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"

This Advanced master’s degree contains a series of specialized modules that will allow the computer scientist to delve into aspects such as digital identification, access control systems, information security architecture, the structure of the security area, information security management systems in communications and software operation or the development of the business continuity plan associated with security. At the same time, the most complete and current techniques for data processing and knowledge extraction are addressed, from both a theoretical and practical perspective.  

All the fields of interest you need to master to work successfully in computer science, compiled in a top-quality syllabus"  

Module 1. Programming Fundamentals 

1.1. Introduction to Programming 

1.1.1. Basic Structure of a Computer 
1.1.2. Software 
1.1.3. Programming Languages 
1.1.4. Life Cycle of a Software Application 

1.2. Algorithm Design 

1.2.1. Problem Solving 
1.2.2. Descriptive Techniques 
1.2.3. Algorithm Elements and Structure 

1.3. Elements of a Program 

1.3.1. C++ Origin and Features 
1.3.2. Development Environment 
1.3.3. Concept of Program 
1.3.4. Types of Fundamental Data 
1.3.5. Operators 
1.3.6. Expressions 
1.3.7. Statements 
1.3.8. Data Input and Output 

1.4. Control Sentences 

1.4.1. Statements 
1.4.2. Branches 
1.4.3. Loops 

1.5. Abstraction and Modularity: Functions 

1.5.1. Modular Design 
1.5.2. Concept of Function and Utility 
1.5.3. Definition of a Function 
1.5.4. Execution Flow in a Function Call 
1.5.5. Function Prototypes 
1.5.6. Results Return 
1.5.7. Calling a Function: Parameters 
1.5.8. Passing Parameters by Reference and by Value 
1.5.9. Scope Identifier 

1.6. Static Data Structures 

1.6.1. Arrays 
1.6.2. Matrices: Polyhedra 
1.6.3. Searching and Sorting 
1.6.4. Chaining: I/O Functions for Chains 
1.6.5. Structures. Unions 
1.6.6. New Types of Data 

1.7. Dynamic Data Structures: Pointers 

1.7.1. Concept: Definition of Pointer 
1.7.2. Pointer Operators and Operations 
1.7.3. Pointer Arrays 
1.7.4. Pointers and Arrays 
1.7.5. Chain Pointers 
1.7.6. Structure Pointers 
1.7.7. Multiple Indirection 
1.7.8. Function Pointers 
1.7.9. Function, Structure and Array Passing as Function Parameters 

1.8. Files 

1.8.1. Basic Concepts 
1.8.2. File Operations 
1.8.3. Types of Files 
1.8.4. File Organization 
1.8.5. Introduction to C++ Files 
1.8.6. Managing Files 

1.9. Recursion 

1.9.1. Definition of Recursion 
1.9.2. Types of Recursion 
1.9.3. Advantages and Disadvantages 
1.9.4. Considerations 
1.9.5. Recursive-Iterative Conversion 
1.9.6. The Recursion Stack 

1.10. Testing and Documentation 

1.10.1. Program Testing 
1.10.2. White Box Testing 
1.10.3. Black Box Testing 
1.10.4. Testing Tools 
1.10.5. Program Documentation 

Module 2. Data Structure 

2.1. Introduction to C ++ Programming 

2.1.1. Classes, Constructors, Methods and Attributes 
2.1.2. Variables 
2.1.3. Conditional Expressions and Loops 
2.1.4. Objects 

2.2. Abstract Data Types (ADT) 

2.2.1. Types of Data 
2.2.2. Basic Structures and ADTs 
2.2.3. Vectors and Arrays 

2.3. Linear Data Structures 

2.3.1. ADT List. Definition 
2.3.2. Linked and Doubly Linked Lists 
2.3.3. Sorted Lists 
2.3.4. Lists in C++ 
2.3.5. ADT Stack 
2.3.6. ADT Queue 
2.3.7. Stack and Queue in C++ 

2.4. Hierarchical Data Structures 

2.4.1. ADT Tree 
2.4.2. Paths 
2.4.3. N-Ary Trees 
2.4.4. Binary Trees 
2.4.5. Binary Search Trees 

2.5. Hierarchical Data Structures: Complex Trees 

2.5.1. Perfectly Balanced or Minimum Height Trees 
2.5.2. Multipath Trees 
2.5.3. Bibliographical References 

2.6. Mounds and Priority Queue 

2.6.1. ADT Mounds 
2.6.2. ADT Priority Queue 

2.7. Hash Tables 

2.7.1. ADT Hash Table 
2.7.2. Hash Functions 
2.7.3. Hash Function in Hash Tables 
2.7.4. Redispersion 
2.7.5. Open Hash Tables 

2.8. Graphs 

2.8.1 ADT Graph 
2.8.2 Graph Types 
2.8.3 Graphical Representation and Basic Operations 
2.8.4 Graph Design 

2.9. Advanced Graph Algorithms and Concepts 

2.9.1. Graph Problems 
2.9.2. Path Algorithms 
2.9.3. Search or Path Algorithms 
2.9.4. Other Algorithms 

 2.10. Other Data Structures 

2.10.1. Sets 
2.10.2. Parallel Arrays 
2.10.3. Symbol Tables 
2.10.4. Tries 

Module 3. Algorithm and Complexity 

3.1. Introduction to Algorithm Design Strategies 

3.1.1. Recursion 
3.1.2. Divide and Conquer 
3.1.3. Other Strategies 

3.2. Efficiency and Analysis of Algorithms 

3.2.1. Efficiency Measures 
3.2.2. Measuring the Size of the Input 
3.2.3. Measuring Execution Time 
3.2.4. Worst, Best and Average Case 
3.2.5. Asymptotic Notation 
3.2.6. Criteria for Mathematical Analysis of Non-Recursive Algorithms 
3.2.7. Mathematical Analysis of Recursive Algorithms 
3.2.8. Empirical Analysis of Algorithms 

3.3. Sorting Algorithms 

3.3.1. Concept of Sorting 
3.3.2. Bubble Sorting 
3.3.3. Sorting by Selection 
3.3.4. Sorting by Insertion 
3.3.5. Merge Sort 
3.3.6. Quicksort 

3.4. Algorithms with Trees 

3.4.1. Tree Concept 
3.4.2. Binary Trees 
3.4.3. Tree Paths 
3.4.4. Representing Expressions 
3.4.5. Ordered Binary Trees 
3.4.6. Balanced Binary Trees 

3.5. Algorithms Using Heaps 

3.5.1. Heaps 
3.5.2. The Heapsort Algorithm 
3.5.3. Priority Queues 

3.6. Graph Algorithms 

3.6.1. Representation 
3.6.2. Traversal in Width 
3.6.3. Depth Travel 
3.6.4. Topological Sorting 

3.7. Greedy Algorithms 

3.7.1. Greedy Strategy 
3.7.2. Elements of the Greedy Strategy 
3.7.3. Currency Exchange 
3.7.4. Traveler’s Problem 
3.7.5. Backpack Problem 

3.8. Minimal Path Finding 

3.8.1. The Minimum Path Problem 
3.8.2. Negative Arcs and Cycles 
3.8.3. Dijkstra’s Algorithm 

3.9. Greedy Algorithms on Graphs 

3.9.1. The Minimum Covering Tree 
3.9.2. Prim’s Algorithm 
3.9.3. Kruskal’s Algorithm 
3.9.4. Complexity Analysis 

3.10. Backtracking 

3.10.1. Backtracking 
3.10.2. Alternative Techniques 

Module 4. Advanced Algorithms Design 

4.1. Analysis of Recursive and Divide and Conquer Algorithms 

4.1.1. Posing and Solving Homogeneous and Non-Homogeneous Recurrence Equations
4.1.2. General Description of the Divide and Conquer Strategy 

