Python Course Curriculum

Module 1: Programming (Python + Web Scraping)

Duration: 40 Hours

Topic 1.1: Python Fundamentals

Theory:

• Variables and Data Types

• Operators: Arithmetic, Logical, Comparison

• Conditional Statements (if/else)

• Loops: for, while Lab:

• Write Python programs using variables, loops, and conditions

• Create a mini ATM interface using control statements Scenarios:

• Calculate transaction charges based on balance and account type

• Generate alerts for low account balances

Tasks:

• Develop a Python script to classify transactions as 'low', 'medium', or 'high'

• Calculate interest on savings using loop-based logic Challenges:

• Handle input validation and type errors

• Optimize nested loops for processing large lists of transactions

Topic 1.2: Functions, Modules, and Classes

Theory:

• ·Defining Functions and Parameters

• Lambda Functions

• Modular Programming (using import)

• Object-Oriented Concepts: Classes, Objects, Inheritance Lab:

• Build a reusable function to compute EMI

• Create a class Customer with attributes and methods Scenarios:

• Reuse a class to represent various loan types

• Modularize a financial calculator Tasks:

• Design a class for Bank Account with deposit, withdraw, balance_check methods

• Build and test a Python module for tax computation Challenges:

• Manage inheritance for different account types (savings, current)

• Handle class-based exceptions for transaction errors

Topic 1.3: Decorators, Multithreading, Async Programmin

Theory:

• Decorators and their use in logging/authorization

• Multithreading vs. Multiprocessing

• Asynchronous functions using async/await

• Performance and concurrency Lab:

• Decorator to log transaction details

• Use threading to simulate parallel account updates

• Async call to simulate multiple loan API responses Scenarios:

• Run multiple credit score checks in parallel

• Delay fraud detection alerts using async functions Tasks:

• Create a decorator for transaction authorization

• Use threads to simulate concurrent fund transfers

• Implement async call for fetching stock prices Challenges:

• Race conditions in shared data updates

• Debugging asynchronous errors in real-time pipelines

Topic 1.4: Type Annotations and MyPy

Theory:

• Static typing in Python

• Benefits of MyPy for large codebases

• Lab:

• Add type hints to transaction processing functions

• Run mypy on a banking script and fix issues Scenarios:

• Validate developer-written ETL scripts before deployment Tasks:

• Refactor old code to include type annotations

• Use MyPy in CI/CD for ETL validation Challenges:

• Compatibility issues in legacy codebases

• Managing strict vs. flexible type rules

Topic 1.5: Web Scraping with Beautiful Soup Theory:

• HTML structure, Tags, Classes

• Extracting elements with Beautiful Soup

• Handling dynamic content Lab:

• Scrape exchange rate data from financial websites

• Extract loan interest rates from competitor sites Scenarios:

• Automate scraping of gold/silver rate for bank products

• Monitor changes in government bank policies Tasks:

• Build a scraper for live bank news

• Store scraped data into MongoDB Challenges:

• Handle JavaScript-loaded pages (use Selenium if needed)

• Avoid scraping bans by using proxies and headers.