Web Fullstack AI from scratch (International Certification)

• Definition of primary keys and how they ensure the uniqueness of records.
• Foreign keys and their importance in linking tables.

• Definition of algorithms.
• Structure of an algorithm: Input, Process, and Output.
• Types of algorithms: iterative, recursive, and algorithmic.
• Examples of algorithms in real life.

• Decomposing problems into smaller steps.
• Pattern recognition and abstraction.
• Solution structure: Development and optimization.
• Designing efficient algorithms.

• Modeling real-world problems with pseudocode.
• Conversions between pseudocode and flowcharts.
• Practical exercise: Developing pseudocode to solve a mathematical problem.

• Identification of actors.
• Main and alternative scenario diagrams.

• Representation of the interaction between objects and events.
• Modeling of data flow and message sequence.

• Representation of activities, decisions, and alternative flows.
• Activity diagrams with partitions (swimlanes).

• Primitive data types: integers, floats, booleans, strings.
• Arithemetic, logical, comparison, assignment, and bitwise operators.
• Data type conversion (casting).

• Conditional statements: if, else, elif, switch.
• Counter-controlled loops: for and while.
• Condition-controlled loops: do-while.
• Concepts of recursion vs iteration.

• Return and non-return functions.
• Parameters by value and by reference.
• Nested and recursive functions.
• Use of lambda functions.

• Functional decomposition exercises to solve problems.
• Code modularization to enhance reusability.

• Basic operations on data structures: creation, updating, deletion.
• Advanced methods and operations: append, pop, sort, reverse.
• Dictionaries and handling key-value pairs.

• Step-by-step implementation of sorting algorithms.
• Time and space complexity: O(n^2), O(n log n).
• Comparison between sorting algorithms: efficiency and appropriate use.

• Implementation of sequential search and binary search.
• Necessary conditions for using binary search.
• Comparison of complexities.

• Search and sorting exercises with lists and dictionaries.
• Optimization of search code for large volumes of data.

• Definition of objects, classes, attributes, and methods.
• Encapsulation, abstraction, and modularity.

• Creation and use of objects in Python.
• Public, private, and protected methods.

• Creation and use of objects in Python.
• Public, private, and protected methods.

• Creation and use of objects in Python.
• Public, private, and protected methods.

• Concept of inheritance and class reusability.
• Overridden methods and polymorphism.

• Practical exercise to model entities using OOP.

• Key differences between relational and non-relational databases.
• Examples of relational databases (MySQL, PostgreSQL) and non-relational databases (MongoDB).
• When to use a relational database vs a non-relational database.

• Definition and creation of tables.
• Relationships between tables: one to one, one to many, and many to many.
• Data types in SQL: VARCHAR, INT, DATE, etc.

• Definition of entities, attributes, and relationships.
• Creation of ER diagrams to represent data models.

• Concept of normalization and its levels (1NF, 2NF, 3NF).
• Practical exercises to avoid redundancy in databases.

• Basic SQL Syntax: CREATE TABLE, ALTER TABLE.
• Definition of Constraints (PRIMARY KEY, UNIQUE, NOT NULL).

• Reading queries: SELECT with filters, operators, and sorting.
• Data insertion, modification, and deletion queries: INSERT INTO, UPDATE, DELETE.

• Use of functions to calculate aggregates over data sets.
• Grouping data with GROUP BY and filtering with HAVING.

• Definition and importance of backups for data protection.
• Types of backups: full, incremental, and differential.

• Basic syntax for performing backups.
• Importance of choosing the storage location (local, cloud, external storage).

• Basic procedure for restoring a backup.
• Considerations to keep in mind when restoring: data consistency, database versions, and validation of data integrity.

This module provides an introduction to the version control tools Git and GitHub. The importance of collaboration and versioning in software development will be explained.

• Differences between Git (local) and GitHub (remote repository).
• Workflow in Git: local and remote.

• Initial configuration of Git: user, email, and SSH keys.

• Initializing a Git repository locally.