4.2. Amortized Analysis 

4.2.1. Aggregate Analysis 
4.2.2. The Accounting Method 
4.2.3. The Potential Method 

4.3. Dynamic Programming and Algorithms for NP Problems 

4.3.1. Characteristics of Dynamic Programming 
4.3.2. Backtracking 
4.3.3. Branching and Pruning 

4.4. Combinatorial Optimization 

4.4.1. Representation 
4.4.2. 1D Optimization 

4.5. Randomization Algorithms 

4.5.1. Examples of Randomization Algorithms 
4.5.2. The Buffon Theorem 
4.5.3. Monte Carlo Algorithm 
4.5.4. Las Vegas Algorithm 

4.6. Local and Candidate Search 

4.6.1. Garcient Ascent 
4.6.2. Hill Climbing 
4.6.3. Simulated Annealing 
4.6.4. Tabu Search 
4.6.5. Candidate Search 

4.7. Formal Verification of Programs 

4.7.1. Specification of Functional Abstractions 
4.7.2. The Language of First-Order Logic 
4.7.3. Hoare’s Formal System 

4.8. Verification of Iterative Programs 

4.8.1. Rules of Hoare’s Formal System 
4.8.2. Concept of Invariant Iterations 

4.9. Numeric Methods 

4.9.1. The Bisection Method 
4.9.2. Newton-Raphson Method 
4.9.3. The Secant Method 

4.10. Parallel Algorithms 

4.10.1. Parallel Binary Operations 
4.10.2. Parallel Operations with Graphs 
4.10.3. Parallelism in Divide and Conquer 
4.10.4. Parallelism in Dynamic Programming 

Module 5. Advanced Programming 

5.1. Introduction to Object-Oriented Programming 

5.1.1. Introduction to Object-Oriented Programming 
5.1.2. Class Design 
5.1.3. Introduction to UML for Problem Modeling 

5.2. Relationships Between Classes 

5.2.1. Abstraction and Inheritance 
5.2.2. Advanced Inheritance Concepts 
5.2.3. Polymorphism 
5.2.4. Composition and Aggregation 

5.3. Introduction to Design Patterns for Object-Oriented Problems 

5.3.1. What Are Design Patterns? 
5.3.2. Factory Pattern 
5.3.4. Singleton Pattern 
5.3.5. Observer Pattern 
5.3.6. Composite Pattern 

5.4. Exceptions 

5.4.1. What Are Exceptions? 
5.4.2. Exception Catching and Handling 
5.4.3. Throwing Exceptions 
5.4.4. Exception Creation 

5.5. User Interfaces 

5.5.1. Introduction to Qt 
5.5.2. Positioning 
5.5.3. What Are Events? 
5.5.4. Events: Definition and Catching 
5.5.5. User Interface Development 

5.6. Introduction to Concurrent Programming 

5.6.1. Introduction to Concurrent Programming 
5.6.2. The Concept of Process and Thread 
5.6.3. Interaction Between Processes or Threads 
5.6.4. Threads in C++ 
5.6.5. Advantages and Disadvantages of Concurrent Programming 

5.7. Thread Management and Synchronization 

5.7.1. Life Cycle of a Thread 
5.7.2. Thread Class 
5.7.3. Thread Planning 
5.7.4. Thread Groups 
5.7.5. Daemon Threads 
5.7.6. Synchronization 
5.7.7. Locking Mechanisms 
5.7.8. Communication Mechanisms 
5.7.9. Monitors 

5.8. Common Problems in Concurrent Programming 

5.8.1. The Problem of Consuming Producers 
5.8.2. The Problem of Readers and Writers 
5.8.3. The Problem of the Philosophers’ Dinner Party 

5.9. Software Documentation and Testing 

5.9.1. Why Is it Important to Document Software? 
5.9.2. Design Documentation 
5.9.3. Documentation Tool Use 

5.10. Software Testing 

5.10.1. Introduction to Software Testing 
5.10.2. Types of Tests 
5.10.3. Unit Test 
5.10.4. Integration Test 
5.10.5. Validation Test 
5.10.6. System Test 

Module 6. Theoretical Computer Science 

6.1. Mathematical Concepts Used 

6.1.1. Introduction to Propositional Logic 
6.1.2. Theory of Relations 
6.1.3. Numerable and Non-Numerable Sets 

6.2. Formal Languages and Grammars and Introduction to Turing Machines 

6.2.1. Formal Languages and Grammars 
6.2.2. Decision Problem 
6.2.3. The Turing Machine 

6.3. Extensions to Turing Machines, Constrained Turing Machines and Computers 

6.3.1. Programming Techniques for Turing Machines 
6.3.2. Extensions for Turing Machines 
6.3.3. Restricted Turing Machines 
6.3.4. Turing Machines and Computers 

6.4. Undecidability 

6.4.1. Non-Recursively Enumerable Language 
6.4.2. A Recursively Enumerable Undecidable Problem 

6.5. Other Undecidable Problems 

6.5.1. Undecidable Problems for Turing Machines 
6.5.2. Post Correspondence Problem (PCP) 

6.6. Intractable Problems 

6.6.1. P and NP Class 
6.6.2. A NP-Complete Problem 
6.6.3. Restricted Satisfiability Problem 
6.6.4. Other NP-Complete Problems 

6.7. Co-NP and PS Problems 

6.7.1. Complementary to NP Languages 
6.7.2. Problems Solvable in Polynomial Space 
6.7.3. Complete PS Problems 

6.8. Classes of Randomization-Based Languages 

6.8.1. MT Model with Randomization 
6.8.2. RP and ZPP Classes 
6.8.3. Primality Test 
6.8.4. Complexity of The Primality Test 

6.9. Other Classes and Grammars 

6.9.1. Probabilistic Finite Automata 
6.9.2. Cellular Automata 
6.9.3. McCulloch and Pitts Cells 
6.9.4. Lindenmayer Grammars 

6.10. Advanced Computing Systems 

6.10.1. Membrane Computing: P-Systems 
6.10.2. DNA Computing 
6.10.3. Quantum Computing 

Module 7: Automata Theory and Formal Languages 

7.1. Introduction to Automata Theory 

7.1.1. Why Study Automata Theory? 
7.1.2. Introduction to Formal Demonstrations 
7.1.3. Other Forms of Demonstration 
7.1.4. Mathematical Induction 
7.1.5. Alphabets, Strings and Languages 

7.2. Deterministic Finite Automata 

7.2.1. Introduction to Finite Automata 
7.2.2. Deterministic Finite Automata 

7.3. Non-Deterministic Finite Automata 

7.3.1. Non-Deterministic Finite Automata 
7.3.2. Equivalence Between AFD and AFND 
7.3.3. Finite Automata with Transitions €  

7.4. Languages and Regular Expressions (I) 

7.4.1. Languages and Regular Expressions 
7.4.2. Finite Automata and Regular Expressions 

7.5. Languages and Regular Expressions (II) 

7.5.1. Conversion of Regular Expressions into Automata 
7.5.2. Applications of Regular Expressions 
7.5.3. Algebra of Regular Expressions

7.6. Pumping and Closure Lemma of Regular Languages 

          7.6.1. Pumping Lemma 
          7.6.2. Closure Properties of Regular Languages

7.7. Equivalence and Minimization of Automata 

          7.7.1. FA Equivalence 
          7.7.2. AF Minimization 

7.8. Context-Independent Grammars (CIGs) 

          7.8.1. Context-Independent Grammars 
          7.8.2. Derivation Trees 
          7.8.3. Applications of ICGs 
          7.8.4. Ambiguity in Grammars and Languages

