FULL STACK PYTHON & REACT: BUILD APPS WITH VIBE CODING

Lecture overview

Before we dive into functions, this lecture shows you where to find the presentation slides and project files for this section and how to use them alongside the videos. You will see how the material is organised per lecture so you can quickly find the code that matches what is on screen.

What you will learn

• Where the slides and project files for each section are stored
• How the files map to individual lectures
• How to download, extract and open them in your IDE
• Using the slides as a revision aid after each lecture

Why this matters

Having the exact code and slides at hand lets you compare your work with a known-good version whenever something does not behave as expected.

Practice task

Download this section's files, open them in PyCharm and locate the file that matches the next lecture.

Where this fits in the course

This lecture belongs to the section Functions, Methods in Python. This section is about writing reusable logic. You move from scripts that run top to bottom to well-structured functions, learn how Python resolves names and arguments, and pick up functional tools and packages that make code shorter and more modular. It opens this section and sets the direction for the lectures that follow, and prepares you for the next lecture, How to get in touch with us.

Lecture overview

This short lecture explains how to reach us whenever you are stuck or have a question. You will learn the best channels for course questions, how to write a question that gets a fast and useful answer, and where to find community discussions and updates.

What you will learn

• Where to post questions about the course
• How to describe a problem clearly: what you tried, what you expected, the full error message
• Where to find announcements and updates

Why this matters

Getting unblocked quickly is one of the biggest factors in finishing a course. A well-written question usually gets a solution on the first reply.

Where this fits in the course

This lecture belongs to the section Functions, Methods in Python. This section is about writing reusable logic. You move from scripts that run top to bottom to well-structured functions, learn how Python resolves names and arguments, and pick up functional tools and packages that make code shorter and more modular. It builds directly on the previous lecture, How to access the presentation and Project files, and prepares you for the next lecture, Functions, Scope, Args & Kwargs.

Lecture overview

Functions are the main building blocks of reusable Python code. In this lecture you will define and call functions, pass positional and keyword arguments, set default values, and return results. We then look at scope (local, enclosing, global and built-in) and finish with *args and **kwargs so your functions can accept flexible numbers of arguments.

What you will learn

• Defining and calling functions
• Parameters, arguments and return values
• Default and keyword arguments
• Local, enclosing, global and built-in scope (the LEGB rule)
• *args for variable positional arguments
• **kwargs for variable keyword arguments
• Docstrings and type hints for functions

What we do in this lecture

1. Refactor repeated code into functions
2. Experiment with scope to see how names are resolved
3. Build a flexible function using *args and **kwargs

Why this matters

Frameworks such as Django, FastAPI and Flask are built around functions: views, route handlers, decorators and dependencies all rely on these ideas. Understanding *args and **kwargs in particular makes framework source code much easier to read.

Practice task

Write a function build_profile(name, *skills, **details) that returns a dictionary describing a developer. Call it in at least three different ways and print the results.

By the end of this lecture you will be able to

• Write clean, reusable functions
• Explain how Python resolves variable names
• Use *args and **kwargs confidently

Where this fits in the course

This lecture belongs to the section Functions, Methods in Python. This section is about writing reusable logic. You move from scripts that run top to bottom to well-structured functions, learn how Python resolves names and arguments, and pick up functional tools and packages that make code shorter and more modular. It builds directly on the previous lecture, How to get in touch with us, and prepares you for the next lecture, Map, Filter, Lambda & Python Packages.

Tips for getting the most out of it

• Try changing one small thing at a time (a value, a name, a parameter) and predict the result before you run it.
• Commit your work to Git after finishing the lecture so you always have a working checkpoint to return to.

Lecture overview

In this lecture we look at a more functional style of Python and at how code is organised into modules and packages. You will write lambda functions, use map and filter to transform and select data, compare them with list comprehensions, and then create your own modules and packages and import them correctly.

What you will learn

• Lambda (anonymous) functions and when to use them
• map() for transforming collections
• filter() for selecting items
• Comparing map/filter with comprehensions
• Creating modules and packages
• Import styles and the role of __init__.py
• Installing third-party packages with pip

What we do in this lecture

1. Transform a dataset using map and lambda
2. Filter records using filter and a predicate function
3. Split a script into a small package and import from it

Why this matters

Clean module and package structure is the foundation of every larger project. Functional tools help you write concise data transformations that you will reuse in data processing and API code.

Practice task

Create a package called utils with two modules (text_tools and math_tools). Use map and filter inside them, then import and use both modules from a main script.

By the end of this lecture you will be able to

• Use lambda, map and filter appropriately
• Organise code into modules and packages
• Import and install packages correctly

Where this fits in the course

This lecture belongs to the section Functions, Methods in Python. This section is about writing reusable logic. You move from scripts that run top to bottom to well-structured functions, learn how Python resolves names and arguments, and pick up functional tools and packages that make code shorter and more modular. It builds directly on the previous lecture, Functions, Scope, Args & Kwargs, and prepares you for the next lecture, Practice Use Cases & Prompt Building.

Tips for getting the most out of it

• Use an AI assistant to explain any line you are unsure about, but always verify the answer by running the code yourself.
• Pause the video after each new idea and reproduce it on your own machine before moving on.

Lecture overview

This is a hands-on practice lecture where we solve realistic problems with functions and learn how to build better prompts for function-level code. You will work through several use cases, write the solution yourself, then use AI to generate an alternative and compare the two critically.

What you will learn

• Designing function signatures from requirements
• Writing prompts that specify inputs, outputs and edge cases
• Asking AI to add tests and docstrings
• Comparing and improving AI-generated functions
• Practising lambda and package usage in context

What we do in this lecture

1. Solve practical problems using well-designed functions
2. Write structured prompts for function generation
3. Review AI output for correctness and edge cases

Why this matters

Most AI-assisted coding happens at the function level. Being able to describe a function precisely, and then judge the result, is one of the highest-value skills you can build.

Practice task

Write a prompt asking an AI tool to create a function that validates an email address and a password. Include explicit rules and edge cases in your prompt, then test the result with at least six inputs.

By the end of this lecture you will be able to

• Translate requirements into function designs
• Write precise prompts for function generation
• Critically evaluate AI-generated functions

Where this fits in the course

This lecture belongs to the section Functions, Methods in Python. This section is about writing reusable logic. You move from scripts that run top to bottom to well-structured functions, learn how Python resolves names and arguments, and pick up functional tools and packages that make code shorter and more modular. It builds directly on the previous lecture, Map, Filter, Lambda & Python Packages, and prepares you for the next lecture, Test your Functions, Methods Skills.

Tips for getting the most out of it

• Rewatch the tricky parts at a slower speed; it is completely normal to need two passes on new material.
• Keep a short notes file with the commands, snippets and prompts you use here; you will reuse them later in the course.

Lecture overview

This lecture tests your understanding of functions, scope, arguments, lambdas, map and filter, and packages. Work through each challenge on your own first, then watch the walkthrough to compare approaches and learn alternative solutions.

What you will learn

• Self-assessment on functions and scope
• Tricky cases with default arguments and *args/**kwargs
• Functional tools versus comprehensions
• Package and import questions

Why this matters

Functions are used everywhere in the rest of the course. Confirming that you really understand them now avoids confusion when you meet decorators, views and dependency injection later.

Practice task

Pause at each challenge, solve it in your IDE, and only then watch the solution. Revisit the earlier lectures for any challenge you found difficult.

By the end of this lecture you will be able to

• Verify your mastery of functions and packages
• Identify topics to revise before moving on

Where this fits in the course

This lecture belongs to the section Functions, Methods in Python. This section is about writing reusable logic. You move from scripts that run top to bottom to well-structured functions, learn how Python resolves names and arguments, and pick up functional tools and packages that make code shorter and more modular. It builds directly on the previous lecture, Practice Use Cases & Prompt Building, and wraps up the section, so this is the right moment to review your notes and project files before moving on.

Tips for getting the most out of it

• Commit your work to Git after finishing the lecture so you always have a working checkpoint to return to.
• If something fails, read the full error message first, then compare your code line by line with the project files.

Lecture overview

This lecture contains the downloadable project files for the section Functions, Methods in Python. The archive holds the source code and resources used throughout the section, so you can follow along, compare your work with a known-good version, or jump straight to a working example.

What you will learn

• Where to find the downloadable files for this section (Resources / downloadable materials)
• How the files map to the individual lectures
• How to open and run the code on your own machine
• How to use the reference code to debug your own version

What we do in this lecture

1. Download the zip file attached to this lecture
2. Extract it and open the folder in PyCharm (or VS Code)
3. Create a virtual environment and install the dependencies (for example pip install -r requirements.txt where a requirements file is included)
4. Compare the reference code with the code you wrote during the section

Why this matters

Having a working reference version lets you debug faster: when something does not behave as expected, you can compare your code with the finished version instead of guessing.

Practice task

Run the project from the downloaded files and compare it with your own version. Note at least one difference and understand why it was written that way.

By the end of this lecture you will be able to

• Set up and run the reference code for this section locally
• Use the project files to check and fix your own work

Where this fits in the course

This lecture belongs to the section Functions, Methods in Python. This section is about writing reusable logic. You move from scripts that run top to bottom to well-structured functions, learn how Python resolves names and arguments, and pick up functional tools and packages that make code shorter and more modular. It comes at the end of the section and gives you the reference code for the lectures from How to access the presentation and Project files to Test your Functions, Methods Skills.

Tips for getting the most out of it

• Keep a short notes file with the commands, snippets and prompts you use here; you will reuse them later in the course.
• Use an AI assistant to explain any line you are unsure about, but always verify the answer by running the code yourself.

Lecture overview

A quick reminder before we start object-oriented programming: you can get a free JetBrains IDE licence to use professional tools throughout the course. If the course has been helpful so far, we would also really appreciate an honest rating, as it helps other learners find it.

Why this matters

Good tooling makes OOP much easier to explore, with features such as class hierarchies, go-to-definition and refactoring support.

Where this fits in the course

This lecture belongs to the section Object Oriented Programming (OOP) in Python. This section teaches object-oriented programming the way it is used in real frameworks: classes and objects, inheritance, mixins, special methods, composition and dataclasses. Django models, FastAPI/Pydantic schemas and Flask extensions all rely heavily on these ideas. It opens this section and sets the direction for the lectures that follow, and prepares you for the next lecture, OOP Introduction, Classes, Objects & Naming Conventions.

