Learning Pydantic: Advanced Data Validation In Python from Udemy

Welcome to the best resource online for learning modern Pydantic, a data validation library that has taken the python community by storm.

Pydantic is was first released in 2018 and has since become one of the most popular python libraries. It is nowadays downloaded more than 130 million times a month, and is used by some of the largest organizations out there, from the tech giants like Google, Amazon, Apple, Meta, and Netflix, to large conglomerates in various other industries, such as Starbucks, JPMorgan Chase. Oh, and yes, even NASA.

There's a good reason for this. Pydantic is a powerful library that elegantly solves a very common problem in software development: data validation.

Pydantic's speed, simple declarative syntax, and extensibility make it an indispensable utility in modern python development.

And in this course, you will learn everything you need to know to get started with Pydantic, from the very basics of defining data models, to more advanced topics such as fields with factory defaults, creating custom model-validators, data serialization, and much more.

The first part of the course will be purely about pydantic, where we explore it in isolation. You will learn:

how to define data models with pydantic
how to compose more complex models from simpler ones via inheritance
the foundations of type hinting in python, including enumerations, literals, and other advanced types-
how to use pydantic's powerful validation system
how to serialize and deserialize data
how to extract models to schemas
how to validate data against pydantic models

Then in the second part of the course we will turn our attention to the Capstone Project, where we will use pydantic to develop and deploy a python web API that allows users to create and vote on polls. This app will use Redis as our durable key-value data store, and will be deployed to production as a serverless function.

The Capstone will be developed step by step, in a series of about 30 skill challenges, where you will be asked to incrementally implement small features. This will give you the opportunity to practice what you've learned in the first part of the course, and to:

get a practical feel for how Pydantic is used in real-world applications
learn about modern API development with python
understand what Redis is and how it can be used as a durable data store
learn about virtual environments and dependency management in python
practice using git and github
learn the basics of serverless computing by deploying the API as a serverless function

The course will use the latest version of Pydantic, which leverages the power of Rust to achieve blazing fast performance.

Also, if you're new to python or haven't used the used the language in a while, there's a full-featured python crash course included as an extra appendix which will get you up to speed in no time.

I'm very excited to share this with you, and I look forward to seeing you in the course.

What's inside

Learning objectives

Gain an in-depth understanding of what pydantic is and how it is used
Practice defining pydantic data models using modern type hints, custom validations, and fine-tuned configuration
Learn how to define complex, interdependent, and nested data models with pydantic

Serialize model instances into json and deserialize incoming data
Practice using pydantic in the context of building and deploying a real-world python web api
Master relevant concepts in modern python application development, like dependency management and version control

Gain an in-depth understanding of what pydantic is and how it is used
Practice defining pydantic data models using modern type hints, custom validations, and fine-tuned configuration
Learn how to define complex, interdependent, and nested data models with pydantic
Serialize model instances into json and deserialize incoming data
Practice using pydantic in the context of building and deploying a real-world python web api
Master relevant concepts in modern python application development, like dependency management and version control

Syllabus

Pydantic In A Nutshell

Course Resource Part 1

Introduction To Pydantic

Our First Pydantic Model

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Uses Pydantic, a popular Python library downloaded over 130 million times a month, indicating its widespread adoption and relevance in modern Python development

Covers modern API development with Python, including the use of Redis as a durable data store, which are valuable skills for building robust applications

Includes a capstone project that involves building and deploying a Python web API, providing practical experience with real-world application development

Employs the latest version of Pydantic, which leverages Rust for faster performance, giving learners exposure to cutting-edge technologies

Requires learners to set up a Redis instance, which may require additional steps and familiarity with database management

Includes a Python crash course as an appendix, which is helpful for beginners or those needing a refresher, but may be unnecessary for experienced developers

Reviews summary

Practical pydantic and modern python api development

According to students, this course provides a solid foundation (positive) in Pydantic, the powerful data validation library, and its practical application in modern Python web development. Learners particularly appreciate the clear explanations (positive) of Pydantic's core concepts and advanced features. The Capstone Project (positive), building a web API with FastAPI and Redis, is frequently highlighted as a major strength (positive), offering valuable hands-on experience (positive) and demonstrating real-world usage. While many find the course well-paced (positive) and the instructor's approach highly effective (positive), some note that having a basic understanding of Python (neutral) beyond the included crash course is helpful, especially for the project section. Overall, it's seen as a comprehensive (positive) resource for developers looking to incorporate Pydantic into their workflow.

Content is useful for modern development.

"This course taught me practical skills directly applicable to my work as a Python developer."

"Learning how Pydantic fits into modern API frameworks like FastAPI is highly relevant."

"I found the course material to be very up-to-date and relevant to current Python practices."

Instructor explains concepts effectively.

"The instructor did a fantastic job explaining complex topics in an easy-to-understand way."

"I really enjoyed the instructor's teaching style. Very clear and kept me engaged."

"The explanations for Pydantic features were very clear, thanks to the instructor's delivery."