7.9. Stack Automata and GICs 

           7.9.1. Definition of Stack Automata 
           7.9.2. Languages Accepted by a Stacked Automata 
           7.9.3. Equivalence Between Stacked Automata and GICs 
           7.9.4. Deterministic Stacked Automata 

7.10. Normal Forms, Pumping Lemma of GICs and Properties of LICs 
 
           7.10.1. Normal Forms of GICs 
           7.10.2. Pumping Lemma 
           7.10.3. Closure Properties of Languages 
           7.10.4. Decision Properties of LICs 

Module 8. Language Processors 

8.1. Introduction to the Compilation Process 

8.1.1. Compilation and Interpretation 
8.1.2. Compiler Execution Environment 
8.1.3. Analysis Process 
8.1.4. Synthesis Process 

8.2. Lexical Analyzer 

8.2.1. What Is a Lexical Analyzer? 
8.2.2. Implementation of the Lexical Analyzer 
8.2.3. Semantic Actions 
8.2.4. Error Recovery 
8.2.5. Implementation Issues 

8.3. Parsing 

8.3.1. What Is a Parser? 
8.3.2. Previous Concepts 
8.3.3. Top-Down Analyzers 
8.3.4. Bottom-Up Analyzers 

8.4. Top-Down Parsing and Bottom-Up Parsing 

8.4.1. LL Parser (1) 
8.4.2. LR Parser (0) 
8.4.3. Analyzer Example 

8.5. Advanced Bottom-Up Parsing 

8.5.1. SLR Parser 
8.5.2. LR Parser (1) 
8.5.3. LR Analyzer (k) 
8.5.4. LALR Parser 

8.6. Semantic Analysis (I) 

8.6.1. Syntax-Driven Translation 
8.6.2. Table of Symbols 

8.7. Semantic Analysis (II) 

8.7.1. Type Checking 
8.7.2. The Type Subsystem 
8.7.3. Type Equivalence and Conversions 

8.8. Code Generation and Execution Environment 

8.8.1. Design Aspects 
8.8.2. Execution Environment 
8.8.3. Memory Organization 
8.8.4. Memory Allocation 

8.9. Intermediate Code Generation 

8.9.1. Synthesis-Driven Translation 
8.9.2. Intermediate Representations 
8.9.3. Examples of Translations 

8.10. Code Optimization 

8.10.1. Register Allocation 
8.10.2. Elimination of Dead Assignments 
8.10.3. Compile-Time Execution 
8.10.4. Expression Reordering 
8.10.5. Loop Optimization 

Module 9. Computer Graphics and Visualization 

9.1. Color Theory 

9.1.1. Properties of Light 
9.1.2. Color Models 
9.1.3. The CIE Standard 
9.1.4. Profiling 

9.2. Output Primitives 

9.2.1. The Video Driver 
9.2.2. Line Drawing Algorithms 
9.2.3. Circle Drawing Algorithms 
9.2.4. Filling Algorithms 

9.3. 2D Transformations and 2D Coordinate Systems and 2D Clipping 

9.3.1. Basic Geometric Transformations 
9.3.2. Homogeneous Coordinates 
9.3.3. Inverse Transformation 
9.3.4. Composition of Transformations 
9.3.5. Other Transformations 
9.3.6. Coordinate Change 
9.3.7. 2D Coordinate Systems 
9.3.8. Coordinate Change 
9.3.9. Standardization 
9.3.10. Trimming Algorithms 

9.4. 3D Transformations 

9.4.1. Translation 
9.4.2. Rotation
9.4.3. Scaling 
9.4.4. Reflection 
9.4.5. Shearing 

9.5. Display and Change of 3D Coordinates 

9.5.1. 3D Coordinate Systems 
9.5.2. Visualization 
9.5.3. Coordinate Change 
9.5.4. Projection and Normalization 

9.6. 3D Projection and Clipping 

9.6.1. Orthogonal Projection 
9.6.2. Oblique Parallel Projection 
9.6.3. Perspective Projection 
9.6.4. 3D Clipping Algorithms 

9.7. Hidden Surface Removal 

9.7.1. Back-Face Removal 
9.7.2. Z-Buffer 
9.7.3. Painter Algorithm 
9.7.4. Warnock Algorithm 
9.7.5. Hidden Line Detection 

9.8. Interpolation and Parametric Curves 

9.8.1. Interpolation and Polynomial Approximation 
9.8.2. Parametric Representation 
9.8.3. Lagrange Polynomial 
9.8.4. Natural Cubic Splines 
9.8.5. Basic Functions 
9.8.6. Matrix Representation 

9.9. Bézier Curves 

9.9.1. Algebraic Construction 
9.9.2. Matrix Form 
9.9.3. Composition 
9.9.4. Geometric Construction 
9.9.5. Drawing Algorithm 

9.10. B-Splines 

9.10.1. The Local Control Problem 
9.10.2. Uniform Cubic B-Splines 
9.10.3. Basis Functions and Control Points 
9.10.4. Derivative to the Origin and Multiplicity 
9.10.5. Matrix Representation 
9.10.6. Non-Uniform B-Splines 

Module 10: Bio-Inspired Computing 

10.1. Introduction to Bio-Inspired Computing 

10.1.1. Introduction to Bio-Inspired Computing 

10.2. Social Adaptation Algorithms 

10.2.1. Bio-Inspired Computation Based on Ant Colonies 
10.2.2. Variants of Ant Colony Algorithms 
10.2.3. Particle Cloud Computing 

10.3. Genetic Algorithms 

10.3.1. General Structure 
10.3.2. Implementations of the Major Operators 

10.4. Space Exploration-Exploitation Strategies for Genetic Algorithms 

10.4.1. CHC Algorithm 
10.4.2. Multimodal Problems 

10.5. Evolutionary Computing Models (I) 

10.5.1. Evolutionary Strategies 
10.5.2. Evolutionary Programming 
10.5.3. Algorithms Based on Differential Evolution 

10.6. Evolutionary Computation Models (II) 

10.6.1. Evolutionary Models Based on Estimation of Distributions (EDA) 
10.6.2. Genetic Programming 

10.7. Evolutionary Programming Applied to Learning Problems 

10.7.1 Rule-Based Learning 
10.7.2 Evolutionary Methods in Instance Selection Problems 

10.8. Multi-Objective Problems 

10.8.1. Concept of Dominance 
10.8.2. Application of Evolutionary Algorithms to Multi-Objective Problems 

10.9. Neural Networks (I) 

10.9.1. Introduction to Neural Networks 
10.9.2. Practical Example with Neural Networks 

10.10. Neural Networks (II) 

10.10.1. Use Cases of Neural Networks in Medical Research 
10.10.2. Use Cases of Neural Networks in Economics 
10.10.3. Use Cases of Neural Networks in Computer Vision 

Module 11. Security in System Design and Development 

11.1. Information Systems 

11.1.1. Information System Domains 
11.1.2. Components of an Information System 
11.1.3. Activities of an Information System 
11.1.4. Life Cycle of an Information System 
11.1.5. Information System Resources 

11.2. IT Systems: Typology 

11.2.1. Types of Information Systems 

11.2.1.1. Enterprise 
11.2.1.2. Strategic 
11.2.1.3. According to the Scope of Application 
11.2.1.4. Specific 

11.2.2. IT Systems: Real Examples 
11.2.3. Evolution of Information Systems: Stages 
11.2.4. Information Systems Methodologies 

11.3. Security of Information Systems: Legal Implications 

11.3.1. Data Access 
11.3.2. Security Threats: Vulnerabilities 
11.3.3. Legal Implications: Crimes 
11.3.4. Information System Maintenance Procedures 

11.4. Security of an Information System: Security Protocols 

11.4.1. Security of an Information System 

11.4.1.1. Integrity 
11.4.1.2. Confidentiality 
11.4.1.3. Availability 
11.4.1.4. Authentication 

11.4.2. Security Services 
11.4.3. Information Security Protocols: Typology 
11.4.4. Sensitivity of an Information System 