Lecture overview

This lecture introduces object-oriented programming in Python. You will learn what classes and objects are, define your first class with an __init__ method, add attributes and instance methods, and understand what self really refers to. We also cover Python naming conventions for classes, methods and attributes so your code looks professional from the start.

What you will learn

• What classes and objects are and why OOP is useful
• Defining a class and creating instances
• The __init__ method and instance attributes
• Instance methods and the self parameter
• Class naming conventions (PascalCase) and method naming (snake_case)
• Modelling real-world entities as objects

What we do in this lecture

1. Model a simple entity, such as a BankAccount or Student, as a class
2. Create several objects and call their methods
3. Compare procedural and object-oriented versions of the same logic

Why this matters

Every major Python framework is built on classes. Django models, class-based views, Pydantic models in FastAPI and many Flask extensions are all classes, so this is essential groundwork.

Practice task

Create a Book class with title, author and pages attributes and a method that returns a one-line summary. Create three books and print their summaries.

By the end of this lecture you will be able to

• Define classes and create objects
• Use instance attributes and methods correctly
• Follow Python naming conventions for OOP

Where this fits in the course

This lecture belongs to the section Object Oriented Programming (OOP) in Python. This section teaches object-oriented programming the way it is used in real frameworks: classes and objects, inheritance, mixins, special methods, composition and dataclasses. Django models, FastAPI/Pydantic schemas and Flask extensions all rely heavily on these ideas. It builds directly on the previous lecture, Get a JetBrains Free IDE, consider 5 start rating, and prepares you for the next lecture, Class Methods, Static Methods & Properties.

Tips for getting the most out of it

• Try changing one small thing at a time (a value, a name, a parameter) and predict the result before you run it.
• Commit your work to Git after finishing the lecture so you always have a working checkpoint to return to.

Lecture overview

Here we go beyond instance methods. You will learn the difference between instance methods, class methods and static methods, when to use each, and how to use class methods as alternative constructors. We then introduce properties, which let you control access to attributes with getters and setters while keeping a clean interface.

What you will learn

• Class attributes versus instance attributes
• @classmethod and the cls parameter
• Alternative constructors, for example from_string or from_dict
• @staticmethod for utility functions that belong to a class
• @property for computed and validated attributes
• Setters for controlled attribute updates

What we do in this lecture

1. Add a class-level counter to track instances
2. Build an alternative constructor with @classmethod
3. Use @property to validate values on assignment

Why this matters

Alternative constructors and properties are common patterns in production code and in frameworks. They make APIs of your classes cleaner and help enforce valid data.

Practice task

Create a Temperature class that stores Celsius internally, exposes a fahrenheit property with a setter, and provides a from_fahrenheit class method.

By the end of this lecture you will be able to

• Choose between instance, class and static methods
• Build alternative constructors
• Use properties for validation and computed values

Where this fits in the course

This lecture belongs to the section Object Oriented Programming (OOP) in Python. This section teaches object-oriented programming the way it is used in real frameworks: classes and objects, inheritance, mixins, special methods, composition and dataclasses. Django models, FastAPI/Pydantic schemas and Flask extensions all rely heavily on these ideas. It builds directly on the previous lecture, OOP Introduction, Classes, Objects & Naming Conventions, and prepares you for the next lecture, Inheritance & Polymorphism.

Tips for getting the most out of it

• Use an AI assistant to explain any line you are unsure about, but always verify the answer by running the code yourself.
• Pause the video after each new idea and reproduce it on your own machine before moving on.

Lecture overview

This lecture covers inheritance and polymorphism. You will create parent and child classes, override methods, use super() to extend parent behaviour, and see how polymorphism lets different objects be used through the same interface. We finish with practical guidance on when inheritance helps and when it creates unnecessary coupling.

What you will learn

• Parent and child classes
• Method overriding
• Extending behaviour with super()
• isinstance() and issubclass()
• Polymorphism and duck typing
• When inheritance is the right tool and when it is not

What we do in this lecture

1. Build a small class hierarchy, for example Employee, Manager and Developer
2. Override and extend methods using super()
3. Process a mixed list of objects polymorphically

Why this matters

Frameworks use inheritance extensively. For example, you inherit from models.Model in Django or BaseModel in Pydantic. Understanding what you are inheriting and how overriding works is key to customising framework behaviour safely.

Practice task

Create a Shape base class with an area method, then Circle, Rectangle and Triangle subclasses. Put them in a list and print each area in a single loop.

By the end of this lecture you will be able to

• Design simple inheritance hierarchies
• Use super() correctly
• Apply polymorphism to write flexible code

Where this fits in the course

This lecture belongs to the section Object Oriented Programming (OOP) in Python. This section teaches object-oriented programming the way it is used in real frameworks: classes and objects, inheritance, mixins, special methods, composition and dataclasses. Django models, FastAPI/Pydantic schemas and Flask extensions all rely heavily on these ideas. It builds directly on the previous lecture, Class Methods, Static Methods & Properties, and prepares you for the next lecture, Multiple Inheritance, MRO & Mixins.

Tips for getting the most out of it

• Rewatch the tricky parts at a slower speed; it is completely normal to need two passes on new material.
• Keep a short notes file with the commands, snippets and prompts you use here; you will reuse them later in the course.

Lecture overview

Now we look at more advanced inheritance. You will learn how multiple inheritance works in Python, how the Method Resolution Order (MRO) decides which method runs, and how the classic diamond problem is solved. We then use mixins, small focused classes that add reusable behaviour to other classes, which is a pattern you will see constantly in Django.

What you will learn

• Multiple inheritance syntax and behaviour
• Method Resolution Order and the C3 linearisation
• Inspecting MRO with ClassName.__mro__ or mro()
• The diamond problem and how super() cooperates
• Mixins as reusable building blocks
• Best practices to keep multiple inheritance manageable

What we do in this lecture

1. Create a diamond-shaped hierarchy and inspect its MRO
2. Build LoggingMixin and JsonMixin and combine them with a base class
3. Observe how cooperative super() calls flow through the MRO

Why this matters

Django class-based views are built almost entirely from mixins, such as LoginRequiredMixin. Understanding MRO lets you combine them correctly and debug surprising behaviour.

Practice task

Create a TimestampMixin that adds created_at and updated_at handling and a SerializeMixin that adds a to_dict method. Combine both in a Product class and test them.

By the end of this lecture you will be able to

• Explain and inspect the MRO
• Use mixins to share behaviour cleanly
• Avoid common pitfalls of multiple inheritance

Where this fits in the course

This lecture belongs to the section Object Oriented Programming (OOP) in Python. This section teaches object-oriented programming the way it is used in real frameworks: classes and objects, inheritance, mixins, special methods, composition and dataclasses. Django models, FastAPI/Pydantic schemas and Flask extensions all rely heavily on these ideas. It builds directly on the previous lecture, Inheritance & Polymorphism, and prepares you for the next lecture, Encapsulation & Special Methods.

Tips for getting the most out of it

• Commit your work to Git after finishing the lecture so you always have a working checkpoint to return to.
• If something fails, read the full error message first, then compare your code line by line with the project files.

Lecture overview

This lecture covers encapsulation and Python's special (dunder) methods. You will learn Python's conventions for protected and private attributes, how name mangling works, and how to make your own objects behave like built-in types by implementing methods such as __str__, __repr__, __eq__, __lt__, __len__ and __add__.

What you will learn

• Encapsulation and why it matters
• Protected (_name) and private (__name) conventions
• Name mangling
• __str__ and __repr__ for readable objects
• Comparison methods such as __eq__ and __lt__
• __len__, __getitem__ and operator overloading with __add__

What we do in this lecture

1. Hide internal state behind a clean public interface
2. Give objects a helpful string representation
3. Make objects comparable and sortable

Why this matters

Well-encapsulated classes are easier to change safely. Special methods are used everywhere in frameworks; for example, Django uses __str__ to display model instances in the admin panel.

Practice task

Create a Money class with amount and currency. Implement __repr__, __str__, __eq__, __add__ (only for the same currency) and __lt__, then sort a list of Money objects.

By the end of this lecture you will be able to

• Apply encapsulation conventions
• Implement key special methods
• Make custom objects behave like built-in types

Where this fits in the course

This lecture belongs to the section Object Oriented Programming (OOP) in Python. This section teaches object-oriented programming the way it is used in real frameworks: classes and objects, inheritance, mixins, special methods, composition and dataclasses. Django models, FastAPI/Pydantic schemas and Flask extensions all rely heavily on these ideas. It builds directly on the previous lecture, Multiple Inheritance, MRO & Mixins, and prepares you for the next lecture, Composition, Abstract Classes & Dataclasses.

Tips for getting the most out of it

• Pause the video after each new idea and reproduce it on your own machine before moving on.
• Try changing one small thing at a time (a value, a name, a parameter) and predict the result before you run it.

Lecture overview

In this lecture we explore three powerful design tools: composition, abstract base classes and dataclasses. You will see why composition (has-a) is often preferred over inheritance (is-a), define abstract classes that enforce an interface using the abc module, and use dataclasses to remove boilerplate from classes that mainly hold data.

What you will learn

• Composition versus inheritance
• Building objects from smaller collaborating objects
• Abstract base classes with ABC and @abstractmethod
• Enforcing interfaces in a team codebase
• @dataclass, default values and field()
• Frozen dataclasses and automatic __eq__ and __repr__

What we do in this lecture

1. Refactor an inheritance-heavy design into composition
2. Define an abstract PaymentProcessor with concrete implementations
3. Replace a boilerplate class with a dataclass

Why this matters

These patterns lead to flexible, testable designs. Dataclasses are also a close relative of the Pydantic models you will use in FastAPI, so this lecture prepares you for data validation later.

Practice task

Design a Car that is composed of Engine and Wheel objects, define an abstract Notifier with EmailNotifier and SmsNotifier implementations, and model an Order as a dataclass.

By the end of this lecture you will be able to

• Choose composition over inheritance where appropriate
• Define and implement abstract interfaces
• Use dataclasses to simplify data-holding classes

Where this fits in the course

This lecture belongs to the section Object Oriented Programming (OOP) in Python. This section teaches object-oriented programming the way it is used in real frameworks: classes and objects, inheritance, mixins, special methods, composition and dataclasses. Django models, FastAPI/Pydantic schemas and Flask extensions all rely heavily on these ideas. It builds directly on the previous lecture, Encapsulation & Special Methods, and prepares you for the next lecture, OOP Use Cases & Practice.

