El sistema Relearning reducirá las largas horas de estudio, tan frecuentes en otros métodos de enseñanza” 

Las herramientas de IA desempeñan un papel significativo en la mejora de la productividad, tanto en programación como desarrollo de software. Entre sus aplicaciones, destaca la detección y corrección de errores de forma más eficiente, lo que reduce el tiempo dedicado a la depuración manual. En sintonía con esto, dichos mecanismos buscan vulnerabilidades de seguridad y realizan auditorías de seguridad con exhaustividad, lo que implica una optimización de la protección de las aplicaciones. Así, los informáticos pueden considerar aspectos como la predicción de plazos o la asignación de recursos para mejorar sus planificaciones. 

En este contexto, TECH ha diseñado una pionera capacitación, que aportará estrategias para mejorar la productividad en el desarrollo de software con Inteligencia Artificial. Así, el temario profundizará en aspectos como la gestión de repositorios, la integración de Aprendizaje Automático con bases de datos y la traducción automática entre lenguajes de programación.  

Asimismo, se enfatizará en la implementación de Clean Architecture a los procedimientos informáticos, ya que mejora la calidad del código y permite un desarrollo más colaborativo. Por otra parte, los materiales brindarán las claves para crear proyectos con Computación Inteligente, tanto en entornos LAMP, como MEVN. Además, se incluirán múltiples casos prácticos reales y ejercicios, para acercar el desarrollo del programa a la práctica informática habitual. 

El plan de estudios estará basado en una perspectiva teórico-práctica, ofreciendo al profesional un aprendizaje intensivo sobre los proyectos web con Inteligencia Artificial. De esta manera, los alumnos asimilarán los contenidos gracias a los vídeo resúmenes de cada tema, las lecturas especializadas y las infografías. Igualmente, gracias al sistema Relearning de TECH, los programadores progresarán de manera natural, consolidando de forma más sencilla los nuevos conceptos, reduciendo de esta forma las largas horas de estudio. El único requisito de esta titulación universitaria será contar con un dispositivo electrónico con conexión a Internet, para acceder en cualquier momento al Campus Virtual. 

Profundizarás en diversas estrategias que te ayudarán en la mantenibilidad de aplicaciones con Aprendizaje Automático” 

Esta Postgraduate diploma en Artificial Intelligence Techniques Application in the Software Projects Life Cycle contiene el programa educativo más completo y actualizado del mercado. Sus características más destacadas son:  

• El desarrollo de casos prácticos presentados por expertos en Aplicación de Técnicas de Inteligencia Artificial en el Ciclo de Vida de Proyectos Software 
• Los contenidos gráficos, esquemáticos y eminentemente prácticos con los que está concebido recogen una información científica y práctica sobre aquellas disciplinas indispensables para el ejercicio profesional 
• Los ejercicios prácticos donde realizar el proceso de autoevaluación para mejorar el aprendizaje 
• Su especial hincapié en metodologías innovadoras  
• Las lecciones teóricas, preguntas al experto, foros de discusión de temas controvertidos y trabajos de reflexión individual 
• La disponibilidad de acceso a los contenidos desde cualquier dispositivo fijo o portátil con conexión a internet 

Ahondarás en la optimización de los códigos empleando ChatGPT, una de las últimas tendencias que han revolucionario el panorama informático”

El programa incluye en su cuadro docente a profesionales del sector que vierten en esta capacitación la experiencia de su trabajo, además de reconocidos especialistas de sociedades de referencia y universidades de prestigio.  

Su contenido multimedia, elaborado con la última tecnología educativa, permitirá al profesional un aprendizaje situado y contextual, es decir, un entorno simulado que proporcionará una capacitación inmersiva programada para entrenarse ante situaciones reales.  

El diseño de este programa se centra en el Aprendizaje Basado en Problemas, mediante el cual el profesional deberá tratar de resolver las distintas situaciones de práctica profesional que se le planteen a lo largo del curso académico. Para ello, contará con la ayuda de un novedoso sistema de vídeo interactivo realizado por reconocidos expertos. 

