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

The computer science industry is critical in an increasingly digitized world, marking a significant impact on society and global economies. In this context, the field of cybersecurity and data analysis takes on special relevance, as it allows managing large volumes of information and protecting systems against increasingly sophisticated threats. High specialization in these areas is configured as a key factor to lead companies towards success and innovation.

The program in Computer Science, Cybersecurity and Data Analytics at TECH Global University addresses the essential concepts of these disciplines, taking the participant from the fundamentals to the most advanced applications. It covers aspects such as advanced programming, information security management systems, data science and architectures for intensive data handling, as well as including the latest trends in risk analysis and innovative technologies such as Blockchain and artificial intelligence.

One of the main advantages of this program is its 100% online modality, which allows students to organize their learning pace according to their needs, facilitating the reconciliation with other responsibilities. This methodology offers a comprehensive experience, designed to boost professional performance and respond to the demands of a sector in constant growth.

Strengthen the business fabric by managing cybersecurity strategically and effectively” 

This Advanced master’s degree in Computer Science, Cybersecurity and Data Analytics contains the most complete and up-to-date educational program on the market. Its most notable features are:

• The development of case studies presented by experts in Computer Science, Cybersecurity and Data Analytics
• 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 the self-assessment process can be carried out to improve learning
• Special emphasis on innovative methodologies in the management of Computer Science, Cybersecurity and Data Analytics Industries
• 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

Reinforce your theoretical knowledge with a multitude of practical resources included in this program” 

It includes in its teaching staff professionals belonging to the field of Cybersecurity and Data Analysis, who pour into this program the experience of their work, as well as recognized specialists from reference companies and prestigious universities.

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

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

Access the most innovative teaching methodology that TECH offers in today's academic landscape"

Study at your own pace with a 100% online program, available anytime, anywhere in the world"

The materials that make up this Advanced master’s degree have been developed by a team of experts in Technology, Computer Security and Data Science. Thanks to this, the curriculum addresses in depth the current challenges in Cybersecurity, the development of advanced solutions in artificial intelligence and the analysis of large volumes of data. With this, graduates will acquire key skills to identify vulnerabilities, design protection strategies and optimize processes through innovative techniques. In addition, this university program encourages the practical application of knowledge through real projects and cases in the technology sector. 

Develop advanced skills in Computer Science, Cybersecurity and Data Analytics to take your potential to the fullest”

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. Passing of Functions, Structures, and Arrays 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. 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 TADs
2.2.3. Vectors and Arrays

2.3. Linear data Structures

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

2.4. Hierarchical Data Structures

2.4.1. TAD 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. Priority Mounds and Queue

2.6.1. TAD Mounds
2.6.2. TAD 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. TAD Graph
2.8.2. Types of Graphs
2.8.3. Graphical Representation and Basic Operations
2.8.4. Graph Design

2.9. Algorithms and Advanced Graph Concepts

2.9.1. Problems about Graphs
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. Mathematical Analysis Criteria for 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. Mixed Sorting (merge_sort)
3.3.6. Quick Sorting (quick_sort)

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. Greedy Strategy Elements
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. Taboo Search
4.6.5. Candidate Searches

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's Method
4.9.3. The Secant Method

4.10. Parallel Algorithms

4.10.1. Parallel Binary Operations
4.10.2. Parallel Operations with Networks
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++
5.6.6. 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. Indecibility

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. The Classes P and NP
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. McCullogh 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 AFN
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. GIC Applications
7.8.4. Ambiguity in Grammars and Languages

7.9. Stack Automatons and GIC

7.9.1. Definition of Stack Automata
7.9.2. Languages Accepted by a Stack Automaton
7.9.3. Equivalence between Stack Automata and GICs
7.9.4. Deterministic Stack 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. Rules-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 Artificial 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. Information 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. Access to Data
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 Crimes. Attacks. Typology
11.7.4. The Case for 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. ISMSI / 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. Plan
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. Endpoint Detection Response (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 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. El DPO. Functions

Module 13. IT Security Management

13.1. Security 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. Methodology NIST

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. Analysis of the Environment

14.1.1. Analysis of the Economic Situation

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. Nonlinear
14.1.1.2.4. Incomprehensible

14.1.2. Analysis of the General Environment. 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. Analysis of the Internal Situation. 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. Revolution 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. Transforming 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. Exoteric Protocols

15.4. Cryptographic Techniques

15.4.1. Key Length
15.4.2. Key Management
15.4.3. Types of Algorithms
15.4.4. Key Management 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. Access to Wireless Networks

16.10.1. Types of Wireless Networks
16.10.2. Security in Wireless Networks
16.10.3. Attacks on Wireless Networks

Module 17. Security in Communications and Software Operation

17.1. Computer Security in Communications and Software Operation

17.1.1. IT Security
17.1.2. Cybersecurity
17.1.3. Cloud Security

17.2. IT 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. Study 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 Systems: Access Policies
18.4.3. Key Management Systems

18.5. System Securitization

18.5.1. Securitization of Storage Systems
18.5.2. Protection of Database Systems
18.5.3. Securing 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. Security in LoRaWAN

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. Cellular IoT Use Cases. Ecosystem
19.8.3. IoT Cellular Security

19.9. WiSUN Technology

19.9.1. WiSUN Technology
19.9.2. WiSUN Use Cases. Ecosystem
19.9.3. Security in WiSUN

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 Technologie

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: Organizational Analysis
20.5.2. Phase 2: Determining the Continuity Strategy
20.5.3. Phase 3: Response to Contingency
20.5.4. Phase 4: Tests, 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 the 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 Analytics 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 Analytics 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 Analytics 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 Analytics Tools in the HR Department
21.8.3. Data Analytics 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 and Information Management and Manipulation in 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 the Measurements

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

22.4. Data Knowledge from the 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 Indexes
22.8.3. Data Mining

22.9. Datawarehouse

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. Principal 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 Analytics

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 Cleaning
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. Unbalance

25.7.1. Classes of Unbalance
25.7.2. Unbalance 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. Normalization of Data
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 Data vs. Discreet Data
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 Versus 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. Trend Seasonality of TS
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 Forecasting Methods

27.5.1. Media
27.5.2. Naïve
27.5.3. Seasonal Naïve
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 Regression 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. Architecture and Systems for Intensive Use of Data

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 Type Data Model

29.3. Databases. Storage Management and Data Recovery

29.3.1. Hash Indexes
29.3.2. Structured Log Storage
29.3.3. B Trees

29.4. Data Coding Formats

29.4.1. Language-Specific Formats
29.4.2. Standardized Formats
29.4.3. Binary Coding Formats
29.4.4. Data Stream 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. Processing of Offline Data

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 Focus of 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 Healthcare Sector

30.2.1. Use of 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 Financial Services Sector
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 Analytics 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 Analytics 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 in Human Resources
30.10.2. Practical Applications in the Business World
30.10.3. Potential Risks Related to the Use of AI

You will solve real cases and face complex situations in simulated environments designed for practical and effective learning”