Tips for getting the most out of it

• Keep a short notes file with the commands, snippets and prompts you use here; you will reuse them later in the course.
• Use an AI assistant to explain any line you are unsure about, but always verify the answer by running the code yourself.

Lecture overview

This practice lecture brings all the OOP concepts together through practical use cases. We model small real-world systems, decide which classes to create, choose between inheritance and composition, and apply encapsulation, special methods and dataclasses in context, using AI assistance where it adds value.

What you will learn

• Class modelling from requirements
• Choosing between inheritance and composition
• Applying encapsulation and properties
• Using dataclasses and special methods in context
• Reviewing AI-generated class designs

What we do in this lecture

1. Model a library or inventory system with several classes
2. Refactor the design for clarity and testability
3. Ask an AI tool to review the design and critique its suggestions

Why this matters

Designing classes from a requirement is exactly what architects do. Practising this on small systems builds the judgement you need for Django models and service layers later.

Practice task

Design a simple ride-booking system (Rider, Driver, Ride, PaymentMethod). Draw the relationships first, then implement the classes and write a script that books and completes a ride.

By the end of this lecture you will be able to

• Turn requirements into a class design
• Justify your OOP design decisions
• Critically review AI-generated designs

Where this fits in the course

This lecture belongs to the section Object Oriented Programming (OOP) in Python. This section teaches object-oriented programming the way it is used in real frameworks: classes and objects, inheritance, mixins, special methods, composition and dataclasses. Django models, FastAPI/Pydantic schemas and Flask extensions all rely heavily on these ideas. It builds directly on the previous lecture, Composition, Abstract Classes & Dataclasses, and prepares you for the next lecture, Test your Object Oriented Programming (OOP) in Python Skills.

Tips for getting the most out of it

• If something fails, read the full error message first, then compare your code line by line with the project files.
• Rewatch the tricky parts at a slower speed; it is completely normal to need two passes on new material.

Lecture overview

Test your object-oriented programming skills. This lecture walks through a series of challenges on classes, methods, properties, inheritance, MRO, mixins, special methods, composition and dataclasses. Attempt each one yourself before watching the solution.

What you will learn

• Self-assessment across all OOP topics
• Common OOP mistakes and misconceptions
• Reading and predicting output of class hierarchies
• Choosing the right OOP tool for a problem

Why this matters

OOP concepts appear constantly in the framework sections. Making sure they are solid now makes Django models, class-based views and Pydantic schemas feel familiar instead of confusing.

Practice task

Solve every challenge before watching the answer, and write a short note explaining why the correct solution works.

By the end of this lecture you will be able to

• Confirm your understanding of Python OOP
• Identify OOP topics to revise

Where this fits in the course

This lecture belongs to the section Object Oriented Programming (OOP) in Python. This section teaches object-oriented programming the way it is used in real frameworks: classes and objects, inheritance, mixins, special methods, composition and dataclasses. Django models, FastAPI/Pydantic schemas and Flask extensions all rely heavily on these ideas. It builds directly on the previous lecture, OOP Use Cases & Practice, and wraps up the section, so this is the right moment to review your notes and project files before moving on.

Tips for getting the most out of it

• Try changing one small thing at a time (a value, a name, a parameter) and predict the result before you run it.
• Commit your work to Git after finishing the lecture so you always have a working checkpoint to return to.

Lecture overview

This lecture contains the downloadable project files for the section Object Oriented Programming (OOP) in Python. The archive holds the source code and resources used throughout the section, so you can follow along, compare your work with a known-good version, or jump straight to a working example.

What you will learn

• Where to find the downloadable files for this section (Resources / downloadable materials)
• How the files map to the individual lectures
• How to open and run the code on your own machine
• How to use the reference code to debug your own version

What we do in this lecture

1. Download the zip file attached to this lecture
2. Extract it and open the folder in PyCharm (or VS Code)
3. Create a virtual environment and install the dependencies (for example pip install -r requirements.txt where a requirements file is included)
4. Compare the reference code with the code you wrote during the section

Why this matters

Having a working reference version lets you debug faster: when something does not behave as expected, you can compare your code with the finished version instead of guessing.

Practice task

Run the project from the downloaded files and compare it with your own version. Note at least one difference and understand why it was written that way.

By the end of this lecture you will be able to

• Set up and run the reference code for this section locally
• Use the project files to check and fix your own work

Where this fits in the course

This lecture belongs to the section Object Oriented Programming (OOP) in Python. This section teaches object-oriented programming the way it is used in real frameworks: classes and objects, inheritance, mixins, special methods, composition and dataclasses. Django models, FastAPI/Pydantic schemas and Flask extensions all rely heavily on these ideas. It comes at the end of the section and gives you the reference code for the lectures from Get a JetBrains Free IDE, consider 5 star rating to Test your Object Oriented Programming (OOP) in Python Skills.

Tips for getting the most out of it

• Keep a short notes file with the commands, snippets and prompts you use here; you will reuse them later in the course.
• Use an AI assistant to explain any line you are unsure about, but always verify the answer by running the code yourself.

Lecture overview

In this lecture we take a short look at DubNext, a real product built with heavy use of vibe coding and AI-assisted development. You will see what the product does, how AI tools accelerated its development, and which decisions still needed a human architect. It is a practical illustration of the approach this course teaches.

What you will learn

• What vibe coding is and where it works well
• How AI tools can speed up a real product build
• Where human architecture decisions were still essential
• Lessons learned from building a production product with AI

Why this matters

Seeing a real product built this way makes the AI-first mindset concrete and shows both the potential and the limits of AI-assisted development.

Practice task

Write down one product idea you could prototype with vibe coding and list the parts where you would still need to make careful architecture decisions yourself.

Where this fits in the course

This lecture belongs to the section Advanced Python Essentials - Exceptions, File IO, Regex & More. This section covers the skills that separate scripts from production-ready code: robust error handling, logging and debugging, file and path handling, structured data formats and regular expressions. It opens this section and sets the direction for the lectures that follow, and prepares you for the next lecture, Advanced Exception Handling & Custom Exceptions.

Lecture overview

This lecture takes error handling beyond the basics. You will review Python's exception hierarchy, use try, except, else and finally properly, catch specific exceptions instead of everything, re-raise and chain exceptions, and design your own custom exception classes that make errors meaningful for your application.

What you will learn

• The Python exception hierarchy
• try / except / else / finally and when each block runs
• Catching specific exceptions versus broad except clauses
• Raising and re-raising exceptions, and exception chaining with from
• Designing custom exception classes
• Cleaning up resources reliably

What we do in this lecture

1. Handle file, conversion and lookup errors gracefully
2. Build a small hierarchy of custom exceptions for an app
3. Chain exceptions to preserve the original error context

Why this matters

APIs and web apps must fail gracefully and return clear errors. Custom exceptions map naturally to HTTP error responses in Django, FastAPI and Flask, which you will see later in the course.

Practice task

Create InsufficientFundsError and AccountLockedError for a banking module, raise them from a withdraw function, and handle them with user-friendly messages.

By the end of this lecture you will be able to

• Write robust error-handling code
• Create meaningful custom exceptions
• Avoid anti-patterns such as bare except clauses

Where this fits in the course

This lecture belongs to the section Advanced Python Essentials - Exceptions, File IO, Regex & More. This section covers the skills that separate scripts from production-ready code: robust error handling, logging and debugging, file and path handling, structured data formats and regular expressions. It builds directly on the previous lecture, The DubNext Project - The power of Vibe coding, and prepares you for the next lecture, Logging, Debugging & Error Management in Python.

Tips for getting the most out of it

• Use an AI assistant to explain any line you are unsure about, but always verify the answer by running the code yourself.
• Pause the video after each new idea and reproduce it on your own machine before moving on.

Lecture overview

Here you will learn how professionals find and fix problems. We replace print debugging with Python's logging module, configure log levels, handlers and formatters, and write logs to both the console and a file. We then look at practical debugging strategies, including using the PyCharm debugger with breakpoints and step-through execution.

What you will learn

• Why logging is better than print statements
• Log levels: DEBUG, INFO, WARNING, ERROR and CRITICAL
• Loggers, handlers and formatters
• Logging to files and the console at the same time
• Logging exceptions with stack traces
• Using the PyCharm debugger with breakpoints, stepping and watches

What we do in this lecture

1. Configure logging for a small application
2. Log an exception with full context
3. Debug a faulty function step by step in PyCharm

Why this matters

In production you cannot attach a debugger, so logs are your main source of truth. Good logging habits make deployed Django, FastAPI and Flask applications far easier to support.

Practice task

Add logging to one of your earlier scripts with INFO messages for normal operations and ERROR messages for failures, written to both the console and app.log.

By the end of this lecture you will be able to

• Configure Python logging properly
• Diagnose issues using logs and the debugger
• Choose appropriate log levels

Where this fits in the course

This lecture belongs to the section Advanced Python Essentials - Exceptions, File IO, Regex & More. This section covers the skills that separate scripts from production-ready code: robust error handling, logging and debugging, file and path handling, structured data formats and regular expressions. It builds directly on the previous lecture, Advanced Exception Handling & Custom Exceptions, and prepares you for the next lecture, Advanced File IO, Context Managers & Paths.

Tips for getting the most out of it

• Rewatch the tricky parts at a slower speed; it is completely normal to need two passes on new material.
• Keep a short notes file with the commands, snippets and prompts you use here; you will reuse them later in the course.

Lecture overview

This lecture covers working with files and file systems. You will read and write text and binary files, use context managers (the with statement) to handle resources safely, and use pathlib for clean, cross-platform path handling. We also look at the os and shutil modules for creating, moving, copying and deleting files and folders.

What you will learn

• File modes: read, write, append and binary
• Context managers and the with statement
• Writing your own context manager
• pathlib.Path for cross-platform paths
• Listing, creating and moving files and directories
• os and shutil for file-system operations

What we do in this lecture

1. Read and write text files safely
2. Organise a folder of files automatically using pathlib
3. Create a simple custom context manager

Why this matters

Uploading files, storing media, reading configuration and processing logs are all everyday tasks in web applications. Correct resource handling prevents leaks and data corruption.

Practice task

Write a script that scans a Downloads-style folder and moves files into subfolders by extension (images, documents, archives), logging each move.