11.5. Security in an Information System: Access Control Measures and Systems 

11.5.1. Safety Measures 
11.5.2. Type of Security Measures 

11.5.2.1. Prevention 
11.5.2.2. Detection 
11.5.2.3. Correction 

11.5.3. Access Control Systems. Typology 
11.5.4. Cryptography 

11.6. Network and Internet Security 

11.6.1. Firewalls 
11.6.2. Digital Identification 
11.6.3. Viruses and Worms 
11.6.4. Hacking 
11.6.5. Examples and Real Cases 

11.7. Computer Crimes 

11.7.1. Computer Crime 
11.7.2. Computer Crimes Typology 
11.7.3. Computer Crime: Attacks. Typology 
11.7.4. The Case of Virtual Reality 
11.7.5. Profiles of Offenders and Victims: Typification of the Crime 
11.7.6. Computer Crimes: Examples and Real Cases 

11.8. Security Plan in an Information System 

11.8.1. Security Plan: Objectives 
11.8.2. Security Plan: Planning 
11.8.3. Risk Plan: Analysis 
11.8.4. Security Policy: Implementation in the Organization 
11.8.5. Security Plan: Implementation in the Organization 
11.8.6. Security Procedures. Types 
11.8.7. Security Plans: Examples: 

11.9. Contingency Plan 

11.9.1. Contingency Plan: Functions 
11.9.2. Emergency Plan: Elements and Objectives 
11.9.3. Contingency Plan in the Organization: Implementation 
11.9.4. Contingency Plans: Examples: 

11.10. Information Systems Security Governance 

11.10.1. Legal Regulations 
11.10.2. Standards 
11.10.3. Certifications 
11.10.4. Technologies 

Module 12. Information Security Architectures and Models 

12.1. Information Security Architecture 

12.1.1. SGSI/PDS 
12.1.2. Strategic Alignment 
12.1.3. Risk Management 
12.1.4. Performance Measurement 

12.2. Information Security Models 

12.2.1. Based on Security Policies 
12.2.2. Based on Protection Tools 
12.2.3. Based on Work Teams 

12.3. Safety Model: Key Components 

12.3.1. Identification of Risks 
12.3.2. Definition of Controls 
12.3.3. Continuous Assessment of Risk Levels 
12.3.4. AwarEness-Raising Plan for Employees, Suppliers, Partners, etc

12.4. Risk Management Process 

12.4.1. Asset Identification 
12.4.2. Threat Identification 
12.4.3. Risk Assessment 
12.4.4. Prioritization of Controls 
12.4.5. Re-Evaluation and Residual Risk 

12.5. Business Processes and Information Security 

12.5.1. Business Processes 
12.5.2. Risk Assessment Based on Business Parameters 
12.5.3. Business Impact Analysis 
12.5.4. Business Operations and Information Security 

12.6. Continuous Improvement Process 

12.6.1. The Deming Cycle 

12.6.1.1. Planning 
12.6.1.2. Do 
12.6.1.3. Verify 
12.6.1.4. Act 

12.7. Security Architectures 

12.7.1. Selection and Homogenization of Technologies 
12.7.2. Identity Management: Authentication 
12.7.3. Access Management: Authorization 
12.7.4. Network Infrastructure Security 
12.7.5. Encryption Technologies and Solutions 
12.7.6. Terminal Equipment Security (EDR) 

12.8. Regulatory Framework 

12.8.1. Sectoral Regulations 
12.8.2. Certifications 
12.8.3. Legislation 

12.9. The ISO 27001 Standard 

12.9.1. Implementation 
12.9.2. Certification 
12.9.3. Audits and Penetration Tests 
12.9.4. Continuous Risk Management 
12.9.5. Classification of Information 

12.10. Privacy Legislation. GDPR 

12.10.1. Scope of the General Data Protection Regulation (GDPR)
12.10.2. Personal Data 
12.10.3. Roles in the Processing of Personal Data 
12.10.4. ARCO Rights 
12.10.5. The DPO: Functions 

Module 13. IT Security Management 

13.1. Safety Management 

13.1.1. Security Operations 
13.1.2. Legal and Regulatory Aspects 
13.1.3. Business Qualification 
13.1.4. Risk Management
13.1.5. Identity and Access Management 

13.2. Structure of the Security Area: The CISO’s Office 

13.2.1. Organizational Structure: Position of the CISO in the Structure 
13.2.2. Lines of Defense 
13.2.3. Organizational Chart of the CISO’s Office 
13.2.4. Budget Management 

13.3. Security Governance 

13.3.1. Safety Committee 
13.3.2. Risk Monitoring Committee 
13.3.3. Audit Committee 
13.3.4. Crisis Committee 

13.4. Security Governance. Functions 

13.4.1. Policies and Standards 
13.4.2. Security Master Plan 
13.4.3. Control Panels 
13.4.4. Awareness and Education 
13.4.5. Supply Chain Security 

13.5. Security Operations 

13.5.1. Identity and Access Management 
13.5.2. Configuration of Network Security Rules: Firewalls 
13.5.3. IDS/IPS Platform Management 
13.5.4. Vulnerability Analysis 

13.6. Cybersecurity Framework: NIST CSF 

13.6.1. NIST Methodology 

13.6.1.1. Identify 
13.6.1.2. Protect 
13.6.1.3. Detect 
13.6.1.4. Respond 
13.6.1.5. Retrieve 

13.7. Security Operations Center (SOC). Functions 

13.7.1. Protection Red Team, Pentesting, Threat Intelligence 
13.7.2. Detection. SIEM, User Behavior Analytics, Fraud Prevention 
13.7.3. Response 

13.8. Security Audits 

13.8.1. Intrusion Test 
13.8.2. Red Team Exercises 
13.8.3. Source Code Audits: Secure Development 
13.8.4. Component Safety (Software Supply Chain) 
13.8.5. Forensic Analysis 

13.9. Incident Response 

13.9.1. Preparation 
13.9.2. Detection, Analysis and Notification 
13.9.3. Containment, Eradication and Recovery 
13.9.4. Post-Incident Activity 

13.9.4.1. Evidence Retention 
13.9.4.2. Forensic Analysis 
13.9.4.3. Gap Management 

13.9.5. Official Cyber-Incident Management Guidelines 

13.10. Vulnerability Management 

13.10.1. Vulnerability Analysis 
13.10.2. Vulnerability Assessment 
13.10.3. System Basing 
13.10.4. Zero-Day Vulnerabilities. Zero-Day 

Module 14. Risk Analysis and IT Security Environment 

14.1. Environment Analysis 

14.1.1. Economic Situation Analysis 

14.1.1.1. VUCA Environments 

14.1.1.1.1. Volatile 
14.1.1.1.2. Uncertain 
14.1.1.1.3. Complex 
14.1.1.1.4. Ambiguous 

14.1.1.2. BANI Environments 

14.1.1.2.1. Brittle 
14.1.1.2.2. Anxious 
14.1.1.2.3. Non-Linear 
14.1.1.2.4. Incomprehensible 

14.1.2. General Environment Analysis: PESTEL 

14.1.2.1. Politics 
14.1.2.2. Economics 
14.1.2.3. Social 
14.1.2.4. Technological 
14.1.2.5. Ecological/Environmental 
14.1.2.6. Legal 