Covers basics to advanced Pydantic features.

"This course provides a really thorough look at Pydantic, from the simple models to custom validators and serialization."

"I feel like I have a strong grasp of defining models, handling types, and using validators after this course."

"It covered all the essential Pydantic concepts I needed, and some advanced ones too."

API project applies Pydantic in a real context.

"The capstone project building the API was the highlight for me. It put everything into perspective."

"I loved building the FastAPI application. It solidified how Pydantic is used in real projects."

"Getting hands-on with FastAPI, Redis, and Pydantic together was incredibly useful."

Python crash course may not be enough for all.

"While the Python crash course is there, you really need a solid foundation before diving into Pydantic and the API."

"Beginners might struggle a bit with the pace, especially in the project section, if they're not already comfortable with Python."

"I had some Python background, which helped, but I think someone starting from zero might find it challenging."

"The Python appendix is a good refresher but doesn't replace prior Python learning."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Learning Pydantic: Advanced Data Validation In Python with these activities:

Review Python Type Hinting

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Solidify your understanding of Python's type hinting system, which is fundamental to defining Pydantic models effectively.

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Read the official Python documentation on typing.
Practice using type hints in simple Python functions.
Experiment with different type hints like List, Dict, and Union.

Review 'Fluent Python'

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Gain a deeper understanding of Python's underlying mechanisms, which will enhance your ability to use Pydantic effectively.

View Fluent Python on Amazon

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Read the chapters on data structures and object-oriented programming.
Pay close attention to the sections on type hints and data validation.
Try to relate the concepts in the book to Pydantic's features.

Implement Custom Validators

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Sharpen your skills in creating custom validators, a crucial aspect of Pydantic for enforcing complex data constraints.

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Define a Pydantic model with several fields.
Implement custom validators for each field using different validation techniques.
Test your validators with various inputs to ensure they work correctly.

Four other activities

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Write a Blog Post on Pydantic

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Solidify your understanding of Pydantic by explaining its features and benefits to others in a blog post.

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Choose a specific aspect of Pydantic to focus on.
Research the topic thoroughly and gather relevant examples.
Write a clear and concise blog post explaining the concept.
Publish your blog post on a platform like Medium or your personal website.

Review 'Effective Python'

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Improve your overall Python coding skills, which will enhance your ability to use Pydantic effectively and write robust applications.

View Effective Python on Amazon

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Read the book and take notes on the key recommendations.
Apply the recommendations to your Pydantic projects.
Refactor existing code to follow the best practices outlined in the book.

Build a Data Validation API

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Apply your Pydantic knowledge to build a practical API that validates incoming data, reinforcing your understanding of real-world applications.

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Design the API endpoints and data models.
Implement the API using a framework like FastAPI or Flask.
Use Pydantic to validate the incoming data against your models.
Test the API thoroughly with various inputs.

Contribute to Pydantic

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Deepen your expertise by contributing to the Pydantic open-source project, gaining insights into its inner workings and best practices.

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Explore the Pydantic GitHub repository and identify areas for contribution.
Read the contribution guidelines and set up your development environment.
Contribute code, documentation, or bug fixes to the project.
Participate in code reviews and discussions with other contributors.

Career center

Learners who complete Learning Pydantic: Advanced Data Validation In Python will develop knowledge and skills that may be useful to these careers:

API Developer

API Developers design, develop, and maintain application programming interfaces. This course is essential for API Developers because it directly addresses data validation. Data validation is a crucial aspect of API development. The course's capstone project focuses on developing a Python web API. Here, you will solidify your skills in creating and managing data models, serializing data, and implementing validation rules within an API context. The focus on Pydantic makes this course particularly valuable for API Developers. This is because Pydantic streamlines data handling and validation.

See salaries and explore the career path for API Developer

Data Scientist

A Data Scientist uses programming languages like Python to analyze and interpret complex data sets. This course, focusing on Pydantic, directly aligns with a data scientist's need to validate data. Data validation is a crucial step in any data analysis pipeline, ensuring data integrity and accuracy. The course's coverage of data serialization and deserialization becomes invaluable when handling data from various sources. A data scientist should take this course to enhance their data quality and streamline their data processing workflows.

See salaries and explore the career path for Data Scientist

Backend Developer

Backend Developers are responsible for the server side logic and databases that power applications. This course helps Backend Developers by teaching Pydantic. This is a valuable tool for ensuring data integrity and consistency in APIs. The course's capstone project, where you develop a Python web API, directly applies to backend development tasks. Understanding how to use data models, custom validators, data serialization and deserialization, and Redis with Pydantic becomes essential. A Backend Developer may find this course helpful for designing robust and scalable APIs.

See salaries and explore the career path for Backend Developer

Software Engineer

Software Engineers often work on projects that require robust data validation and management. Modern Pydantic, a core focus of this course, is a data validation library that can be useful to software engineers. By understanding how to define data models, implement custom validators, and serialize data using Pydantic, a software engineer can build more reliable and maintainable applications. The course's capstone project, which involves building a Python web API, directly translates to the skills needed in web development. This course, therefore, becomes an asset for improving code quality and efficiency for software engineers.