By the end of this lecture you will be able to

• Handle files safely with context managers
• Use pathlib for robust path handling
• Automate file-system tasks

Where this fits in the course

This lecture belongs to the section Advanced Python Essentials - Exceptions, File IO, Regex & More. This section covers the skills that separate scripts from production-ready code: robust error handling, logging and debugging, file and path handling, structured data formats and regular expressions. It builds directly on the previous lecture, Logging, Debugging & Error Management in Python, and prepares you for the next lecture, Structured Data, CSV, JSON & Large Files in Python.

Tips for getting the most out of it

• Commit your work to Git after finishing the lecture so you always have a working checkpoint to return to.
• If something fails, read the full error message first, then compare your code line by line with the project files.

Lecture overview

In this lecture we work with structured data. You will read and write CSV files with the csv module, parse and generate JSON, and learn techniques for processing files that are too large to load into memory at once, such as line-by-line reading and generators. We also look briefly at configuration files and pickle, including its security caveats.

What you will learn

• Reading and writing CSV with csv.reader, DictReader and DictWriter
• Parsing and producing JSON with the json module
• Converting between CSV and JSON
• Streaming large files with iterators and generators
• Configuration files and when to use them
• pickle and why it must never be used with untrusted data

What we do in this lecture

1. Load a CSV dataset and export it as JSON
2. Process a large file without loading it fully
3. Store and load application settings from a config file

Why this matters

JSON is the language of web APIs, and CSV is the most common data exchange format in business. Handling both efficiently is essential for building REST APIs and data features.

Practice task

Take a CSV of sales records, compute totals per product, and save the result as a nicely formatted JSON file. Make your script work line by line so it could handle millions of rows.

By the end of this lecture you will be able to

• Read and write CSV and JSON confidently
• Process large files efficiently
• Use configuration files safely

Where this fits in the course

This lecture belongs to the section Advanced Python Essentials - Exceptions, File IO, Regex & More. This section covers the skills that separate scripts from production-ready code: robust error handling, logging and debugging, file and path handling, structured data formats and regular expressions. It builds directly on the previous lecture, Advanced File IO, Context Managers & Paths, and prepares you for the next lecture, Regular Expressions in Python.

Tips for getting the most out of it

• Pause the video after each new idea and reproduce it on your own machine before moving on.
• Try changing one small thing at a time (a value, a name, a parameter) and predict the result before you run it.

Lecture overview

This lecture introduces regular expressions in Python. You will learn the core regex syntax, use the re module to search, match, find all occurrences and substitute text, and work with groups to extract structured information from unstructured text. We finish with common validation patterns such as email addresses and phone numbers.

What you will learn

• Regex building blocks: characters, classes, quantifiers and anchors
• re.search, re.match, re.fullmatch, re.findall and re.sub
• Capturing groups and named groups
• Compiling patterns for reuse
• Common validation patterns
• When regex is the wrong tool

What we do in this lecture

1. Extract dates and emails from a block of text
2. Validate user input with regex patterns
3. Clean and normalise messy text with re.sub

Why this matters

Regex is used for input validation, URL routing (Django uses regex-style path converters), log analysis and data cleaning. A little regex knowledge saves a lot of manual string code.

Practice task

Write a function that extracts all order IDs in the format ORD-12345 and all prices such as 19.99 from a block of email text and returns them as structured data.

By the end of this lecture you will be able to

• Write and read common regex patterns
• Use the re module effectively
• Validate and extract data from text

Where this fits in the course

This lecture belongs to the section Advanced Python Essentials - Exceptions, File IO, Regex & More. This section covers the skills that separate scripts from production-ready code: robust error handling, logging and debugging, file and path handling, structured data formats and regular expressions. It builds directly on the previous lecture, Structured Data, CSV, JSON & Large Files in Python, and prepares you for the next lecture, Advanced Use Cases & Practice.

Tips for getting the most out of it

• Keep a short notes file with the commands, snippets and prompts you use here; you will reuse them later in the course.
• Use an AI assistant to explain any line you are unsure about, but always verify the answer by running the code yourself.

Lecture overview

This practice lecture combines everything from the section in realistic scenarios. We build small utilities such as a log analyser and a file organiser that use exception handling, logging, file I/O, structured data and regex together, with AI assistance for the repetitive parts.

What you will learn

• Combining error handling, logging and file I/O
• Log file analysis with regex
• Automated file organisation
• Producing structured JSON reports
• Reviewing AI-generated utility code

What we do in this lecture

1. Build a log analyser that reports error counts by type
2. Build a file organiser with logging and error handling
3. Export results as JSON

Why this matters

Small internal tools like these are exactly what developers build every day, and they exercise all the robustness skills needed for production services.

Practice task

Extend the log analyser so it also reports the busiest hour of the day and writes a summary report to a JSON file.

By the end of this lecture you will be able to

• Build robust utility scripts
• Apply advanced Python essentials together

Where this fits in the course

This lecture belongs to the section Advanced Python Essentials - Exceptions, File IO, Regex & More. This section covers the skills that separate scripts from production-ready code: robust error handling, logging and debugging, file and path handling, structured data formats and regular expressions. It builds directly on the previous lecture, Regular Expressions in Python, and prepares you for the next lecture, Test your Python Essentials - Exceptions, File IO, Regex skills.

Tips for getting the most out of it

• If something fails, read the full error message first, then compare your code line by line with the project files.
• Rewatch the tricky parts at a slower speed; it is completely normal to need two passes on new material.

Lecture overview

Test your knowledge of exceptions, logging, file I/O, structured data and regular expressions. Work through each challenge yourself first, then compare your solution with the walkthrough.

What you will learn

• Self-assessment on advanced Python essentials
• Tricky exception flow questions
• File and data handling challenges
• Regex pattern challenges

Why this matters

These skills make your applications reliable. Confirming them now means fewer mysterious errors when you build real web applications.

Practice task

Solve each challenge in your IDE before watching the solution, and add any weak areas to your revision list.

By the end of this lecture you will be able to

• Confirm your mastery of advanced Python essentials

Where this fits in the course

This lecture belongs to the section Advanced Python Essentials - Exceptions, File IO, Regex & More. This section covers the skills that separate scripts from production-ready code: robust error handling, logging and debugging, file and path handling, structured data formats and regular expressions. It builds directly on the previous lecture, Advanced Use Cases & Practice, and wraps up the section, so this is the right moment to review your notes and project files before moving on.

Tips for getting the most out of it

• Try changing one small thing at a time (a value, a name, a parameter) and predict the result before you run it.
• Commit your work to Git after finishing the lecture so you always have a working checkpoint to return to.

Lecture overview

This lecture contains the downloadable project files for the section Advanced Python Essentials - Exceptions, File IO, Regex & More. The archive holds the source code and resources used throughout the section, so you can follow along, compare your work with a known-good version, or jump straight to a working example.

What you will learn

• Where to find the downloadable files for this section (Resources / downloadable materials)
• How the files map to the individual lectures
• How to open and run the code on your own machine
• How to use the reference code to debug your own version

What we do in this lecture

1. Download the zip file attached to this lecture
2. Extract it and open the folder in PyCharm (or VS Code)
3. Create a virtual environment and install the dependencies (for example pip install -r requirements.txt where a requirements file is included)
4. Compare the reference code with the code you wrote during the section

Why this matters

Having a working reference version lets you debug faster: when something does not behave as expected, you can compare your code with the finished version instead of guessing.

Practice task

Run the project from the downloaded files and compare it with your own version. Note at least one difference and understand why it was written that way.

By the end of this lecture you will be able to

• Set up and run the reference code for this section locally
• Use the project files to check and fix your own work

Where this fits in the course

This lecture belongs to the section Advanced Python Essentials - Exceptions, File IO, Regex & More. This section covers the skills that separate scripts from production-ready code: robust error handling, logging and debugging, file and path handling, structured data formats and regular expressions. It comes at the end of the section and gives you the reference code for the lectures from The DubNext Project - The power of Vibe coding to Test your Python Essentials - Exceptions, File IO, Regex skills.

Tips for getting the most out of it

• Keep a short notes file with the commands, snippets and prompts you use here; you will reuse them later in the course.
• Use an AI assistant to explain any line you are unsure about, but always verify the answer by running the code yourself.

Lecture overview

Before we start NumPy, this short lecture shows WisperLocal, a desktop application built with vibe coding. You will see what the app does, how AI tools were used to build it quickly, and what it teaches about shipping small, useful tools with AI assistance.

What you will learn

• A real desktop app built with AI-assisted development
• How local AI models can power useful tools
• What vibe coding sped up and what still needed care

Why this matters

It shows how the skills in this course combine into real products, and it previews the local-AI ideas you will use later in the Local LLM project.

Where this fits in the course

This lecture belongs to the section NumPy for Data Science in Python. This section introduces NumPy, the numerical foundation of the Python data and AI ecosystem. You will learn to work with arrays efficiently, which prepares you for Pandas, machine learning libraries and AI features in your applications. It opens this section and sets the direction for the lectures that follow, and prepares you for the next lecture, NumPy Fundamentals - Arrays, Creation & Data Types.

Lecture overview

This lecture introduces NumPy and its core object, the ndarray. You will learn why NumPy arrays are faster and more memory-efficient than Python lists, create arrays in several ways, and inspect their shape, number of dimensions, size and data type.

What you will learn

• What NumPy is and why it is so widely used
• Python lists versus NumPy arrays
• Creating arrays with array, zeros, ones, arange, linspace and full
• Array attributes: shape, ndim, size and dtype
• Choosing and converting data types
• 1D, 2D and 3D arrays

What we do in this lecture

1. Create arrays from lists and from generator functions
2. Inspect and change data types
3. Compare performance of lists and arrays on a simple calculation

Why this matters

NumPy underpins Pandas, scikit-learn, PyTorch and many AI tools. Understanding arrays makes working with data and model outputs in your web applications much easier.

Practice task

Create a 5x5 array of zeros, a 3x4 array of random integers and an array of 10 evenly spaced values between 0 and 1. Print the shape, dtype and size of each.

By the end of this lecture you will be able to

• Create and inspect NumPy arrays
• Explain why arrays outperform lists for numeric work

Where this fits in the course

This lecture belongs to the section NumPy for Data Science in Python. This section introduces NumPy, the numerical foundation of the Python data and AI ecosystem. You will learn to work with arrays efficiently, which prepares you for Pandas, machine learning libraries and AI features in your applications. It builds directly on the previous lecture, Vide coded Desktop App - WisperLocal, and prepares you for the next lecture, Indexing, Slicing & Iterating in NumPy.