14.1.3. Internal Situation Analysis: SWOT Analysis 

14.1.3.1. Objectives 
14.1.3.2. Threats 
14.1.3.3. Opportunities 
14.1.3.4. Strengths 

14.2. Risk and Uncertainty 

14.2.1. Risk 
14.2.2. Risk Management 
14.2.3. Risk Management Standards 

14.3. ISO 31.000:2018 Risk Management Guidelines 

14.3.1. Object 
14.3.2. Principles 
14.3.3. Frame of Reference 
14.3.4. Process 

14.4. Information Systems Risk Analysis and Management Methodology (MAGERIT)

14.4.1. MAGERIT Methodology 

14.4.1.1. Objectives 
14.4.1.2. Method 
14.4.1.3. Components 
14.4.1.4. Techniques 
14.4.1.5. Available Tools (PILAR) 

14.5. Cyber Risk Transfer 

14.5.1. Risk Transfer 
14.5.2. Cyber Risks. Typology 
14.5.3. Cyber Risk Insurance 

14.6. Agile Methodologies for Risk Management 

14.6.1. Agile Methodologies 
14.6.2. Scrum for Risk Management 
14.6.3. Agile Risk Management 

14.7. Technologies for Risk Management 

14.7.1. Artificial Intelligence Applied to Risk Management 
14.7.2. Blockchain and Cryptography: Value Preservation Methods 
14.7.3. Quantum Computing: Opportunity or Threat 

14.8. IT Risk Mapping Based on Agile Methodologies 

14.8.1. Representation of Probability and Impact in Agile Environments
14.8.2. Risk as a Threat to Value 
14.8.3. Re-Evolution in Project Management and Agile Processes based on KRIs 

14.9. Risk-Driven in Risk Management 

14.9.1. Risk-Driven 
14.9.2. Risk-Driven in Risk Management 
14.9.3. Development of a Risk-Driven Business Management Model 

14.10. Innovation and Digital Transformation in IT Risk Management 

14.10.1. Agile Risk Management as a Source of Business Innovation 
14.10.2. Transformation of Data into Useful Information for Decision-Making 
14.10.3. Holistic View of the Enterprise through Risk 

Module 15. Cryptography in IT 

15.1. Cryptography 

15.1.1. Cryptography 
15.1.2. Fundamentals of Mathematics 

15.2. Cryptology 

15.2.1. Cryptology 
15.2.2. Cryptanalysis 
15.2.3. Steganography and Stegoanalysis 

15.3. Cryptographic Protocols 

15.3.1. Basic Blocks 
15.3.2. Basic Protocols 
15.3.3. Intermediate Protocols 
15.3.4. Advanced Protocol 
15.3.5. Esoteric Protocols 

15.4. Cryptographic Techniques 

15.4.1. Key Length 
15.4.2. Key Management 
15.4.3. Types of Algorithms 
15.4.4. Summary of Functions: Hash 
15.4.5. Pseudo-Random Number Generators 
15.4.6. Use of Algorithms 

15.5. Symmetric Cryptography 

15.5.1. Block Ciphers 
15.5.2. DES (Data Encryption Standard) 
15.5.3. RC4 Algorithm 
15.5.4. AES (Advanced Encryption Standard) 
15.5.5. Combination of Block Ciphers 
15.5.6. Key Derivation 

15.6. Asymmetric Cryptography 

15.6.1. Diffie-Hellman 
15.6.2. DSA (Digital Signature Algorithm) 
15.6.3. RSA (Rivest, Shamir and Adleman) 
15.6.4. Elliptic Curve 
15.6.5. Asymmetric Cryptography: Typology 

15.7. Digital Certificates 

15.7.1. Digital Signature 
15.7.2. X509 Certificates 
15.7.3. Public Key Infrastructure (PKI) 

15.8. Implementations 

15.8.1. Kerberos 
15.8.2. IBM CCA 
15.8.3. Pretty Good Privacy (PGP) 
15.8.4. ISO Authentication Framework 
15.8.5. SSL and TLS 
15.8.6. Smart Cards in Means of Payment (EMV) 
15.8.7. Mobile Telephony Protocols 
15.8.8. Blockchain 

15.9. Steganography 

15.9.1. Steganography 
15.9.2. Stegoanalysis 
15.9.3. Applications and Uses 

15.10. Quantum Cryptography 

15.10.1. Quantum Algorithms 
15.10.2. Protection of Algorithms from Quantum Computing 
15.10.3. Quantum Key Distribution 

Module 16. Identity and Access Management in IT security 

16.1. Identity and Access Management (IAM) 

16.1.1. Digital Identity 
16.1.2. Identity Management 
16.1.3. Identity Federation 

16.2. Physical Access Control 

16.2.1. Protection Systems 
16.2.2. Area Security 
16.2.3. Recovery Facilities 

16.3. Logical Access Control 

16.3.1. Authentication: Typology 
16.3.2. Authentication Protocols 
16.3.3. Authentication Attacks 

16.4. Logical Access Control: MFA Authentication 

16.4.1. Logical Access Control: MFA Authentication 
16.4.2. Passwords: Importance 
16.4.3. Authentication Attacks 

16.5. Logical Access Control: Biometric Authentication 

16.5.1. Logical Access Control: Biometric Authentication 

16.5.1.1. Biometric Authentication: Requirements 

16.5.2. Operation 
16.5.3. Models and Techniques 

16.6. Authentication Management Systems 

16.6.1. Single Sign On 
16.6.2. Kerberos 
16.6.3. AAA Systems 

16.7. Authentication Management Systems: AAA Systems 

16.7.1. TACACS 
16.7.2. RADIUS 
16.7.3. DIAMETER 

16.8. Access Control Services 

16.8.1. FW - Firewall 
16.8.2. VPN - Virtual Private Networks 
16.8.3. IDS - Intrusion Detection System 

16.9. Network Access Control Systems 

16.9.1. NAC 
16.9.2. Architecture and Elements 
16.9.3. Operation and Standardization 

16.10. Wireless Network Access 

16.10.1. Types of Wireless Networks 
16.10.2. Wireless Network Security 
16.10.3. Wireless Network Attacks 

Module 17. Security in Communications and Software Operation 

17.1. Computer Security in Communications and Software Operation 

17.1.1. Computer Security 
17.1.2. Cybersecurity 
17.1.3. Cloud Security 

17.2. Computer Security in Communications and Software Operation: Typology 

17.2.1. Physical Security 
17.2.2. Logical Security 

17.3. Communications Security 

17.3.1. Main Elements 
17.3.2. Network Security 
17.3.3. Best Practices 

17.4. Cyberintelligence 

17.4.1. Social Engineering 
17.4.2. Deep Web 
17.4.3. Phishing 
17.4.4. Malware 

17.5. Secure Development in Communications and Software Operation 

17.5.1. Secure Development: HTTP Protocol 
17.5.2. Secure Development. Life Cycle 
17.5.3. Secure Development: PHP Security 
17.5.4. Secure Development: NET Security 
17.5.5. Secure Development: Best Practices 

17.6. Information Security Management Systems in Communications and Software Operation

17.6.1. GDPR 
17.6.2. ISO 27021 
17.6.3. ISO 27017/18 

17.7. SIEM Technologies 

17.7.1. SIEM Technologies 
17.7.2. SOC Operation 
17.7.3. SIEM Vendors 

17.8. The Role of Security in Organizations 

17.8.1. Roles in Organizations 
17.8.2. Role of IoT Specialists in Companies 
17.8.3. Recognized Certifications in the Market 

17.9. Forensic Analysis 

17.9.1. Forensic Analysis 
17.9.2. Forensic Analysis: Methodology 
17.9.3. Forensic Analysis: Tools and Implementation 

17.10. Cybersecurity Today 

17.10.1. Major Cyber-Attacks 
17.10.2. Employability Forecasts 
17.10.3. Challenges 

Module 18. Security in Cloud Environments 

18.1. Security in Cloud Computing Environments 

18.1.1. Security in Cloud Computing Environments 
18.1.2. Security in Cloud Computing Environments. Threats and Security Risks 
18.1.3. Security in Cloud Computing Environments. Key Security Aspects 