Prepararás los entornos de desarrollo óptimos para tus procesos informáticos, todo gracias a este innovador programa 100% online” 

Conseguirás tus objetivos a través de las herramientas didácticas de TECH, entre las que destacan los vídeos explicativos y los resúmenes interactivos” 

This academic itinerary will expose the keys for software development using Artificial Intelligence, as well as for the effective management of repositories. It will delve into no-code interface design, translation between programming languages and the use of intelligent tools to improve software productivity.  Massive data storage will also be analyzed in detail, addressing advanced algorithms and structures. In addition, the course materials will delve into the testing life cycle, providing students with a complete picture that will ensure both the efficiency and reliability of products.

You will learn through real cases and by solving complex situations in simulated learning environments”

Module 1. Improving Software Development Productivity with Artificial Intelligence

1.1. Prepare a Suitable Development Environment

1.1.1. Selection of Essential Tools for Artificial Intelligence Development
1.1.2. Configuration of the Chosen Tools
1.1.3. Implementation of CI/CD Pipelines Adapted to Artificial Intelligence Projects
1.1.4. Efficient Management of Dependencies and Versions in Development Environments

1.2. Essential Artificial Intelligence Extensions for Visual Studio Code

1.2.1. Exploring and Selecting Artificial Intelligence Extensions for Visual Studio Code
1.2.2. Integration of Static and Dynamic Analysis Tools in the SDI
1.2.3. Automation of Repetitive Tasks with Specific Extensions
1.2.4. Customization of the Development Environment to Improve Efficiency

1.3. No-code  Design of User Interfaces with Artificial Intelligence Elements

1.3.1. No-code  Design Principles and Their Application to User Interfaces
1.3.2. Incorporation of Artificial Intelligence Elements in the Visual Design of Interfaces
1.3.3. Tools and Platforms for No-code Creation of Intelligent Interfaces
1.3.4. Evaluation and Continuous Improvement of No-code Interfaces with Artificial Intelligence

1.4. Code Optimization using ChatGPT

1.4.1. Identifying Duplicate Code
1.4.2. Refactor
1.4.3. Create Readable Code
1.4.4. Understanding What Code Does
1.4.5. Improving Variable and Function Names
1.4.6. Automatic Documentation Creation

1.5. Repository Management with Artificial Intelligence

1.5.1. Automation of Version Control Processes with Artificial Intelligence Techniques
1.5.2. Conflict Detection and Automatic Resolution in Collaborative Environments
1.5.3. Predictive Analysis of Changes and Trends in Code Repositories
1.5.4. Improved Organization and Categorization of Repositories using Artificial Intelligence

1.6. Integration of Artificial Intelligence in Database Management

1.6.1. Query and Performance Optimization Using Artificial Intelligence Techniques
1.6.2. Predictive Analysis of Database Access Patterns
1.6.3. Implementation of Recommender Systems to Optimize Database Structure
1.6.4. Monitoring and Proactive Detection of Potential Problems in Databases

1.7. Fault Finding and Creation of Unit Tests with Artificial Intelligence

1.7.1. Automatic Generation of Test Cases Using Artificial Intelligence Techniques
1.7.2. Early Detection of Vulnerabilities and Bugs using Static Analysis with Artificial Intelligence
1.7.3. Improving Test Coverage by Identifying Critical Areas with Artificial Intelligence

1.8. Pair Programming with GitHub Copilot

1.8.1. Integration and Effective Use of GitHub Copilot in  Pair ProgrammingSessions
1.8.2. Integration Improvements in Communication and Collaboration between Developers with GitHub Copilot
1.8.3. Integration Strategies for Making the Most of Code Hints Generated by GitHub Copilot
1.8.4. Integration Case Studies and Best Practices in Artificial Intelligence-assisted Pair Programming

1.9. Automatic Translation between Programming Languages

1.9.1. Programming Language Specific Machine Translation Tools and Services
1.9.2. Adapting Machine Translation Algorithms to Development Contexts
1.9.3. Improving Interoperability between Different Languages by Machine Translation
1.9.4. Assessing and Mitigating Potential Challenges and Limitations of Machine Translation

1.10. Recommended Artificial Intelligence Tools to Improve Productivity

1.10.1. Comparative Analysis of Artificial Intelligence Tools for Software Development
1.10.2. Integration of Artificial Intelligence Tools in Workflows
1.10.3. Automation of Routine Tasks with Artificial Intelligence Tools
1.10.4. Evaluating and Selecting Tools Based on Context and Project Requirements