Tips for getting the most out of it

• Rewatch the tricky parts at a slower speed; it is completely normal to need two passes on new material.
• Keep a short notes file with the commands, snippets and prompts you use here; you will reuse them later in the course.
• Using an AI assistant to explain vectorised expressions line by line can help, but always check array shapes yourself with .shape.

Lecture overview

Here you will learn how to access and navigate data inside NumPy arrays. We cover indexing and slicing in one and several dimensions, boolean and fancy indexing, iterating over arrays, and the important difference between views and copies.

What you will learn

• Indexing in 1D and multi-dimensional arrays
• Slicing rows, columns and sub-arrays
• Boolean masks for filtering
• Fancy indexing with integer arrays
• Iterating with loops and nditer
• Views versus copies and why it matters

What we do in this lecture

1. Select rows, columns and blocks of a 2D array
2. Filter values with boolean conditions
3. Show how modifying a view changes the original array

Why this matters

Selecting the right data is the first step in any analysis. The views-versus-copies distinction also prevents confusing bugs where data changes unexpectedly.

Practice task

Given a 2D array of exam scores (students by subjects), select all scores above 80, the scores of the third student, and the average of the last subject.

By the end of this lecture you will be able to

• Index and slice arrays confidently
• Filter data with boolean masks
• Avoid view-versus-copy bugs

Where this fits in the course

This lecture belongs to the section NumPy for Data Science in Python. This section introduces NumPy, the numerical foundation of the Python data and AI ecosystem. You will learn to work with arrays efficiently, which prepares you for Pandas, machine learning libraries and AI features in your applications. It builds directly on the previous lecture, NumPy Fundamentals - Arrays, Creation & Data Types, and prepares you for the next lecture, Array Shape Manipulation & Restructuring.

Tips for getting the most out of it

• Commit your work to Git after finishing the lecture so you always have a working checkpoint to return to.
• If something fails, read the full error message first, then compare your code line by line with the project files.
• Using an AI assistant to explain vectorised expressions line by line can help, but always check array shapes yourself with .shape.

Lecture overview

This lecture is about changing the structure of arrays. You will reshape arrays, flatten and ravel them, transpose matrices, add and remove dimensions, and combine or split arrays with stacking and splitting functions.

What you will learn

• reshape and the -1 shortcut
• flatten versus ravel
• transpose and swapping axes
• expand_dims and squeeze
• vstack, hstack and concatenate
• split, hsplit and vsplit

What we do in this lecture

1. Reshape a flat dataset into a matrix
2. Stack several arrays into one
3. Split a dataset into training and test portions

Why this matters

Data rarely arrives in the shape you need. Machine learning models and image data in particular require precise shapes, so reshaping is a daily task.

Practice task

Create an array of 24 numbers, reshape it into 4x6, 2x3x4 and 6x4 forms, then stack two of them together and split the result back into parts.

By the end of this lecture you will be able to

• Reshape, transpose and combine arrays
• Prepare data in the shape a task requires

Where this fits in the course

This lecture belongs to the section NumPy for Data Science in Python. This section introduces NumPy, the numerical foundation of the Python data and AI ecosystem. You will learn to work with arrays efficiently, which prepares you for Pandas, machine learning libraries and AI features in your applications. It builds directly on the previous lecture, Indexing, Slicing & Iterating in NumPy, and prepares you for the next lecture, Universal Functions & Broadcasting in NumPy.

Tips for getting the most out of it

• Pause the video after each new idea and reproduce it on your own machine before moving on.
• Try changing one small thing at a time (a value, a name, a parameter) and predict the result before you run it.
• Using an AI assistant to explain vectorised expressions line by line can help, but always check array shapes yourself with .shape.

Lecture overview

In this lecture you will learn how NumPy performs fast element-wise operations. We cover universal functions (ufuncs), vectorisation instead of Python loops, and broadcasting, the set of rules that lets NumPy combine arrays of different shapes.

What you will learn

• Universal functions such as add, sqrt, exp and log
• Vectorised arithmetic versus Python loops
• Broadcasting rules step by step
• Common broadcasting errors and how to fix them
• Performance comparison of loops and vectorised code

What we do in this lecture

1. Replace a loop-based calculation with vectorised code
2. Apply broadcasting to normalise a dataset
3. Time both approaches to compare performance

Why this matters

Vectorisation is what makes Python fast enough for data and AI work. Thinking in arrays rather than loops leads to shorter, faster and clearer code.

Practice task

Normalise each column of a 100x3 random array so that it has a mean of 0 and a standard deviation of 1, using broadcasting and no explicit loops.

By the end of this lecture you will be able to

• Use ufuncs and vectorised operations
• Understand and apply broadcasting rules

Where this fits in the course

This lecture belongs to the section NumPy for Data Science in Python. This section introduces NumPy, the numerical foundation of the Python data and AI ecosystem. You will learn to work with arrays efficiently, which prepares you for Pandas, machine learning libraries and AI features in your applications. It builds directly on the previous lecture, Array Shape Manipulation & Restructuring, and prepares you for the next lecture, Aggregation, Statistics & Sorting in NumPy.

Tips for getting the most out of it

• Keep a short notes file with the commands, snippets and prompts you use here; you will reuse them later in the course.
• Use an AI assistant to explain any line you are unsure about, but always verify the answer by running the code yourself.
• Using an AI assistant to explain vectorised expressions line by line can help, but always check array shapes yourself with .shape.

Lecture overview

This lecture covers summarising and ordering data with NumPy. You will compute sums, means, medians, standard deviations, minimums and maximums, apply them along specific axes, and sort arrays or find the positions of values with sort, argsort, argmax and argmin.

What you will learn

• Aggregation functions: sum, mean, median, std, var, min and max
• Aggregating along axes
• Cumulative functions such as cumsum
• sort and argsort
• argmax, argmin and where
• Handling NaN values in aggregations

What we do in this lecture

1. Compute statistics per row and per column
2. Rank items with argsort
3. Find top-N values in a dataset

Why this matters

Summary statistics are the first thing you calculate on any dataset, and they are the basis of dashboards and analytics endpoints you might expose from a backend.

Practice task

For a dataset of monthly sales (products by months), find the best month for each product, the best-selling product overall and the average monthly sales.

By the end of this lecture you will be able to

• Summarise arrays with aggregation functions
• Sort and rank data efficiently

Where this fits in the course

This lecture belongs to the section NumPy for Data Science in Python. This section introduces NumPy, the numerical foundation of the Python data and AI ecosystem. You will learn to work with arrays efficiently, which prepares you for Pandas, machine learning libraries and AI features in your applications. It builds directly on the previous lecture, Universal Functions & Broadcasting in NumPy, and prepares you for the next lecture, Linear Algebra with NumPy.

Tips for getting the most out of it

• If something fails, read the full error message first, then compare your code line by line with the project files.
• Rewatch the tricky parts at a slower speed; it is completely normal to need two passes on new material.
• Using an AI assistant to explain vectorised expressions line by line can help, but always check array shapes yourself with .shape.

Lecture overview

This lecture introduces linear algebra with NumPy. You will perform matrix multiplication, compute dot products, transposes, determinants and inverses, solve systems of linear equations, and get a brief introduction to decompositions used in data science.

What you will learn

• Vectors and matrices in NumPy
• Dot product and matrix multiplication (@ operator)
• Transpose, determinant and inverse
• Solving linear systems with linalg.solve
• Eigenvalues and decompositions at a high level
• Where linear algebra appears in machine learning

What we do in this lecture

1. Multiply matrices and verify results
2. Solve a system of equations
3. Relate these operations to simple ML examples

Why this matters

Linear algebra is the language of machine learning and AI. Even a basic understanding helps you reason about embeddings, similarity search and model behaviour.

Practice task

Solve the system 2x + 3y = 13 and x - y = -1 with NumPy, then verify the solution by substituting it back.

By the end of this lecture you will be able to

• Perform core linear algebra operations with NumPy
• Solve linear systems programmatically

Where this fits in the course

This lecture belongs to the section NumPy for Data Science in Python. This section introduces NumPy, the numerical foundation of the Python data and AI ecosystem. You will learn to work with arrays efficiently, which prepares you for Pandas, machine learning libraries and AI features in your applications. It builds directly on the previous lecture, Aggregation, Statistics & Sorting in NumPy, and prepares you for the next lecture, Random Sampling & Distributions in NumPy.

Tips for getting the most out of it

• Try changing one small thing at a time (a value, a name, a parameter) and predict the result before you run it.
• Commit your work to Git after finishing the lecture so you always have a working checkpoint to return to.
• Using an AI assistant to explain vectorised expressions line by line can help, but always check array shapes yourself with .shape.

Lecture overview

Here you will learn to generate random data with NumPy. We use the modern random Generator API, set seeds for reproducibility, sample from uniform, normal and other distributions, shuffle data and use random sampling for simulations and synthetic test data.

What you will learn

• The default_rng Generator API
• Seeds and reproducible results
• Uniform, normal, binomial and Poisson distributions
• Random choice, shuffle and permutation
• Simulation and synthetic data generation

What we do in this lecture

1. Generate reproducible random datasets
2. Simulate a simple experiment, such as dice rolls
3. Create synthetic test data for an application

Why this matters

Synthetic data is invaluable for testing APIs and dashboards before real data exists, and random sampling underpins many ML techniques.

Practice task

Simulate 10,000 rolls of two dice and plot or print how often each total appears. Compare the result with the theoretical probabilities.

By the end of this lecture you will be able to

• Generate reproducible random data
• Use distributions for simulation and testing

Where this fits in the course

This lecture belongs to the section NumPy for Data Science in Python. This section introduces NumPy, the numerical foundation of the Python data and AI ecosystem. You will learn to work with arrays efficiently, which prepares you for Pandas, machine learning libraries and AI features in your applications. It builds directly on the previous lecture, Linear Algebra with NumPy, and prepares you for the next lecture, NumPy Use Cases & Practice.

Tips for getting the most out of it

• Use an AI assistant to explain any line you are unsure about, but always verify the answer by running the code yourself.
• Pause the video after each new idea and reproduce it on your own machine before moving on.
• Using an AI assistant to explain vectorised expressions line by line can help, but always check array shapes yourself with .shape.