18.2. Types of Cloud Infrastructure 

18.2.1. Public 
18.2.2. Private 
18.2.3. Hybrid 

18.3. Shared Management Model 

18.3.1. Security Elements Managed by Vendor 
18.3.2. Elements Managed by Customer 
18.3.3. Definition of the Security Strategy 

18.4. Prevention Mechanisms 

18.4.1. Authentication Management Systems 
18.4.2. Authorization Management System: Access Policies 
18.4.3. Key Management Systems 

18.5. System Securitization 

18.5.1. Storage System Securitization 
18.5.2. Database System Protection 
18.5.3. Securitization of Data in Transit 

18.6. Infrastructure Protection 

18.6.1. Secure Network Design and Implementation 
18.6.2. Security in Computing Resources 
18.6.3. Tools and Resources for Infrastructure Protection 

18.7. Detection of Threats and Attacks 

18.7.1. Auditing, Logging and Monitoring Systems 
18.7.2. Event and Alarm Systems 
18.7.3. SIEM Systems 

18.8. Incident Response 

18.8.1. Incident Response Plan 
18.8.2. Business Continuity 
18.8.3. Forensic Analysis and Remediation of Incidents of the Same Nature

18.9. Security in Public Clouds 

18.9.1. AWS (Amazon Web Services) 
18.9.2. Microsoft Azure 
18.9.3. Google GCP 
18.9.4. Oracle Cloud 

18.10. Regulations and Compliance 

18.10.1. Security Compliance 
18.10.2. Risk Management 
18.10.3. People and Process in Organizations 

Module 19. Security in IoT Device Communications 

19.1. From Telemetry to IoT 

19.1.1. Telemetry 
19.1.2. M2M Connectivity 
19.1.3. Democratization of Telemetry 

19.2. IoT Reference Models 

19.2.1. IoT Reference Model 
19.2.2. Simplified IoT Architecture 

19.3. IoT Security Vulnerabilities 

19.3.1. IoT Devices 
19.3.2. IoT Devices. Usage Case Studies 
19.3.3. IoT Devices: Vulnerabilities 

19.4. IoT Connectivity 

19.4.1. PAN, LAN, WAN Networks 
19.4.2. Non-IoT Wireless Technologies 
19.4.3. LPWAN Wireless Technologies 

19.5. LPWAN Technologies 

19.5.1. The Iron Triangle of LPWAN Networks 
19.5.2. Free Frequency Bands vs. Licensed Bands 
19.5.3. LPWAN Technology Options 

19.6. LoRaWAN Technology 

19.6.1. LoRaWAN Technology 
19.6.2. LoRaWAN Use Cases. Ecosystem 
19.6.3. LoRaWAN Security 

19.7. Sigfox Technology 

19.7.1. Sigfox Technology 
19.7.2. Sigfox Use Cases: Ecosystem 
19.7.3. Sigfox Security 

19.8. IoT Cellular Technology 

19.8.1. IoT Cellular Technology (NB-IoT and LTE-M) 
19.8.2. IoT Cellular Use Cases: Ecosystem 
19.8.3. IoT Cellular Security 

19.9. Wi-SUN Technology 

19.9.1. Wi-SUN Technology 
19.9.2. Wi-SUN Use Cases: Ecosystem 
19.9.3. Wi-SUN Security 

19.10. Other IoT Technologies 

19.10.1. Other IoT Technologies 
19.10.2. Use Cases and Ecosystem of Other IoT Technologies 
19.10.3. Security in Other IoT Technologies 

Module 20. Business Continuity Plan Associated with Security 

20.1. Business Continuity Plans 

20.1.1. Business Continuity Plans (BCP) 
20.1.2. Business Continuity Plans (BCP) Key Aspects 
20.1.3. Business Continuity Plan (BCP) for Business Valuation 

20.2. Metrics in a Business Continuity Plan (BCP)

20.2.1. Recovery Time Objective (RTO) and Recovery Point Objective (RPO) 
20.2.2. Maximum Tolerable Time (MTD) 
20.2.3. Minimum Recovery Levels (ROL) 
20.2.4. Recovery Point Objective (RPO) 

20.3. Continuity Projects. Typology 

20.3.1. Business Continuity Plan (BCP) 
20.3.2. ICT Continuity Plan (ICTCP)
20.3.3. Disaster Recovery Plan (DRP) 

20.4. Risk Management Associated with the BCP 

20.4.1. Business Impact Analysis 
20.4.2. Benefits of Implementing a BCP 
20.4.3. Risk-Based Mentality 

20.5. Life Cycle of a Business Continuity Plan 

20.5.1. Phase 1: Analysis of the Organization 
20.5.2. Phase 2: Determination of the Continuity Strategy 
20.5.3. Phase 3: Contingency Response 
20.5.4. Phase 4: Testing, Maintenance and Review 

20.6. Organizational Analysis Phase of a BCP 

20.6.1. Identification of Processes in the Scope of the BCP 
20.6.2. Identification of Critical Business Areas 
20.6.3. Identification of Dependencies Between Areas and Processes 
20.6.4. Determination of Appropriate BAT 
20.6.5. Deliverables: Creation of a Plan 

20.7. Determination Phase of the Continuity Strategy in a BCP 

20.7.1. Roles in the Strategy Determination Phase 
20.7.2. Tasks in the Strategy Determination Phase 
20.7.3. Deliverables 

20.8. Contingency Response Phase of a BCP 

20.8.1. Roles in the Response Phase 
20.8.2. Tasks in This Phase 
20.8.3. Deliverables 

20.9. Testing, Maintenance and Revision Phase of a BCP 

20.9.1. Roles in the Testing, Maintenance and Review Phase 
20.9.2. Tasks in the Testing, Maintenance and Review Phase 
20.9.3. Deliverables 

20.10. ISO Standards Associated with Business Continuity Plans (BCP) 

20.10.1. ISO 22301:2019 
20.10.2. ISO 22313:2020 
20.10.3. Other Related ISO and International Standards 

Module 21. Data Analysis in a Business Organization 

21.1. Business Analysis 

21.1.1. Business Analysis 
21.1.2. Data Structure 
21.1.3. Phases and Elements 

21.2. Data Analysis in the Business 

21.2.1. Scorecards and KPIs by Departments 
21.2.2. Operational, Tactical and Strategic Reports 
21.2.3. Data Analysis Applied to Each Department 

21.2.3.1. Marketing and Communication 
21.2.3.2. Commercial 
21.2.3.3. Customer Service 
21.2.3.4. Purchasing 
21.2.3.5. Administration 
21.2.3.6. Human Resources 
21.2.3.7. Production 
21.2.3.8. IT 

21.3. Marketing and Communication 

21.3.1. KPIs to be Measured, Applications and Benefits 
21.3.2. Marketing Systems and Data Warehouse 
21.3.3. Implementation of a Data Analysis Framework in Marketing 
21.3.4. Marketing and Communication Plan 
21.3.5. Strategies, Prediction and Campaign Management 

21.4. Commerce and Sales 

21.4.1. Contributions of Data Analysis in the Commercial Area 
21.4.2. Sales Department Needs 
21.4.3. Market Research 

21.5. Customer Service 

21.5.1. Loyalty 
21.5.2. Personal Coaching and Emotional Intelligence 
21.5.3. Customer Satisfaction 

21.6. Purchasing 

21.6.1. Data Analysis for Market Research 
21.6.2. Data Analysis for Competency Research 
21.6.3. Other Applications 

21.7. Administration 

21.7.1. Needs of the Administration Department 
21.7.2. Data Warehouse and Financial Risk Analysis 
21.7.3. Data Warehouse and Credit Risk Analysis 

