Python Advance Course details

Ravindra Gudhekar
3 min readSep 2, 2023


An advanced Python course can cover a wide range of topics, depending on your goals and interests. Here’s a list of advanced Python topics that you might consider including in such a course:

Object-Oriented Programming (OOP):

- Advanced concepts like inheritance, polymorphism, and encapsulation.

- Design patterns (e.g., Singleton, Factory, Observer).

2. Functional Programming:

- Lambda functions and higher-order functions.

- Functional programming libraries like `functools`.

- Immutable data structures and functional programming paradigms.

3. Concurrency and Parallelism:

- Threading and multi-threading.

- Multiprocessing and parallelism with the `multiprocessing` module.

- Asynchronous programming with `asyncio` for concurrent I/O operations.

4. Decorators and Metaprogramming:

- Writing and using decorators.

- Metaclasses and class manipulation.

5. Generators and Iterators:

- Writing custom iterators and generators.

- Coroutines and asynchronous generators.

6. Databases and ORM:

- Database connectivity using libraries like SQLAlchemy or Django ORM.

- Advanced querying and optimization.

7. Web Development:

- Building RESTful APIs with Flask or Django.

- Websockets and real-time applications.

- Authentication and security considerations.

8. Data Science and Machine Learning:

- Advanced data manipulation with libraries like NumPy and pandas.

- Machine learning libraries like scikit-learn and TensorFlow.

- Deep learning with PyTorch or Keras.

9. Testing and Test-Driven Development (TDD):

- Writing unit tests and integration tests.

- Test frameworks like `unittest` and `pytest`.

- TDD principles and practices.

10. Debugging and Profiling:

- Using debugging tools (e.g., `pdb`, `ipdb`).

- Profiling code for performance optimization.

11. Packaging and Distribution:

- Creating Python packages and modules.

- Distributing packages via PyPI.

- Virtual environments and dependency management (e.g., `venv`, `pipenv`).

12. Advanced Topics in Python Standard Library:

- Exploring less common modules like `collections`, `itertools`, `asyncio`, etc.

13. Concurrency and Parallelism:

- Understanding the Global Interpreter Lock (GIL).

- Parallel processing with `multiprocessing` and `concurrent.futures`.

- Asynchronous programming with `asyncio`.

14. Performance Optimization:

- Profiling and optimizing code with tools like `cProfile`.

- Writing C extensions with Cython or using Python’s C API.

15. Python Best Practices:

- Code style and PEP 8 compliance.

- Documentation using docstrings.

- Version control with Git and collaborative development.

16. Advanced Topics in Python Ecosystem:

- Exploring specific libraries and frameworks according to the course’s focus, e.g., web development, data analysis, scientific computing, etc.

17. Advanced Python Features:

- Context managers and the `with` statement.

- Metaclasses and class customization.

- Function and class decorators.

18. Security:

- Common security vulnerabilities and best practices.

- Secure coding guidelines.

19. Advanced Topics in Python 3.7+:

- Latest features and improvements in the newer Python versions (async/await, f-strings, data classes, etc.).

20. Project Work:

- Collaborative coding on real-world projects to apply the advanced concepts learned during the course.

Remember that the specific topics you cover should align with the goals and interests of the participants and the overall objectives of the course. You can choose to focus on specific domains or technologies depending on the needs of your audience.

Programs :-

Creating a program for each advanced Python topic can be a comprehensive way to learn and practice these concepts. Here’s a simple example program for each of the topics listed:

1. Object-Oriented Programming (OOP):


class Animal:

def __init__(self, name): = name

def speak(self):


class Dog(Animal):

def speak(self):

return f”{} says Woof!”

dog = Dog(“Buddy”)



2. Functional Programming:


# Using lambda functions

square = lambda x: x ** 2


# Using functools

from functools import reduce

numbers = [1, 2, 3, 4, 5]

product = reduce(lambda x, y: x * y, numbers)



3. Concurrency and Parallelism (Threading):


import threading

def print_numbers():

for i in range(1, 6):

print(f”Number {i}”)

def print_letters():

for letter in ‘abcde’:

print(f”Letter {letter}”)

t1 = threading.Thread(target=print_numbers)

t2 = threading.Thread(target=print_letters)






4. Decorators:


def my_decorator(func):

def wrapper():

print(“Something is happening before the function is called.”)


print(“Something is happening after the function is called.”)

return wrapper


def say_hello():




5. Generators and Iterators:


def countdown(n):

while n > 0:

yield n

n -= 1

for i in countdown(5):



6. Databases and ORM (Using SQLAlchemy):


from sqlalchemy import create_engine, Column, Integer, String

from sqlalchemy.orm import sessionmaker

from sqlalchemy.ext.declarative import declarative_base

engine = create_engine(‘sqlite:///example.db’)

Session = sessionmaker(bind=engine)

Base = declarative_base()

class User(Base):

__tablename__ = ‘users’

id = Column(Integer, primary_key=True)

name = Column(String)


session = Session()

new_user = User(name=”John”)




These are simplified examples to illustrate each topic. In practice, you’d dive deeper and build more complex programs as you learn and gain proficiency in each area.



Ravindra Gudhekar

AI-powered translators use advanced language models and machine learning techniques to provide accurate and efficient translation services across a wide range.