Lecture overview

This practice lecture applies NumPy to realistic problems. We analyse student scores, work with image-like arrays, and perform simple portfolio calculations, combining indexing, reshaping, broadcasting and aggregation.

What you will learn

• Student score analysis
• Treating images as arrays of pixels
• Simple financial or portfolio calculations
• Combining multiple NumPy techniques in one workflow

What we do in this lecture

1. Analyse a score dataset end to end
2. Manipulate a small image-like array (flip, crop, brighten)
3. Calculate portfolio returns from price data

Why this matters

Practising on realistic problems shows how the separate NumPy tools combine into a complete workflow.

Practice task

Take a small grayscale image represented as a 2D array, crop it, flip it horizontally and increase its brightness without exceeding 255.

By the end of this lecture you will be able to

• Apply NumPy to complete real-world tasks

Where this fits in the course

This lecture belongs to the section NumPy for Data Science in Python. This section introduces NumPy, the numerical foundation of the Python data and AI ecosystem. You will learn to work with arrays efficiently, which prepares you for Pandas, machine learning libraries and AI features in your applications. It builds directly on the previous lecture, Random Sampling & Distributions in NumPy, and prepares you for the next lecture, Test your NumPy for Data Science in Python Skills.

Tips for getting the most out of it

• Rewatch the tricky parts at a slower speed; it is completely normal to need two passes on new material.
• Keep a short notes file with the commands, snippets and prompts you use here; you will reuse them later in the course.
• Using an AI assistant to explain vectorised expressions line by line can help, but always check array shapes yourself with .shape.

Lecture overview

Test your NumPy skills. This lecture walks through challenges on array creation, indexing, reshaping, broadcasting, aggregation, linear algebra and random sampling. Solve each challenge first, then compare with the walkthrough.

What you will learn

• Self-assessment across all NumPy topics
• Shape and broadcasting puzzles
• Vectorisation challenges

Why this matters

NumPy knowledge will support your work with Pandas and AI features. Confirming it now avoids confusion later.

Practice task

Solve every challenge before watching the solution and note any topic you need to revisit.

By the end of this lecture you will be able to

• Confirm your NumPy proficiency

Where this fits in the course

This lecture belongs to the section NumPy for Data Science in Python. This section introduces NumPy, the numerical foundation of the Python data and AI ecosystem. You will learn to work with arrays efficiently, which prepares you for Pandas, machine learning libraries and AI features in your applications. It builds directly on the previous lecture, NumPy Use Cases & Practice, and prepares you for the next lecture, Get in Touch with me on LinkedIn.

Tips for getting the most out of it

• Commit your work to Git after finishing the lecture so you always have a working checkpoint to return to.
• If something fails, read the full error message first, then compare your code line by line with the project files.

Lecture overview

A short lecture on how to connect with me on LinkedIn. I regularly share updates about new courses, AI tools, career tips and projects, and you are welcome to reach out and connect.

Why this matters

Building a professional network is an important part of growing your career as a developer.

Where this fits in the course

This lecture belongs to the section NumPy for Data Science in Python. This section introduces NumPy, the numerical foundation of the Python data and AI ecosystem. You will learn to work with arrays efficiently, which prepares you for Pandas, machine learning libraries and AI features in your applications. It builds directly on the previous lecture, Test your NumPy for Data Science in Python Skills, and wraps up the section, so this is the right moment to review your notes and project files before moving on.

Lecture overview

This lecture contains the downloadable project files for the section NumPy for Data Science in Python. The archive holds the source code and resources used throughout the section, so you can follow along, compare your work with a known-good version, or jump straight to a working example.

What you will learn

• Where to find the downloadable files for this section (Resources / downloadable materials)
• How the files map to the individual lectures
• How to open and run the code on your own machine
• How to use the reference code to debug your own version

What we do in this lecture

1. Download the zip file attached to this lecture
2. Extract it and open the folder in PyCharm (or VS Code)
3. Create a virtual environment and install the dependencies (for example pip install -r requirements.txt where a requirements file is included)
4. Compare the reference code with the code you wrote during the section

Why this matters

Having a working reference version lets you debug faster: when something does not behave as expected, you can compare your code with the finished version instead of guessing.

Practice task

Run the project from the downloaded files and compare it with your own version. Note at least one difference and understand why it was written that way.

By the end of this lecture you will be able to

• Set up and run the reference code for this section locally
• Use the project files to check and fix your own work

Where this fits in the course

This lecture belongs to the section NumPy for Data Science in Python. This section introduces NumPy, the numerical foundation of the Python data and AI ecosystem. You will learn to work with arrays efficiently, which prepares you for Pandas, machine learning libraries and AI features in your applications. It comes at the end of the section and gives you the reference code for the lectures from Vibe Coded Desktop App - WhisperLocal to Get in Touch with me on LinkedIn.

Tips for getting the most out of it

• Keep a short notes file with the commands, snippets and prompts you use here; you will reuse them later in the course.
• Use an AI assistant to explain any line you are unsure about, but always verify the answer by running the code yourself.

Lecture overview

A quick note before we start Pandas: building a course of this size takes a lot of work. If you are enjoying it, please consider leaving an honest rating. It makes a real difference and helps other learners find the course.

Why this matters

Your feedback directly shapes future updates to the course.

Where this fits in the course

This lecture belongs to the section Pandas for Data Science in Python. This section teaches Pandas, the standard Python library for working with tabular data. It builds on NumPy and gives you the tools to load, explore, clean and analyse real datasets, skills you will reuse when building data-driven features and reports. It opens this section and sets the direction for the lectures that follow, and prepares you for the next lecture, Pandas Fundamentals - Series, DataFrames & Basic Operations.

Lecture overview

This lecture introduces the two core Pandas data structures: Series and DataFrame. You will create them from lists, dictionaries and NumPy arrays, inspect them with head, tail, info and describe, and perform basic operations such as adding columns, renaming, sorting and simple calculations.

What you will learn

• What Pandas is and how it relates to NumPy
• Series: one-dimensional labelled data
• DataFrame: two-dimensional labelled tables
• Creating DataFrames from dicts, lists and arrays
• Inspecting data with head, tail, info, describe and shape
• Adding, renaming and removing columns
• Sorting and basic column calculations

What we do in this lecture

1. Build a DataFrame of employees or products
2. Explore it with inspection methods
3. Add calculated columns and sort the data

Why this matters

DataFrames are the most common way to handle tabular data in Python, from quick analysis to preparing data for APIs, reports and machine learning.

Practice task

Create a DataFrame of ten products with name, category, price and stock. Add a stock_value column (price times stock) and sort by it in descending order.

By the end of this lecture you will be able to

• Create and inspect Series and DataFrames
• Perform basic DataFrame operations

Where this fits in the course

This lecture belongs to the section Pandas for Data Science in Python. This section teaches Pandas, the standard Python library for working with tabular data. It builds on NumPy and gives you the tools to load, explore, clean and analyse real datasets, skills you will reuse when building data-driven features and reports. It builds directly on the previous lecture, Difficult to build this, Cosider 5 Star rating, and prepares you for the next lecture, Data IO with Pandas.

Tips for getting the most out of it

• Commit your work to Git after finishing the lecture so you always have a working checkpoint to return to.
• If something fails, read the full error message first, then compare your code line by line with the project files.

Lecture overview

Here you will learn how to get data into and out of Pandas. We read CSV, Excel and JSON files, control options such as separators, headers, column types and dates, perform an initial inspection, and write results back to files in different formats.

What you will learn

• read_csv and its most useful options
• Reading Excel and JSON files
• Parsing dates and setting column types
• Handling headers, separators and encodings
• Initial inspection after loading
• Writing data with to_csv, to_excel and to_json

What we do in this lecture

1. Load a real CSV dataset and inspect it
2. Fix common loading problems such as wrong types and dates
3. Export a cleaned subset to a new file

Why this matters

Almost every data task starts by loading a file and ends by exporting a result. These are also the building blocks of import and export features in web applications.

Practice task

Load a CSV dataset, parse the date column correctly, keep only the last 30 days of records, and save the result as both CSV and JSON.

By the end of this lecture you will be able to

• Load data from common file formats
• Control parsing options
• Export results in the required format

Where this fits in the course

This lecture belongs to the section Pandas for Data Science in Python. This section teaches Pandas, the standard Python library for working with tabular data. It builds on NumPy and gives you the tools to load, explore, clean and analyse real datasets, skills you will reuse when building data-driven features and reports. It builds directly on the previous lecture, Pandas Fundamentals - Series, DataFrames & Basic Operations, and prepares you for the next lecture, Indexing, Selection & Filtering in Pandas.

Tips for getting the most out of it

• Pause the video after each new idea and reproduce it on your own machine before moving on.
• Try changing one small thing at a time (a value, a name, a parameter) and predict the result before you run it.

Lecture overview

This lecture focuses on selecting exactly the data you need. You will select columns, use loc for label-based selection and iloc for position-based selection, filter rows with boolean conditions, combine conditions, and use helper methods such as isin, between and query.

What you will learn

• Selecting single and multiple columns
• loc for label-based selection
• iloc for position-based selection
• Boolean filtering and combining conditions with & and |
• isin, between and query
• Setting and resetting the index

What we do in this lecture

1. Select specific rows and columns with loc and iloc
2. Filter a dataset using multiple conditions
3. Use query for readable filtering

Why this matters

Filtering and selecting data is the Pandas equivalent of writing database queries. The same thinking applies when you query data through Django ORM or SQLAlchemy later.

Practice task

From a sales dataset, select all orders from two specific regions with a value above 500 in the last quarter, showing only customer, region and value.

By the end of this lecture you will be able to

• Use loc and iloc correctly
• Filter data with complex conditions

Where this fits in the course

This lecture belongs to the section Pandas for Data Science in Python. This section teaches Pandas, the standard Python library for working with tabular data. It builds on NumPy and gives you the tools to load, explore, clean and analyse real datasets, skills you will reuse when building data-driven features and reports. It builds directly on the previous lecture, Data IO with Pandas, and prepares you for the next lecture, Data Cleaning & Wrangling in Pandas.

Tips for getting the most out of it

• Keep a short notes file with the commands, snippets and prompts you use here; you will reuse them later in the course.
• Use an AI assistant to explain any line you are unsure about, but always verify the answer by running the code yourself.

Lecture overview

Real data is messy. In this lecture you will detect and handle missing values, remove duplicates, fix data types, clean text columns, rename and restructure columns, and apply transformations so a dataset becomes reliable enough for analysis or loading into an application.