21.8. Human Resources 

21.8.1. HR and the Benefits of Data Analysis 
21.8.2. Data Analysis Tools in the HR Department 
21.8.3. Data Analysis Applications in the HR Department 

21.9. Production  

21.9.1. Data Analysis in a Production Department 
21.9.2. Applications 
21.9.3. Benefits 

21.10. IT 

21.10.1. IT Department 
21.10.2. Data Analysis and Digital Transformation 
21.10.3. Innovation and Productivity 

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

22.1. Statistics. Variables, Indices and Ratios 

22.1.1. Statistics 
22.1.2. Statistical Dimensions 
22.1.3. Variables, Indices and Ratios 

22.2. Type of Data 

22.2.1. Qualitative 
22.2.2. Quantitative 
22.2.3. Characterization and Categories 

22.3. Data Knowledge from Measurements 

22.3.1. Centralization Measurements 
22.3.2. Measures of Dispersion 
22.3.3. Correlation 

22.4. Knowledge of Data from Graphs 

22.4.1. Visualization According to Type of Data 
22.4.2. Interpretation of Graphic Information 
22.4.3. Customization of graphics with R 

22.5. Probability 

22.5.1. Probability 
22.5.2. Function of Probability 
22.5.3. Distributions 

22.6. Data Collection 

22.6.1. Methodology of Data Collection 
22.6.2. Data Collection Tools 
22.6.3. Data Collection Channels 

22.7. Data Cleaning 

22.7.1. Phases of Data Cleansing 
22.7.2. Data Quality 
22.7.3. Data Manipulation (With R) 

22.8. Data Analysis, Interpretation and Evaluation of Results 

22.8.1. Statistical Measures 
22.8.2. Relationship Indices 
22.8.3. Data Mining 

22.9. Data Warehouse 

22.9.1. Components  
22.9.2. Design 

22.10. Data Availability 

22.10.1. Access 
22.10.2. Uses 
22.10.3. Security 

Module 23. IoT Devices and Platforms as the Basis for Data Science

23.1. Internet of Things 

23.1.1. Internet of the Future, Internet of Things 
23.1.2. The Industrial Internet Consortium 

23.2. Architecture of Reference 

23.2.1. The Architecture of Reference 
23.2.2. Layers 
23.2.3. Components 

23.3. Sensors and IoT Devices 

23.3.1 Main Components 
23.3.2. Sensors and Actuators 

23.4. Communications and Protocols 

23.4.1. Protocols. OSI Model 
23.4.2. Communication Technologies 

23.5. Cloud Platforms for IoT and IIoT 

23.5.1. General Purpose Platforms 
23.5.2. Industrial Platforms 
23.5.3. Open Code Platforms 

23.6. Data Management on IoT Platforms 

23.6.1. Data Management Mechanisms. Open Data 
23.6.2. Data Exchange and Visualization 

23.7. IoT Security 

23.7.1. Requirements and Security Areas 
23.7.2. Security Strategies in IIoT 

23.8. Applications of IoT 

23.8.1. Intelligent Cities 
23.8.2. Health and Fitness 
23.8.3. Smart Home 
23.8.4. Other Applications 

23.9. Applications of IIoT 

23.9.1. Fabrication 
23.9.2. Transport 
23.9.3. Energy 
23.9.4. Agriculture and Livestock 
23.9.5. Other Sectors 

23.10. Industry 4.0 

23.10.1. IoRT (Internet of Robotics Things) 
23.10.2. 3D Additive Manufacturing 
23.10.3. Big Data Analysis 

Module 24. Graphical Representation of Data Analysis 

24.1. Exploratory Analysis 

24.1.1. Representation for Information Analysis 
24.1.2. The Value of Graphical Representation 
24.1.3. New Paradigms of Graphical Representation 

24.2. Optimization for Data Science 

24.2.1. Color Range and Design 
24.2.2. Gestalt in Graphic Representation 
24.2.3. Errors to Avoid and Advice 

24.3. Basic Data Sources 

24.3.1. For Quality Representation 
24.3.2. For Quantity Representation 
24.3.3. For Time Representation 

24.4. Complex Data Sources 

24.4.1. Files, Lists and Databases 
24.4.2. Open Data 
24.4.3. Continuous Data Generation 

24.5. Types of Graphs 

24.5.1. Basic Representations 
24.5.2. Block Representation 
24.5.3. Representation for Dispersion Analysis 
24.5.4. Circular Representations 
24.5.5. Bubble Representations 
24.5.6. Geographical Representations 

24.6. Types of Visualization 

24.6.1. Comparative and Relational 
24.6.2. Distribution 
24.6.3. Hierarchical 

24.7. Report Design with Graphic Representation 

24.7.1. Application of Graphs in Marketing Reports 
24.7.2. Application of Graphs in Scorecards and KPI’s 
24.7.3. Application of Graphs in Strategic Plans 
24.7.4. Other Uses: Science, Health, Business 

24.8. Graphic Narration 

24.8.1. Graphic Narration 
24.8.2. Evolution 
24.8.3. Uses 

24.9. Tools Oriented Towards Visualization 

24.9.1. Advanced Tools 
24.9.2. Online Software 
24.9.3. Open Source 

24.10. New Technologies in Data Visualization 

24.10.1. Systems for Virtualization of Reality 
24.10.2. Reality Enhancement and Improvement Systems 
24.10.3. Intelligent Systems 

Module 25. Data Science Tools 

25.1. Data Science 

25.1.1. Data Science 
25.1.2. Advanced Tools for the Data Scientist 

25.2. Data, Information and Knowledge 

25.2.1. Data, Information and Knowledge 
25.2.2. Types of Data 
25.2.3. Data Sources 

25.3. From Data to Information 

25.3.1. Data Analysis 
25.3.2. Types of Analysis 
25.3.3. Extraction of Information from a Dataset 

25.4. Extraction of Information Through Visualization 

25.4.1. Visualization as an Analysis Tool 
25.4.2. Visualization Methods 
25.4.3. Visualization of a Data Set 

25.5. Data Quality 

25.5.1. Quality Data 
25.5.2. Data Cleansing 
25.5.3. Basic Data Pre-Processing 

25.6. Dataset 

25.6.1. Dataset Enrichment 
25.6.2. The Curse of Dimensionality 
25.6.3. Modification of Our Data Set 

25.7. Imbalance 

25.7.1. Class Imbalance 
25.7.2. Imbalance Mitigation Techniques
25.7.3. Balancing a Dataset 

25.8. Unsupervised Models 

25.8.1. Unsupervised Model 
25.8.2. Methods 
25.8.3. Classification with Unsupervised Models 

25.9. Supervised Models 

25.9.1. Supervised Model 
25.9.2. Methods 
25.9.3. Classification with Supervised Models 

25.10. Tools and Good Practices 

25.10.1. Good Practices for Data Scientists 
25.10.2. The Best Model 
25.10.3. Useful Tools 

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

26.1. Statistical Inference 

26.1.1. Descriptive Statistics vs. Statistical Inference 
26.1.2. Parametric Procedures 
26.1.3. Non-Parametric Procedures 

26.2. Exploratory Analysis 

26.2.1. Descriptive Analysis 
26.2.2. Visualization 
26.2.3. Data Preparation 

26.3. Data Preparation 

26.3.1. Integration and Data Cleaning 
26.3.2. Data Standardization 
26.3.3. Transforming Attributes 

26.4. Missing Values 

26.4.1. Treatment of Missing Values 
26.4.2. Maximum Likelihood Imputation Methods 
26.4.3. Missing Value Imputation Using Machine Learning 

