Move beyond basic scripts
Develop reusable components and organize larger programs with clearer boundaries.
Programming · Beyond the fundamentals
Advanced Python
Move beyond basic scripts and develop a more thoughtful approach to Python application development. Explore reusable components, efficient data processing, concurrent workflows, tested APIs, and maintainable project structure.
Connect advanced language features with practical development tasks, then bring them together in a REST API capstone.
Course overview
Advanced Python focuses on writing clear, reusable, and production-oriented code. The learning path connects language features with application architecture, error handling, testing, and delivery.
Instead of treating decorators, generators, classes, and asynchronous programming as isolated topics, explore how they support real application workflows. Practice making design choices and explaining the trade-offs behind them.
What makes this course different?
- Connect advanced concepts with focused implementation tasks.
- Examine normal behavior, edge cases, and failure scenarios.
- Develop a testing mindset alongside application features.
- Organize code into understandable modules and interfaces.
- Document setup steps, design decisions, and limitations.
The goal is not simply to use more complex Python syntax. It is to choose appropriate techniques and build software that is easier to understand, test, and improve.
Before you begin
This course builds on basic Python programming and introductory object-oriented programming.
- Write functions with parameters and return values.
- Work with lists, dictionaries, tuples, and sets.
- Use loops, conditionals, and comprehensions.
- Create simple classes and understand instance methods.
- Read and write files and handle basic exceptions.
- Import modules and run Python programs from a terminal.
Readiness activity
Build a small command-line program that reads records from a JSON file, validates required fields, filters the records, and writes a summary. Split the logic into functions and handle missing files gracefully.
If these tasks are unfamiliar, review our Python Programming course before exploring this course.
Who can explore this course?
Explore API development
Connect Python code with HTTP endpoints, validation, documentation, and automated tests.
Improve workflow tools
Explore data pipelines, resource handling, concurrency, and dependable error reporting.
Refine existing applications
Revisit project structure, introduce tests, and document how your software works.
What you will learn
- Create reusable functions with closures and decorators.
- Build lazy processing workflows with iterators and generators.
- Use composition, dataclasses, and clear class interfaces.
- Manage resources with context managers and exception handling.
- Compare threading, multiprocessing, and asynchronous approaches.
- Design API endpoints with validation and structured errors.
- Write unit and integration tests with pytest.
- Organize application configuration, dependencies, and packaging.
- Investigate performance before attempting optimization.
- Explain implementation choices through project documentation.
Curriculum outline
Eight expanded modules connect Python language features with application-development practice. The exercises below provide practical directions for applying each topic.
01
Advanced functions and decorators
Explore functions as values and build reusable behavior without unnecessarily duplicating logic.
- Function scope, closures, and captured state.
- Higher-order functions and callable objects.
- Positional and keyword argument handling.
- Decorators with and without parameters.
- Preserving function metadata with functools.wraps.
- Separating reusable behavior from business logic.
Practice: Build a timing decorator and a configurable logging decorator, then test their behavior with successful and failing functions.
02
Iterators, generators, and data pipelines
Explore incremental processing and learn to reason about when values are produced and consumed.
- Iterable objects and the iterator protocol.
- Custom iterators with __iter__ and __next__.
- Generator functions and yield expressions.
- Generator expressions and chained processing.
- One-pass iteration and exhausted iterators.
- Streaming records instead of loading everything at once.
Practice: Create a file-processing pipeline that reads records, filters invalid entries, transforms values, and produces a summary.
03
Advanced OOP and application design
Design objects around responsibilities and keep application components understandable.
- Properties and controlled attribute access.
- Dataclasses for structured application data.
- Composition and appropriate inheritance.
- Abstract interfaces and typing protocols.
- Selected special methods and object behavior.
- Service and repository boundaries.
- Type hints as a communication tool.
Practice: Refactor a task-management program into data models, a service layer, and an interchangeable storage interface.
04
Context managers and resource handling
Make cleanup behavior explicit and manage resources even when operations fail.
- With statements and resource lifecycles.
- Class-based context managers.
- Generator-based context managers with contextlib.
- Exception propagation and cleanup paths.
- Custom exceptions and useful error messages.
- Temporary files and controlled resource ownership.
Practice: Build a managed temporary workspace and verify that cleanup occurs after both successful operations and raised exceptions.
05
Concurrency and asynchronous workflows
Compare approaches to coordinating multiple tasks and choose based on workload characteristics.