What you will learn

• Detecting missing values with isna and info
• Dropping versus filling missing values
• Removing duplicates
• Fixing data types and parsing dates
• Cleaning text with the .str accessor
• apply and map for custom transformations
• Grouping and aggregating with groupby

What we do in this lecture

1. Audit a messy dataset for problems
2. Clean missing, duplicate and badly typed values
3. Summarise the cleaned data with groupby

Why this matters

Bad data leads to wrong reports and broken features. Data cleaning is one of the most common real-world tasks for Python developers working with business data.

Practice task

Take a messy customer dataset with missing emails, duplicated rows and inconsistent country names, clean it, and produce a count of customers per country.

By the end of this lecture you will be able to

• Diagnose data quality problems
• Clean and transform messy datasets
• Summarise data with groupby

Where this fits in the course

This lecture belongs to the section Pandas for Data Science in Python. This section teaches Pandas, the standard Python library for working with tabular data. It builds on NumPy and gives you the tools to load, explore, clean and analyse real datasets, skills you will reuse when building data-driven features and reports. It builds directly on the previous lecture, Indexing, Selection & Filtering in Pandas, and prepares you for the next lecture, Pandas Use Cases & Practice.

Tips for getting the most out of it

• If something fails, read the full error message first, then compare your code line by line with the project files.
• Rewatch the tricky parts at a slower speed; it is completely normal to need two passes on new material.

Lecture overview

This practice lecture applies Pandas to a complete mini analysis workflow. We load a dataset, explore it, filter and clean it, calculate summaries and export the results, using AI assistance for exploratory questions while verifying every result.

What you will learn

• End-to-end analysis workflow
• Loading, filtering and cleaning in sequence
• Summaries with groupby and pivot tables
• Exporting results for reports
• Verifying AI-suggested Pandas code

What we do in this lecture

1. Run a complete mini analysis on a sample dataset
2. Answer business questions with Pandas
3. Export a summary report

Why this matters

Putting all the steps together is how you build confidence to handle real datasets in your own projects and in data-driven web features.

Practice task

Using a sales dataset, answer three business questions of your choice (for example: best month, top product per region, average basket size) and export the answers to a single Excel file with one sheet per question.

By the end of this lecture you will be able to

• Carry out a complete data analysis with Pandas

Where this fits in the course

This lecture belongs to the section Pandas for Data Science in Python. This section teaches Pandas, the standard Python library for working with tabular data. It builds on NumPy and gives you the tools to load, explore, clean and analyse real datasets, skills you will reuse when building data-driven features and reports. It builds directly on the previous lecture, Data Cleaning & Wrangling in Pandas, and prepares you for the next lecture, Test your Pandas Skills.

Tips for getting the most out of it

• Try changing one small thing at a time (a value, a name, a parameter) and predict the result before you run it.
• Commit your work to Git after finishing the lecture so you always have a working checkpoint to return to.

Lecture overview

Test your Pandas skills. This lecture covers challenges on DataFrame creation, data loading, selection, filtering, cleaning and aggregation. Try each challenge on your own first, then compare with the walkthrough.

What you will learn

• Self-assessment across all Pandas topics
• Selection and filtering puzzles
• Cleaning and groupby challenges

Why this matters

Solid Pandas skills let you handle data confidently in any Python role, whether you are building APIs, dashboards or AI features.

Practice task

Solve each challenge before watching the solution and revisit any lecture where you struggled.

By the end of this lecture you will be able to

• Confirm your Pandas proficiency

Where this fits in the course

This lecture belongs to the section Pandas for Data Science in Python. This section teaches Pandas, the standard Python library for working with tabular data. It builds on NumPy and gives you the tools to load, explore, clean and analyse real datasets, skills you will reuse when building data-driven features and reports. It builds directly on the previous lecture, Pandas Use Cases & Practice, and wraps up the section, so this is the right moment to review your notes and project files before moving on.

Tips for getting the most out of it

• Use an AI assistant to explain any line you are unsure about, but always verify the answer by running the code yourself.
• Pause the video after each new idea and reproduce it on your own machine before moving on.

Lecture overview

This lecture contains the downloadable project files for the section Pandas for Data Science in Python. The archive holds the source code and resources used throughout the section, so you can follow along, compare your work with a known-good version, or jump straight to a working example.

What you will learn

• Where to find the downloadable files for this section (Resources / downloadable materials)
• How the files map to the individual lectures
• How to open and run the code on your own machine
• How to use the reference code to debug your own version

What we do in this lecture

1. Download the zip file attached to this lecture
2. Extract it and open the folder in PyCharm (or VS Code)
3. Create a virtual environment and install the dependencies (for example pip install -r requirements.txt where a requirements file is included)
4. Compare the reference code with the code you wrote during the section

Why this matters

Having a working reference version lets you debug faster: when something does not behave as expected, you can compare your code with the finished version instead of guessing.

Practice task

Run the project from the downloaded files and compare it with your own version. Note at least one difference and understand why it was written that way.

By the end of this lecture you will be able to

• Set up and run the reference code for this section locally
• Use the project files to check and fix your own work

Where this fits in the course

This lecture belongs to the section Pandas for Data Science in Python. This section teaches Pandas, the standard Python library for working with tabular data. It comes at the end of the section and gives you the reference code for the lectures from Difficult to build this, Consider 5 Star rating to Test your Pandas Skills.

Tips for getting the most out of it

• Keep a short notes file with the commands, snippets and prompts you use here; you will reuse them later in the course.
• Use an AI assistant to explain any line you are unsure about, but always verify the answer by running the code yourself.

Lecture overview

This lecture introduces AI-powered development. You will learn what large language models are at a practical level, how chatbots and IDE coding assistants differ, what they are good and bad at, and how to fit them into a professional workflow. We also look at how applications call LLM APIs, which prepares you for the AI projects later in the course.

What you will learn

• What LLMs are and how they generate text and code
• Chat assistants versus IDE-integrated coding assistants
• Strengths and weaknesses of AI for coding
• Calling LLM APIs from Python applications (OpenAI-style and Anthropic-style workflows)
• Tokens, context windows and cost at a high level
• Privacy and security considerations

What we do in this lecture

1. Compare several AI tools on the same coding task
2. Make a simple API call to an LLM from Python
3. Discuss where AI fits in a development workflow

Why this matters

AI tools are changing how software is built. Understanding how they work helps you use them effectively and build AI features into your own applications.

Practice task

Write a short Python script that sends a question to an LLM API and prints the answer. Store the API key in an environment variable, not in your code.

By the end of this lecture you will be able to

• Explain how LLM-based tools work at a practical level
• Integrate an LLM API call into a Python script
• Use AI tools responsibly

Where this fits in the course

This lecture belongs to the section AI Tools, LLM Chatbots & Building Applications with AI. This section turns AI into a practical development tool. You learn how LLM chatbots and coding assistants work, how to prompt them for high-quality code, and how to structure projects so that AI-assisted development stays maintainable. It opens this section and sets the direction for the lectures that follow, and prepares you for the next lecture, Mastering Prompting for Code Generation.

Tips for getting the most out of it

• Commit your work to Git after finishing the lecture so you always have a working checkpoint to return to.
• If something fails, read the full error message first, then compare your code line by line with the project files.

Lecture overview

This lecture goes deep into prompting for code generation. You will use prompt templates, few-shot examples, explicit constraints and step-by-step instructions to get better code. We also cover using AI for debugging, refactoring, writing tests and generating documentation, and how to spot hallucinated APIs or unwanted dependencies.

What you will learn

• Reusable prompt templates for coding tasks
• Few-shot prompting with examples
• Specifying constraints: libraries, versions, style and error handling
• Prompting for debugging and refactoring
• Generating tests and documentation with AI
• Spotting hallucinations and unrequested dependencies

What we do in this lecture

1. Build a reusable prompt template for creating endpoints or functions
2. Use AI to find and fix a bug
3. Generate unit tests and review them critically

Why this matters

The quality of AI-generated code depends heavily on the prompt. Strong prompting skills save hours of rework and help you avoid problems like unnecessary third-party libraries or API keys sneaking into your project.

Practice task

Create a prompt template you can reuse for generating a Python function with type hints, docstrings, error handling and pytest tests. Try it on two different tasks.

By the end of this lecture you will be able to

• Write advanced prompts for code generation
• Use AI for debugging, refactoring and testing
• Detect and correct AI mistakes

Where this fits in the course

This lecture belongs to the section AI Tools, LLM Chatbots & Building Applications with AI. This section turns AI into a practical development tool. You learn how LLM chatbots and coding assistants work, how to prompt them for high-quality code, and how to structure projects so that AI-assisted development stays maintainable. It builds directly on the previous lecture, Introduction to AI-Powered Development & LLM Chatbots, and prepares you for the next lecture, Project Structure & Best Practices for AI-Assisted Development.

Tips for getting the most out of it

• Pause the video after each new idea and reproduce it on your own machine before moving on.
• Try changing one small thing at a time (a value, a name, a parameter) and predict the result before you run it.

Lecture overview

This lecture shows how to structure projects so that AI-assisted development stays clean and maintainable. You will learn a sensible folder structure, how to separate configuration from code, how to manage dependencies and environment variables, and how clear structure and documentation give AI tools better context.

What you will learn

• A clean Python project layout
• Separating configuration, secrets and code
• requirements files and dependency management
• Environment variables and .env files
• README and conventions that help both humans and AI
• Keeping AI-generated code consistent with the project style

What we do in this lecture

1. Scaffold a well-structured Python project
2. Move secrets into environment variables
3. Write a short README that guides future AI prompts

Why this matters

Good structure is what keeps a project manageable as it grows. It also makes AI tools far more accurate because they can follow existing patterns instead of inventing new ones.

Practice task

Restructure one of your earlier scripts into a proper project with src, tests, a requirements file, a .env.example file and a README.

By the end of this lecture you will be able to

• Scaffold maintainable Python projects
• Manage configuration and dependencies properly
• Give AI tools good project context

Where this fits in the course

This lecture belongs to the section AI Tools, LLM Chatbots & Building Applications with AI. This section turns AI into a practical development tool. You learn how LLM chatbots and coding assistants work, how to prompt them for high-quality code, and how to structure projects so that AI-assisted development stays maintainable. It builds directly on the previous lecture, Mastering Prompting for Code Generation, and prepares you for the next lecture, Test yout AI Tools, LLM Chatbots Skills.