Module 2. Software Architecture with Artificial Intelligence

2.1. Optimization and Performance Management in Artificial Intelligence Tools

2.1.1. Performance Analysis and Profiling in Artificial Intelligence Tools
2.1.2. Algorithm Optimization Strategies and Artificial Intelligence Models
2.1.3. Implementation of Caching and Parallelization Techniques to Improve Performance
2.1.4. Tools and Methodologies for Continuous Real-Time Performance Monitoring

2.2. Scalability in Artificial Intelligence Applications

2.2.1. Design of Scalable Architectures for Artificial Intelligence Applications
2.2.2. Implementation of Partitioning and Load Distribution Techniques
2.2.3. Workflow and Workload Management for Scalable Systems
2.2.4. Strategies for Horizontal and Vertical Expansion in Variable Demand Environments

2.3. Application Maintainability with Artificial Intelligence

2.3.1. Design Principles to Facilitate Maintainability in Artificial Intelligence Projects
2.3.2. Specific Documentation Strategies for Artificial Intelligence Models and Algorithms
2.3.3. Implementation of Unit and Integration Tests to Facilitate Maintenance
2.3.4. Methods for Refactoring and Continuous Improvement in Systems with Artificial Intelligence Components

2.4. Design of Large-Scale Systems

2.4.1. Architectural Principles for the Design of Large-Scale Systems
2.4.2. Decomposition of Complex Systems into Microservices
2.4.3. Implementation of Specific Design Patterns for Distributed Systems
2.4.4. Strategies for Complexity Management in Large-Scale Architectures with Artificial Intelligence Components

2.5. Large-Scale Data Warehousing for Artificial Intelligence Tools

2.5.1. Selection of Scalable Data Warehousing Technologies
2.5.2. Designing Database Schemas for Efficient Management of Large Data Volumes
2.5.3. Partitioning and Replication Strategies in Massive Data Storage Environments
2.5.4. Implementation of Data Management Systems to Ensure Integrity and Availability in Artificial Intelligence Projects

2.6. Data Structures and Artificial Intelligence

2.6.1. Adaptation of Classical Data Structures for Use in Artificial Intelligence Algorithms
2.6.2. Designing and Optimizing Specific Data Structures for Machine Learning Models
2.6.3. Integration of Efficient Data Structures in Data Intensive Systems
2.6.4. Strategies for Real-Time Data Manipulation and Storage in Artificial Intelligence Data Structures

2.7. Programming Algorithms for Artificial Intelligence Products

2.7.1. Development and Implementation of Application-Specific Algorithms for Artificial Intelligence Applications
2.7.2. Algorithm Selection Strategies according to Problem Type and Product Requirements
2.7.3. Adaptation of Classical Algorithms for Integration into Artificial Intelligence Systems
2.7.4. Evaluation and Comparison of Performance between Different Algorithms in Artificial Intelligence Development Contexts

2.8. Design Patterns for Artificial Intelligence Development

2.8.1. Identification and Application of Common Design Patterns in Projects with Artificial Intelligence Components
2.8.2. Development of Specific Patterns for the Integration of Models and Algorithms into Existing Systems
2.8.3. Pattern Implementation Strategies for Improving Reusability and Maintainability in Artificial Intelligence Projects
2.8.4. Case Studies and Best Practices in the Application of Design Patterns in Artificial Intelligence Architectures

2.9. Implementation of Clean Architecture

2.9.1. Fundamental Principles and Concepts of Clean Architecture
2.9.2. Adaptation of Clean Architecture to Projects with Artificial Intelligence Components
2.9.3. Implementation of Layers and Dependencies in Systems with Clean Architecture
2.9.4. Benefits and Challenges of Implementing Clean Architecture  in Artificial Intelligence Software Development

2.10. Secure Software Development in Web Applications with Artificial Intelligence

2.10.1. Principles of Security in Software Development with Artificial Intelligence Components
2.10.2. Identifying and Mitigating Potential Vulnerabilities in Artificial Intelligence Models and Algorithms
2.10.3. Implementation of Secure Development Practices in Web Applications with Artificial Intelligence Functionalities
2.10.4. Strategies for the Protection of Sensitive Data and Prevention of Attacks in Artificial Intelligence Projects