- Concurrency versus parallel execution.
- Threads, shared state, and synchronization.
- Process-based execution and communication overhead.
- ThreadPoolExecutor and ProcessPoolExecutor.
- Coroutines, async, await, and event loops.
- Task coordination, cancellation, and timeouts.
- Bounded concurrency and resource limits.
- Avoiding blocking work inside asynchronous workflows.
Practice: Compare sequential and concurrent versions of a batch-processing task. Record results and explain where the overhead or improvements come from.
06
API development and validation
Build a clear interface between clients and application logic using FastAPI concepts.
- HTTP methods, resource naming, and response codes.
- Path parameters, query parameters, and request bodies.
- Request validation and response models.
- Dependency injection and service boundaries.
- Consistent error responses and exception handling.
- Authentication and authorization concepts.
- Pagination and filtering design.
- API documentation and example requests.
Practice: Create task-management endpoints for creating, retrieving, updating, and deleting records with validation and clear errors.
07
Testing and quality checks
Turn requirements into repeatable checks and develop confidence when changing code.
- Unit tests and integration tests.
- pytest assertions, fixtures, and parametrization.
- Testing expected exceptions and boundary conditions.
- Mocks and controlled external dependencies.
- FastAPI TestClient and endpoint testing.
- Isolated test data and repeatable setup.
- Coverage reports and untested behavior.
- Formatting, linting, and basic static checks.
Practice: Test successful API requests, missing records, invalid payloads, and service-layer failures using isolated fixtures.
08
Packaging, delivery, and performance review
Prepare an application for repeatable setup, review its operational behavior, and document its delivery requirements.
- Project layout and package boundaries.
- Virtual environments and dependency management.
- Project metadata and packaging configuration.
- Environment-based configuration and secret handling.
- Logging and useful diagnostic information.
- Dockerfile and container workflow concepts.
- Profiling, benchmarking, and measured optimization.
- README documentation and deployment checklists.
Practice: Package the capstone, document its setup, run its tests, and investigate one measured performance bottleneck.
Practical exercise ideas
Use these focused projects to connect individual concepts before combining them in the capstone.
Decorator utility toolkit
Create timing and logging decorators with configurable behavior and clearly documented usage.
Review focus: metadata preservation, exception behavior, and small reusable interfaces.
Streaming record processor
Read a large generated dataset incrementally, validate records, and calculate summary statistics.
Review focus: one-pass processing, malformed records, and memory use.
Concurrent request collector
Collect responses from a controlled test service with bounded concurrency and clear failure reporting.
Review focus: timeouts, cancellation, resource limits, and repeatable measurements.
API regression test suite
Write tests that exercise normal requests, invalid inputs, missing resources, and updates.
Review focus: isolated fixtures, meaningful assertions, and regression protection.
Suggested eight-week learning plan
This is a proposed study sequence, not a confirmed timetable. Adjust the pace to your starting knowledge and available practice time.
| Week | Focus | Suggested milestone |
|---|---|---|
| 01 | Advanced functions | Create and test reusable decorators. |
| 02 | Iterators and generators | Build an incremental processing pipeline. |
| 03 | OOP and interfaces | Define capstone models and service boundaries. |
| 04 | Resource handling | Add cleanup behavior and structured exceptions. |
| 05 | Concurrency | Compare execution approaches in a focused exercise. |
| 06 | API development | Implement and document core API endpoints. |
| 07 | Automated testing | Test endpoints, edge cases, and service logic. |
| 08 | Packaging and review | Complete setup instructions and a project walkthrough. |
Bring the topics together
Capstone project
Production-oriented task-management REST API
Design and build an API for managing tasks and their status. Use the project to demonstrate validation, application structure, testing, logging, and delivery configuration.
Core feature scope
- Create tasks with validated names and descriptions.
- Retrieve individual tasks and paginated task lists.
- Update task information and completion status.
- Delete records with clear response behavior.
- Filter records by status or another defined field.
- Return consistent errors for invalid or missing records.
- Keep business logic separate from HTTP route handlers.
- Use a replaceable storage interface.
Quality and delivery requirements
- Automated tests for key endpoints and service behavior.
- Configuration loaded without committing secrets.
- Logs that support diagnosis without exposing sensitive data.
- API examples covering successful and unsuccessful requests.
- Clear dependency and local setup instructions.
- Container configuration as a delivery exercise.
- A README explaining architecture and known limitations.