Tips for getting the most out of it

• Keep a short notes file with the commands, snippets and prompts you use here; you will reuse them later in the course.
• Use an AI assistant to explain any line you are unsure about, but always verify the answer by running the code yourself.

Lecture overview

Test your understanding of AI tools, LLM chatbots and prompting for development. This lecture includes practical challenges where you prompt, review and improve AI-generated code.

What you will learn

• Self-assessment on AI-assisted development
• Prompt improvement exercises
• Reviewing AI output for errors and risks

Why this matters

You will rely on these skills throughout the framework and project sections, so it is worth confirming them now.

Practice task

Complete every challenge, and for each one write down one risk you spotted in the AI output.

By the end of this lecture you will be able to

• Confirm your AI-assisted development skills

Where this fits in the course

This lecture belongs to the section AI Tools, LLM Chatbots & Building Applications with AI. This section turns AI into a practical development tool. You learn how LLM chatbots and coding assistants work, how to prompt them for high-quality code, and how to structure projects so that AI-assisted development stays maintainable. It builds directly on the previous lecture, Project Structure & Best Practices for AI-Assisted Development, and wraps up the section, so this is the right moment to review your notes and project files before moving on.

Tips for getting the most out of it

• If something fails, read the full error message first, then compare your code line by line with the project files.
• Rewatch the tricky parts at a slower speed; it is completely normal to need two passes on new material.

Lecture overview

This lecture contains the downloadable project files for the section AI Tools, LLM Chatbots & Building Applications with AI. The archive holds the source code and resources used throughout the section, so you can follow along, compare your work with a known-good version, or jump straight to a working example.

What you will learn

• Where to find the downloadable files for this section (Resources / downloadable materials)
• How the files map to the individual lectures
• How to open and run the code on your own machine
• How to use the reference code to debug your own version

What we do in this lecture

1. Download the zip file attached to this lecture
2. Extract it and open the folder in PyCharm (or VS Code)
3. Create a virtual environment and install the dependencies (for example pip install -r requirements.txt where a requirements file is included)
4. Compare the reference code with the code you wrote during the section

Why this matters

Having a working reference version lets you debug faster: when something does not behave as expected, you can compare your code with the finished version instead of guessing.

Practice task

Run the project from the downloaded files and compare it with your own version. Note at least one difference and understand why it was written that way.

By the end of this lecture you will be able to

• Set up and run the reference code for this section locally
• Use the project files to check and fix your own work

Where this fits in the course

This lecture belongs to the section AI Tools, LLM Chatbots & Building Applications with AI. This section turns AI into a practical development tool. You learn how LLM chatbots and coding assistants work, how to prompt them for high-quality code, and how to structure projects so that AI-assisted development stays maintainable. It comes at the end of the section and gives you the reference code for the lectures from Introduction to AI-Powered Development & LLM Chatbots to Test your AI Tools, LLM Chatbots Skills.

Tips for getting the most out of it

• Commit your work to Git after finishing the lecture so you always have a working checkpoint to return to.
• If something fails, read the full error message first, then compare your code line by line with the project files.

Lecture overview

This lecture introduces RESTful APIs. You will learn what an API is, what REST stands for, how clients and servers communicate over HTTP, and how resources, endpoints, HTTP methods, status codes and JSON payloads fit together. We also make simple API requests from Python to see it in practice.

What you will learn

• What an API is and why it matters
• REST principles: resources, statelessness and a uniform interface
• HTTP methods: GET, POST, PUT, PATCH and DELETE
• Endpoints and URL design
• Requests, responses, headers and JSON bodies
• Status codes at a high level
• Calling an API from Python with requests

What we do in this lecture

1. Explore a public API with a browser and a Python script
2. Read and interpret JSON responses
3. Map CRUD operations to HTTP methods

Why this matters

Every backend you build in this course, whether with Django, FastAPI or Flask, will expose REST APIs that your React frontend consumes. This lecture provides the vocabulary for everything that follows.

Practice task

Use Python requests to call a free public API (for example a weather or quotes API), print selected fields from the JSON response and handle a failed request gracefully.

By the end of this lecture you will be able to

• Explain the core ideas of REST
• Make and interpret API requests from Python

Where this fits in the course

This lecture belongs to the section RestFul API Basics. This short section introduces the fundamentals of RESTful APIs, the communication layer between the backends and frontends you will build later in the course. It opens this section and sets the direction for the lectures that follow, and prepares you for the next lecture, Test your RestFul API Skills.

Tips for getting the most out of it

• Pause the video after each new idea and reproduce it on your own machine before moving on.
• Try changing one small thing at a time (a value, a name, a parameter) and predict the result before you run it.

Lecture overview

Test your understanding of RESTful API basics. This lecture covers questions and small challenges on HTTP methods, endpoints, status codes and working with JSON.

What you will learn

• Self-assessment on REST concepts
• Choosing the right HTTP method
• Interpreting responses and status codes

Why this matters

A solid grasp of REST makes the backend and frontend sections much easier to follow.

Practice task

Answer each question before watching the explanation, and design the endpoints for a simple to-do API as an extra exercise.

By the end of this lecture you will be able to

• Confirm your understanding of REST fundamentals

Where this fits in the course

This lecture belongs to the section RestFul API Basics. This short section introduces the fundamentals of RESTful APIs, the communication layer between the backends and frontends you will build later in the course. It builds directly on the previous lecture, RESTful API Basics, and wraps up the section, so this is the right moment to review your notes and project files before moving on.

Tips for getting the most out of it

• Keep a short notes file with the commands, snippets and prompts you use here; you will reuse them later in the course.
• Use an AI assistant to explain any line you are unsure about, but always verify the answer by running the code yourself.

Lecture overview

A short lecture on how to connect with me on Instagram, where I share quick tips, behind-the-scenes content from course production and updates on new projects.

Why this matters

Following along is an easy way to keep learning between sessions.

Where this fits in the course

This lecture belongs to the section Architecture Design patterns. This section introduces the design patterns and architectural thinking that you will apply when building real applications, and then puts them to work by building a blog application with AI assistance. It opens this section and sets the direction for the lectures that follow, and prepares you for the next lecture, Important Design patterns to explore.

Lecture overview

This lecture introduces the design patterns that matter most for Python developers building real applications. You will learn what design patterns are, why they exist, and explore patterns such as Singleton, Factory, Strategy, Observer, Decorator, Repository and MVC/MVT, with practical Python examples and guidance on when each one helps and when it adds unnecessary complexity.

What you will learn

• What design patterns are and the problems they solve
• Creational patterns: Singleton and Factory
• Behavioural patterns: Strategy and Observer
• Structural patterns: Decorator and Adapter
• Repository and service layer patterns for data access
• MVC and Django's MVT architecture
• Avoiding over-engineering

What we do in this lecture

1. Implement several patterns in small Python examples
2. Identify patterns used inside popular frameworks
3. Discuss trade-offs of each pattern

Why this matters

Patterns give you a shared vocabulary with other developers and help you design systems that are easier to extend and test. They also help you recognise how Django, FastAPI and Flask are structured internally.

Practice task

Implement a payment module using the Strategy pattern with at least two payment methods, and a notification system using the Observer pattern.

By the end of this lecture you will be able to

• Recognise and apply common design patterns
• Explain when a pattern is appropriate
• Relate patterns to framework architecture

Where this fits in the course

This lecture belongs to the section Architecture Design patterns. This section introduces the design patterns and architectural thinking that you will apply when building real applications, and then puts them to work by building a blog application with AI assistance. It builds directly on the previous lecture, Get in touch with me on Instagram, and prepares you for the next lecture, Building Blog application using AI.

Tips for getting the most out of it

• If something fails, read the full error message first, then compare your code line by line with the project files.
• Rewatch the tricky parts at a slower speed; it is completely normal to need two passes on new material.

Lecture overview

In this lecture we put design and prompting skills together by building a blog application with AI assistance. You will plan the features and structure first, then use AI to generate the code step by step, reviewing and correcting it as an architect would. This sets the scene for the framework paths that follow.

What you will learn

• Planning features and data models before prompting
• Breaking a project into AI-sized tasks
• Generating models, routes and templates with AI
• Reviewing AI output for structure, security and unwanted dependencies
• Iterating on a working prototype

What we do in this lecture

1. Plan the blog's features, data and pages
2. Build the application incrementally with AI prompts
3. Review and refactor the generated code

Why this matters

This is the workflow you will use in real projects: architecture first, then AI-accelerated implementation, then careful review. It also shows why understanding frameworks matters even when AI writes much of the code.

Practice task

Add a comments feature to the blog using AI assistance. Before you prompt, write down the data model and the routes you expect, then check whether the AI followed your plan.

By the end of this lecture you will be able to

• Plan and build a small application with AI assistance
• Review AI-generated code with an architect's eye

Where this fits in the course

This lecture belongs to the section Architecture Design patterns. This section introduces the design patterns and architectural thinking that you will apply when building real applications, and then puts them to work by building a blog application with AI assistance. It builds directly on the previous lecture, Important Design patterns to explore, and prepares you for the next lecture, Test your Architecture Design patterns Skills.

Tips for getting the most out of it

• Try changing one small thing at a time (a value, a name, a parameter) and predict the result before you run it.
• Commit your work to Git after finishing the lecture so you always have a working checkpoint to return to.

Lecture overview

Test your knowledge of design patterns and architectural thinking. This lecture presents scenarios where you choose the most suitable pattern and explain why.

What you will learn

• Self-assessment on design patterns
• Choosing patterns for real scenarios
• Recognising over-engineering

Why this matters

Being able to justify design decisions is a core architect skill and a common interview topic.

Practice task

For each scenario, decide on a pattern and write one sentence justifying it before watching the answer.

By the end of this lecture you will be able to

• Confirm your understanding of design patterns

Where this fits in the course

This lecture belongs to the section Architecture Design patterns. This section introduces the design patterns and architectural thinking that you will apply when building real applications, and then puts them to work by building a blog application with AI assistance. It builds directly on the previous lecture, Building Blog application using AI, and wraps up the section, so this is the right moment to review your notes and project files before moving on.

Tips for getting the most out of it

• Use an AI assistant to explain any line you are unsure about, but always verify the answer by running the code yourself.
• Pause the video after each new idea and reproduce it on your own machine before moving on.