26.5. Noise in the Data 

26.5.1. Noise Classes and Attributes 
26.5.2. Noise Filtering 
26.5.3. The Effect of Noise 

26.6. The Curse of Dimensionality 

26.6.1. Oversampling 
26.6.2. Undersampling 
26.6.3. Multidimensional Data Reduction 

26.7. From Continuous to Discrete Attributes 

26.7.1. Continuous vs. Discrete 
26.7.2. Discretization Process 

26.8. The Data 

26.8.1. Data Selection 
26.8.2. Prospects and Selection Criteria
26.8.3. Selection Methods 

26.9. Instance Selection 

26.9.1. Methods for Instance Selection 
26.9.2. Prototype Selection 
26.9.3. Advanced Methods for Instance Selection 

26.10. Data Pre-Processing in Big Data Environments 

26.10.1. Big Data 
26.10.2. Classical vs. Massive Pre-Processing 
26.10.3. Smart Data 

Module 27. Predictability and Analysis of Stochastic Phenomena 

27.1. Time Series 

27.1.1. Time Series 
27.1.2. Utility and Applicability 
27.1.3. Related Case Studies 

27.2. Time Series 

27.2.1. Seasonal Trend of ST 
27.2.2. Typical Variations 
27.2.3. Waste Analysis 

27.3. Typology 

27.3.1. Stationary 
27.3.2. Non-Stationary 
27.3.3. Transformations and Settings 

27.4. Time Series Schemes 

27.4.1. Additive Scheme (Model) 
27.4.2. Multiplicative Scheme (Model) 
27.4.3. Procedures to Determine the Type of Model 

27.5. Basic Forecast Methods 

27.5.1. Media 
27.5.2. Naïve 
27.5.3. Seasonal Naivety 
27.5.4. Method Comparison 

27.6. Waste Analysis 

27.6.1. Autocorrelation 
27.6.2. ACF of Waste 
27.6.3. Correlation Test 

27.7. Regression in the Context of Time Series 

27.7.1. ANOVA 
27.7.2. Fundamentals 
27.7.3. Practical Applications 

27.8. Predictive Methods of Time Series 

27.8.1. ARIMA 
27.8.2. Exponential Smoothing 

27.9. Manipulation and Analysis of Time Series with R 

27.9.1. Data Preparation 
27.9.2. Identification of Patterns 
27.9.3. Model Analysis 
27.9.4. Prediction 

27.10. Combined Graphical Analysis with R 

27.10.1. Normal Situations 
27.10.2. Practical Application for the Resolution of Simple Problems 
27.10.3. Practical Application for the Resolution of Advanced Problems 

Module 28. Design and Development of Intelligent Systems 

28.1. Data Pre-Processing 

28.1.1. Data Pre-Processing 
28.1.2. Data Transformation 
28.1.3. Data Mining 

28.2. Machine Learning 

28.2.1. Supervised and Unsupervised Learning 
28.2.2. Reinforcement Learning 
28.2.3. Other Learning Paradigms 

28.3. Classification Algorithms 

28.3.1. Inductive Machine Learning 
28.3.2. SVM and KNN 
28.3.3. Metrics and Scores for Ranking 

28.4. Regression Algorithms 

28.4.1. Lineal Regression, Logistical Regressions and Non-Lineal Models 
28.4.2. Time Series 
28.4.3. Metrics and Scores for Regression 

28.5. Clustering Algorithms 

28.5.1. Hierarchical Clustering Techniques 
28.5.2. Partitional Clustering Techniques 
28.5.3. Metrics and Scores for Clustering 

28.6. Association Rules Techniques 

28.6.1. Methods for Rule Extraction 
28.6.2. Metrics and Scores for Association Rule Algorithms 

28.7. Advanced Classification Techniques: Multiclassifiers 

28.7.1. Bagging Algorithms 
28.7.2. Random Forests Sorter 
28.7.3. “Boosting” for Decision Trees 

28.8. Probabilistic Graphical Models 

28.8.1. Probabilistic Models 
28.8.2. Bayesian Networks. Properties, Representation and Parameterization 
28.8.3. Other Probabilistic Graphical Models 

28.9. Neural Networks 

28.9.1. Machine Learning with Artificial Neural Networks 
28.9.2. Feedforward Networks 

28.10. Deep Learning 

28.10.1. Deep Feedforward Networks 
28.10.2. Convolutional Neural Networks and Sequence Models 
28.10.3. Tools for Implementing Deep Neural Networks 

Module 29. Data-Intensive Systems and Architectures 

29.1. Non-Functional Requirements: Pillars of Big Data Applications 

29.1.1. Reliability 
29.1.2. Adaptation 
29.1.3. Maintainability 

29.2. Data Models 

29.2.1. Relational Model 
29.2.2. Document Model 
29.2.3. Graph Data Model 

29.3. Databases: Data Storage and Retrieval Management 

29.3.1. Hash Index 
29.3.2. Log Structured Storage 
29.3.3. B-Trees 

29.4. Data Encoding Formats 

29.4.1. Language-Specific Formats 
29.4.2. Standardized Formats 
29.4.3. Binary Coding Formats 
29.4.4. Data Flow Between Processes 

29.5. Replication 

29.5.1. Objectives of Replication 
29.5.2. Replication Models 
29.5.3. Problems with Replication 

29.6. Distributed Transactions 

29.6.1. Transaction 
29.6.2. Protocols for Distributed Transactions
29.6.3. Serializable Transactions 

29.7. Partitions 

29.7.1. Forms of Partitioning 
29.7.2. Secondary Index Interaction and Partitioning 
29.7.3. Partition Rebalancing 

29.8. Offline Data Processing 

29.8.1. Batch Processing 
29.8.2. Distributed File Systems 
29.8.3. MapReduce 

29.9 Data Processing in Real Time 

29.9.1. Types of Message Brokers 
29.9.2. Representation of Databases as Data Streams 
29.9.3. Data Stream Processing 

29.10. Practical Applications in Business 

29.10.1. Consistency in Readings 
29.10.2. Holistic Approach to Data 
29.10.3. Scaling of a Distributed Service 

Module 30. Practical Application of Data Science in Business Sectors 

30.1. Health Sector 

30.1.1. Implications of AI and Data Analysis in the Health Sector 
30.1.2. Opportunities and Challenges 

30.2. Risks and Trends in the Health Sector  

30.2.1. Use in the Health Sector 
30.2.2. Potential Risks Related to the Use of AI 

30.3. Financial Services 

30.3.1. Implications of AI and Data Analysis in the Financial Services Industry 
30.3.2. Use in the Financial Services 
30.3.3. Potential Risks Related to the Use of AI 

30.4. Retail 

30.4.1. Implications of AI and Data Analysis in the Retail Sector 
30.4.2. Use in Retail 
30.4.3. Potential Risks Related to the Use of AI 

30.5. Industry 4.0 

30.5.1. Implications of AI and Data Analysis in Industry 4.0 
30.5.2. Use in Industry 4.0 

30.6. Risks and Trends in Industry 4.0  

30.6.1. Potential Risks Related to the Use of AI 

30.7. Public Administration 

30.7.1. Implications of AI and Data Analysis for Public Administration 
30.7.2. Use in Public Administration 
30.7.3. Potential Risks Related to the Use of AI 

30.8. Educational 

30.8.1. Implications of AI and Data Analysis in Education 
30.8.2. Potential Risks Related to the Use of AI 

30.9. Forestry and Agriculture 

30.9.1. Implications of AI and Data Analysis in Forestry and Agriculture 
30.9.2. Use in Forestry and Agriculture 
30.9.3. Potential Risks Related to the Use of AI 

30.10. Human Resources  

30.10.1. Implications of AI and Data Analysis for Human Resource Management 
30.10.2. Practical Applications in the Business World
30.10.3. Potential Risks Related to the Use of AI 

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