The Relearning system will reduce the long hours of study, so frequent in other teaching methods"

Artificial Intelligence tools play a significant role in improving productivity, both in programming and software development. Among their applications, they detect and correct errors more efficiently, reducing the time spent on manual debugging. In line with this, these mechanisms search for security vulnerabilities and perform security audits in an exhaustive manner, which implies an optimization of application protection. In this way, IT specialists can consider aspects such as predicting deadlines or allocating resources to improve their schedules.

In this context, TECH has designed a pioneering course that will provide strategies to improve productivity in software development with Artificial Intelligence. Therefore, the syllabus will delve into aspects such as repository management, the integration of Machine Learning with databases and automatic translation between programming languages.

Emphasis will also be placed on the implementation of Clean Architecture to computer procedures, since it improves code quality and allows a more collaborative development. On the other hand, the materials will provide the keys to create projects with Intelligent Computing, both in LAMP and MEVN environments. In addition, multiple real case studies and exercises will be included, to bring the development of the program closer to the usual computing practice.

The curriculum will be based on a theoretical-practical perspective, offering the professional an intensive learning about web projects with Artificial Intelligence. In this way, students will assimilate the contents thanks to video summaries of each topic, specialized readings and infographics. Also, thanks to TECH's Relearning system, programmers will progress in a natural way, consolidating new concepts more easily, thus reducing the long hours of study.  The only requirement for this university program will be to have an electronic device with an Internet connection, to access the Virtual Campus at any time. 

You will delve into various strategies that will help you in the maintainability of applications with Machine Learning"

This Postgraduate diploma in Artificial Intelligence Techniques Application in the Software Projects Life Cycle contains the most complete and up-to-date program on the market. Its most notable features are:

• The development of practical cases presented by experts in Application of Artificial Intelligence Techniques in the Life Cycle of Software Projects
• 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
• Its special emphasis on innovative methodologies
• 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

You will delve into code optimization using ChatGPT, one of the latest trends that have revolutionized the IT landscape"

The program’s teaching staff includes professionals from the industry who contribute their work experience to this program, as well as renowned specialists from leading societies and prestigious universities.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations.

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

You will prepare the optimal development environments for your IT processes, all thanks to this innovative 100% online program"

You will achieve your objectives thanks to TECH's didactic tools, including explanatory videos and interactive summaries"

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"