliers 
• Apply dynamic regression models and apply the methodology for the construction of such models from observed series 
• Address the spectral analysis of univariate time series, as well as the fundamentals related to periodogram-based inference and interpretation 
• Estimate the probability and trend in time series for a given time horizon 

Module 28. Design and Development of Intelligent Systems 

• Analyze the step from information to knowledge 
• Develop the different types of machine learning 
• Examine metrics and scores to quantify model quality 
• Implement the different machine learning algorithms 
• Identify probabilistic reasoning models 
• Lay the foundations for deep learning 
• Demonstrate the skills acquired to understand the various machine learning algorithms 

Module 29. Architecture and Systems for Intensive Use of Data 

• Determine the requirements for mass data usage systems 
• Examine different data models and analyze databases 
• Analyze the key functionalities for distributed systems and their importance in different types of systems 
• Evaluate which widely used applications use the fundamentals of distributed systems to design their systems
• Analyze the way in which databases store and retrieve information 
• Understand the different replication models and associated issues 
• Develop partitioning and distributed transactions 
• Assess batch systems and (near) real time systems 

Module 30. Practical Application of Data Science in Business Sectors  

• Analyze the state of the art of Artificial Intelligence (AI) and data analysis
• Develop specialized knowledge of the most widely used technologies 
• Generate a better understanding of the technology through use cases
• Analyze the chosen strategies to select the best technologies to implement 
• Determine the fields of application 
• Examine the actual and potential risks of the technology used 
• Propose benefits derived from the use 
• Identify future trends in specific fields 

Achieve excellence by completing a program that will enable you to generate specialized knowledge in Computer Science, Cybersecurity and Data Analysis"