Optional extensions
After completing the core scope, explore persistent database storage, authentication, user-level permissions, or a background processing workflow. Add extensions gradually and test each new behavior.
Production-oriented does not mean automatically production-ready. A real deployment also requires environment-specific security, operations, monitoring, and reliability review.
Project review checklist
Use these questions to review your work or organize a project demonstration.
- Can another developer understand the project structure?
- Do invalid requests produce useful, consistent errors?
- Are business rules tested independently of the API layer?
- Can tests run repeatedly without relying on old test data?
- Are resource cleanup and failure paths considered?
- Are dependencies and configuration requirements documented?
- Can you explain why you selected a concurrency approach?
- Do performance claims include actual measurements?
- Are limitations and unfinished features stated clearly?
- Can someone follow the README and run the application?
Tools and technologies
The core tool list connects Python development, API implementation, automated testing, version control, and container concepts.
- Python 3
- FastAPI concepts
- pytest
- Git
- Docker
Supporting topics
Explore virtual environments, type hints, configuration files, JSON data, command-line workflows, and standard-library tools as needed for the exercises.
Development setup
- A computer with a compatible Python 3 installation.
- A code editor and terminal access.
- A separate environment for project dependencies.
- Git for tracking changes and reviewing progress.
- A local workspace for exercises, tests, and documentation.
Learning outcomes
By working through the proposed activities, aim to demonstrate the following abilities:
- Explain and implement a decorator with a clear purpose.
- Build a generator-based pipeline and describe its behavior.
- Organize code around models, interfaces, and services.
- Handle resource cleanup and expected failure conditions.
- Compare concurrency approaches through a practical example.
- Implement a validated API with useful error responses.
- Write automated tests for normal and boundary cases.
- Prepare reproducible setup and project documentation.
Learning outcomes are objectives, not guarantees. Progress depends on your starting knowledge, practice, project scope, and review process.
Connect learning with your interests
Shape your project around the type of Python work you want to explore. Use the finished application to discuss your decisions, tests, and improvements.
- Python Backend Developer
- API Developer
- Automation Engineer
- Software Engineer
Portfolio presentation ideas
- Show the problem your project is designed to solve.
- Walk through a successful request and an error scenario.
- Explain the application layers and their responsibilities.
- Demonstrate a small selection of meaningful tests.
- Discuss a trade-off and what you would improve next.
These are professional interests, not promised job outcomes. A project can demonstrate learning but does not replace broader experience or role-specific requirements.
Frequently asked questions
Who is this course for?
This course is for learners with basic Python programming and object-oriented programming knowledge who want to explore advanced language features and application-development practices.
Is this a beginner Python course?
No. The course level is Intermediate to Advanced. If you are new to Python, review the Python Programming course first.
How long is the course?
The listed duration is 8 weeks. The suggested weekly plan on this page provides a learning sequence rather than a confirmed class timetable.
What should I know before starting?
Be comfortable with functions, collections, loops, simple classes, files, imports, and basic exception handling. Use the readiness activity to identify topics you need to revisit.
What is the capstone project?
The expanded capstone is a task-management REST API with validation, structured errors, tests, logging, and delivery configuration. Database persistence and authentication are optional extensions.
Does the course cover asynchronous Python?
The concurrency module includes coroutines, async and await, event loops, task coordination, cancellation, timeouts, and bounded concurrency.
Will I learn API testing?
The testing module includes pytest, fixtures, parametrization, and API endpoint tests using FastAPI TestClient. Exercises cover both valid requests and failure scenarios.
Which tools are included?
The core list includes Python 3, FastAPI concepts, pytest, Git, and Docker. Supporting exercises also use standard-library features and project configuration tools.
Do I need database knowledge?
You can begin the core capstone with a simple storage implementation. Database knowledge is useful if you choose to add persistent storage as an extension.
Which learning modes are listed?
The supplied course details list online and classroom learning. Browse the catalogue by your preferred mode and verify current availability before making enrollment plans.
Are fees or certification details included?
Fees, certification requirements, class schedules, and enrollment terms are not provided on this page. Check the academy's confirmed course information before enrolling.
Will completing this course guarantee a job?
No job outcome is guaranteed. Use the exercises and capstone to build demonstrable skills, explain your decisions, and identify areas for continued learning.
Give your Python learning direction
Build your next Python project
Review the prerequisites, choose a practical goal, and work through the curriculum with a project you can test, document, and improve.