Module 3. Artificial Intelligence for QA Testing

3.1. Testing Life Cycle

3.1.1. Description and Understanding of the Testing Life Cycle in Software Development
3.1.2. Phases of the  Testing Life Cycle and Its Importance for Quality Assurance
3.1.3. Integration of Artificial Intelligence in Different Stages of the Testing Life Cycle
3.1.4. Strategies for Continuous Improvement of the Testing Life Cycle using Artificial Intelligence

3.2. Test Cases and Bug Detection

3.2.1. Effective Test Case Design and Writing in the QA Testing Context
3.2.2. Identification of Bugs and Errors during Test Case Execution
3.2.3. Application of Early Bug Detection Techniques using Static Analysis
3.2.4. Use of Artificial intelligence Tools for the Automatic Identification of Bugs in Test Cases

3.3. Types of Testing

3.3.1. Exploration of Different Types of Testing in the QA Domain
3.3.2. Unit, Integration, Functional, and Acceptance Testing: Characteristics and Applications
3.3.3. Strategies for the Selection and Appropriate Combination of Testing Types in Artificial Intelligence Projects
3.3.4. Adaptation of Conventional Testing Types to Projects with Artificial Intelligence Components

3.4. Creating a Test Plan

3.4.1. Designing and Structuring a Comprehensive Test Plan
3.4.2. Identifying Requirements and Test Scenarios in Artificial Intelligence Projects
3.4.3. Strategies for Manual and Automated Test Planning
3.4.4. Continuous Evaluation and Adjustment of the Test Plan as the Project Develops

3.5. Artificial Intelligence Bug Detection and Reporting

3.5.1. Implementation of Automatic Bug Detection Techniques using Machine Learning Algorithms
3.5.2. Use of Artificial Intelligence Tools for Dynamic Code Analysis in Search of Possible Errors
3.5.3. Strategies for Automatic Generation of Detailed Reports on Artificial Intelligence-Detected Bugs
3.5.4. Effective Collaboration between Development and QA Teams in the Management of Artificial Intelligence-Detected Bugs

3.6. Creation of Automated Testing with Artificial Intelligence

3.6.1. Development of Automated Test Scripts for Projects with AI Components
3.6.2. Integration of Artificial Intelligence-based Test Automation Tools
3.6.3. Use of Machine Learning Algorithms for Dynamic Generation of Automated Test Cases
3.6.4. Strategies for Efficient Execution and Maintenance of Automated Test Cases in Artificial Intelligence Projects

3.7. API Testing

3.7.1. Fundamental Concepts of API Testing and Its Importance in QA
3.7.2. Development of Tests for API Verification in Environments with Artificial Intelligence Components
3.7.3. Strategies for Data and Results Validation in API Testing with Artificial Intelligence
3.7.4. Use of Specific Tools for API Testing in Artificial Intelligence Projects

3.8. Artificial Intelligence Tools for Web Testing

3.8.1. Exploring Artificial Intelligence Tools for Test Automation in Web Environments
3.8.2. Integration of Element Recognition and Visual Analysis Technologies in Web Testing
3.8.3. Strategies for Automatic Detection of Changes and Performance Problems in Web Applications using Artificial Intelligence
3.8.4. Evaluation of Specific Tools for Improving Efficiency in Web Testing with Artificial Intelligence

3.9. Mobile Testing  Using Artificial Intelligence

3.9.1. Development of Testing Strategies for Mobile Applications with Artificial Intelligence Components
3.9.2. Integration of Specific Testing Tools for Artificial Intelligence-based Mobile Platforms
3.9.3. Use of Machine Learning Algorithms for the Detection of Performance Problems in Mobile Apps
3.9.4. Strategies for the Validation of Specific Mobile Application Interfaces and Functions using Artificial Intelligence

3.10. QA Data Science and Artificial Intelligence

3.10.1. Exploration of QA Tools and Platforms that Incorporate Artificial Intelligence Functionalities
3.10.2. Evaluation of Tools for Efficient Test Management and Execution in Artificial Intelligence Projects
3.10.3. Use of Machine Learning Algorithms for Test Case Generation and Optimization
3.10.4. Strategies for Effective Selection and Adoption of QA Tools with Artificial Intelligence Capabilities

This academic itinerary is exclusive to TECH and you will be able to develop it at your own pace thanks to its 100% online Relearning methodology"