• Uploading a local repository to GitHub.
• Creating a remote repository from GitHub.

• Creation of commits, writing clear and detailed messages.
• Using git log and git diff to manage versions.

• Basic flow of Pull Requests: creation, review, and merging of changes.
• Resolution of code conflicts.

This module covers the fundamental principles of frontend design and development. Students will learn to structure and design interactive and responsive web applications using HTML, CSS, and JavaScript, generating new versions with Git and GitHub.

• Visual design, typography, and color usage.
• Principles of visual hierarchy and balance.

• Usability and how to improve site navigation.
• Prototyping and usability testing with users.

• Creation of wireframes using tools such as Figma or Sketch.

• Design and development of visual components in HTML and CSS.

• Choice of format based on content type and optimization.

• Use of tools such as Photoshop and online tools for image compression.

• Implementation of the "Mobile First" approach for websites.

• Using Bootstrap to quickly create responsive interfaces.

• Introduction to AI image generation and its integration into websites.

• Basic structure of web pages with HTML and CSS.
• New HTML5 tags and best practices.

• Advanced layout design using Flexbox and CSS Grid for responsive pages.

• Basic programming in JavaScript: variables, control structures, and functions.

• Manipulation of the HTML structure with JavaScript and events.

This module teaches students to develop backend applications using Python and Flask. Students will learn to manage server logic, create APIs, and connect with databases.

• How a web server works and its role in the Internet ecosystem.
• Differences between static and dynamic HTTP servers.

• Structure of an HTTP request: headers, body, and methods.
• HTTP status codes (200, 404, 500) and their meanings.

• Installation of Flask and creation of a virtual environment.
• Basic architecture of a Flask application.

• Use of routes to manage URLs and HTTP methods (GET, POST).
• Use of templates with Jinja2 to generate dynamic content in HTML.

• How to define routes to handle different types of HTTP requests.
• Best practices for the secure handling of data sent through forms.

• Form validation and user input sanitization.
• Error handling in data validation (errors 400 and 422).

• Introduction to ORM (Object-Relational Mapping) with SQLAlchemy.
• Mapping tables to classes in Python.

• Creation of routes and views for CRUD operations (Create, Read, Update, Delete).
• Implementation of CRUD using SQLAlchemy and Flask.

• Versioning changes in the database and handling migrations.
• Conflict resolution and branch management in collaborative projects.

Students will learn to deploy web applications on servers and hosting platforms. The module will cover practices to ensure the security and scalability of the applications.

• Comparison between different hosting options and their advantages and disadvantages.
• Selection of the appropriate type of hosting for different web projects.

• Configuration of basic servers for Flask applications.
• Use of SSH servers for remote server management.

• Steps to deploy applications on free hosting platforms.
• Configuration of environment variables on platforms like Heroku.

• Configuration of automatic deployment with Git for continuous updates.
• Use of CI/CD tools to automate the deployment process.

• Linking custom domains with hosting providers.
• Configuring DNS records for the proper functioning of the domains.

• Process of installing SSL certificates to secure communication.
• Configuration of HTTPS to secure requests between server and client.

The different types of clouds and cloud service providers will be taught. A discussion will include the implications for security, privacy, and costs when adopting the cloud in web projects.

• Characteristics of public, private, and hybrid clouds.
• Use cases for each type of cloud in web and business projects.

• Overview of the main cloud service providers.
• Cost, features, and scalability comparison between platforms.

• Advantages of automatic scalability in the cloud.
• Environmental and economic impact of cloud usage.

This module will teach the use of generative AI tools for content creation in web applications. Ethical considerations and responsibilities in the use of AI will also be discussed.

• Creation of descriptions, automated responses, and articles using AI.
• Customization of outputs generated by ChatGPT for specific content.

• Generation of custom images based on text descriptions using DALL·E.
• Basic editing of images generated by AI.

• Creation of videos using HeyGen and other video generation algorithms.
• Application of AI to automate video editing and production.

• Use of generative AI APIs for integration into websites and applications.
• Creation of dynamic content with AI to enhance user experience.