See salaries and explore the career path for Software Engineer

Technical Lead

A Technical Lead guides a team of developers and makes key technical decisions. This course addresses data validation. Given that data validation is a critical aspect of software development, understanding how to use Pydantic helps build robust systems. The course's capstone project, which involves developing a Python web API, is useful for technical leads. The focus on aspects like data models, custom validators, and data serialization enables the Technical Lead to guide the team effectively. A Technical Lead should consider this course to stay updated with modern Python development practices.

See salaries and explore the career path for Technical Lead

Full-Stack Developer

Full Stack Developers work on both the front end and back end of web applications. They need to have a broad understanding of different technologies. Pydantic helps Full Stack Developers by validating data efficiently. The course's discussion of data models, custom validators, and data serialization becomes helpful. The course's capstone project, involving the development of a Python web API and its deployment as a serverless function, covers skills that are directly applicable to full stack development. A Full Stack Developer may find this course useful for developing robust applications.

See salaries and explore the career path for Full-Stack Developer

Solutions Architect

Solutions Architects design and implement IT solutions to meet business needs. This course focuses on Pydantic, which helps in data validation. Given that data validation is required for robust systems, understanding how to use data models, custom validators, and data serialization becomes important. The course's capstone project, which involves developing a Python web API, enables a Solutions Architect to design scalable and reliable solutions. This is done by taking advantage of modern validation practices.

See salaries and explore the career path for Solutions Architect

Software Architect

Software Architects design the structure of software systems. They typically require a strong understanding of data validation methods. This course introduces Pydantic, a data validation library. By understanding the capabilities of Pydantic, a software architect can make informed decisions about how to implement data validation within a system. The course's focus on data models, custom validators, and data serialization can help a software architect design robust and maintainable applications.

See salaries and explore the career path for Software Architect

Cloud Engineer

Cloud Engineers manage and maintain cloud infrastructure. This course includes a segment where the course participant develops and deploys a Python web API as a serverless function. Cloud Engineers need to understand how to deploy and manage applications in cloud environments. The deployment process is introduced via the capstone project. This focus on serverless deployment makes this course valuable for enhancing skills in managing Python applications in the cloud.

See salaries and explore the career path for Cloud Engineer

Machine Learning Engineer

Machine Learning Engineers build and deploy machine learning models. This course may be useful for Machine Learning Engineers because it focuses on data validation using Pydantic. Data validation is crucial in machine learning pipelines to ensure that the models are trained on clean and reliable data. By understanding how to use data models, custom validators, and serialization techniques, a machine learning engineer can improve the robustness of their data processing workflows. The skills taught in this course may contribute to the development of more accurate and reliable machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Data Engineer

Data Engineers are responsible for designing, building, and maintaining data pipelines. This course may be useful to data engineers due to its focus on data validation. Data validation is essential for ensuring data quality. By learning how to define data models, implement custom validators, and serialize data using Pydantic, a data engineer can build more robust and reliable data pipelines. The course’s coverage of data serialization and deserialization techniques would be helpful in managing data flow between different systems.

See salaries and explore the career path for Data Engineer

DevOps Engineer

DevOps Engineers automate and streamline software development processes. This course focuses on Pydantic, and the course's emphasis on API development and deployment as a serverless function may be useful to DevOps Engineers. DevOps Engineers need to understand the full lifecycle of applications, including how to deploy and manage them efficiently. DevOps Engineers can use the knowledge gained in this course to enhance their skills in deploying and managing Python based applications in a serverless environment.

See salaries and explore the career path for DevOps Engineer

Data Analyst

Data Analysts interpret data to provide insights and support decision making. This course may be useful to Data Analysts since it discusses data validation using Pydantic. Data validation is necessary for ensuring data integrity and accuracy. By learning how to use Pydantic to define data models and validate data, a data analyst can enhance the reliability of their analyses. The skills taught in this course may help improve the overall quality of data analysis workflows.

See salaries and explore the career path for Data Analyst

System Administrator

System Administrators manage computer systems and ensure they are running smoothly. While this course focuses on Pydantic, the skills related to managing dependencies and using virtual environments in Python may be useful to System Administrators. The course touches on dependency management and deployment. This can give a system administrator skills in managing Python based applications and their environments.

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Database Administrator

Database Administrators manage and maintain databases, ensuring data integrity and availability. This course may be useful to Database Administrators because it touches on data validation. Data validation is a part of ensuring data integrity in databases. By understanding how to use Pydantic to define data models and validate data, a database administrator can enhance the quality of data stored in databases. The course's capstone project uses Redis as a data store, introducing concepts related to key value databases.

See salaries and explore the career path for Database Administrator

Learning Pydantic

Advanced Data Validation In Python

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What's inside

Learning objectives

Syllabus

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Reviews summary

Practical pydantic and modern python api development

Activities

Career center

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