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  • Week 1-2 : First two days of your Python learning journey:

    Day 1: Introduction to Python

    • Start by understanding what Python is:
    • Python is a high-level, interpreted programming language known for its readability and versatility.
    • It is widely used in web development, data science, machine learning, scientific computing, and more.
    • Explore Python’s characteristics:
    • Readability and clean syntax make it easy for beginners.
    • It is dynamically typed, meaning you don’t need to declare variable types explicitly.
    • Python has a large standard library with pre-built modules for various tasks.
    • Learn about Python 2 vs. Python 3:
    • Python 2 is legacy and no longer supported; focus on Python 3, which is the current version (e.g., Python 3.7, 3.8, 3.9, etc.).
    • Install Python:
    • Visit the official Python website (https://www.python.org/) to download the latest Python version for your operating system (Windows, macOS, Linux).
    • Installation steps for Windows:
    • Run the installer.
    • Check the box that says “Add Python X.X to PATH” during installation.
    • Click “Install Now.”
    • Installation steps for macOS:
    • Run the installer package.
    • Follow the installation instructions.
    • Installation steps for Linux:
    • Open a terminal.
    • Use your package manager to install Python (e.g., sudo apt-get install python3 for Debian/Ubuntu).
    • Verify the installation by opening a terminal and typing python3. You should see the Python interpreter prompt.

    Day 2: Installing an IDE (e.g., Anaconda, Jupyter Notebook, or Visual Studio Code)

    • Understand what an Integrated Development Environment (IDE) is:
    • An IDE is a software application that provides a comprehensive environment for writing, debugging, and running code.
    • Explore different Python IDE options:
    • Anaconda: A Python distribution that includes Python, Jupyter Notebook, and popular data science libraries. Great for data science and scientific computing.
    • Jupyter Notebook: An interactive web-based environment for data analysis and visualization. Often used for data science and educational purposes.
    • Visual Studio Code (VS Code): A highly customizable, free, and open-source code editor with Python support. Suitable for a wide range of Python projects.
    • Installation steps for Anaconda (if you choose this option):
    • Download the Anaconda installer for your OS from the Anaconda website (https://www.anaconda.com/products/individual).
    • Run the installer and follow the installation instructions.
    • Anaconda includes Python, Jupyter Notebook, and various data science libraries.
    • Installation steps for Jupyter Notebook (if you choose this option):
    • If you’ve installed Anaconda, Jupyter Notebook is already available.
    • Otherwise, you can install it using pip: pip install notebook.
    • Installation steps for Visual Studio Code (if you choose this option):
    • Download the Visual Studio Code installer for your OS from the VS Code website (https://code.visualstudio.com/).
    • Run the installer and follow the installation instructions.
    • Install the “Python” extension within VS Code for enhanced Python support.
    • Launch your chosen IDE and create your first Python script:
    • In Anaconda or Jupyter Notebook, open a new Jupyter Notebook or create a Python script.
    • In Visual Studio Code, open a new Python file (.py) and start writing code.

    By the end of Day 2, you should have Python installed and be ready to start coding in your chosen IDE.

  • Comprehensive Curriculum to help you learn Python

    Week 1-2: Introduction to Python Basics

    • Day 1-2: Introduction to Python, installing Python and an IDE (e.g., Anaconda, Jupyter Notebook, or Visual Studio Code)
    • Day 3-4: Variables, data types (integers, floats, strings), and basic operations
    • Day 5-7: Control flow (if statements, loops) and basic input/output

    Week 3-4: Data Structures

    • Day 1-2: Lists and list manipulation
    • Day 3-4: Tuples, sets, and dictionaries
    • Day 5-7: List comprehensions and basic file handling

    Week 5-6: Functions and Modules

    • Day 1-2: Functions (defining, calling, parameters, and return values)
    • Day 3-4: Scope and lifetime of variables, global and local scope
    • Day 5-7: Modules and importing in Python, creating your modules

    Week 7-8: Object-Oriented Programming (OOP)

    • Day 1-2: Introduction to OOP concepts (classes, objects, methods)
    • Day 3-4: Creating and using classes in Python
    • Day 5-7: Inheritance, polymorphism, and encapsulation

    Week 9-10: Exception Handling and Error Checking

    • Day 1-2: Introduction to exceptions and error handling
    • Day 3-4: Handling exceptions with try-except blocks
    • Day 5-7: Custom exceptions and raising exceptions

    Week 11-12: File I/O and Data Manipulation

    • Day 1-2: Reading and writing text files
    • Day 3-4: Working with CSV and JSON data
    • Day 5-7: Introduction to data serialization (pickle, JSON)

    Week 13-14: Libraries and External Modules

    • Day 1-2: Introduction to popular libraries (e.g., NumPy for numerical computing)
    • Day 3-4: Working with data in external formats (e.g., Excel, databases)
    • Day 5-7: Introduction to data visualization (e.g., Matplotlib, Seaborn)

    Week 15-16: Web Development (Optional)

    • Day 1-2: Introduction to web development with Python (e.g., Flask or Django)
    • Day 3-4: Creating a basic web application
    • Day 5-7: Handling forms, database integration, and REST APIs

    Week 17-18: Advanced Topics

    • Day 1-2: Multithreading and multiprocessing
    • Day 3-4: Regular expressions for text processing
    • Day 5-7: Testing and debugging techniques

    Week 19-20: Final Projects and Review

    • Day 1-14: Work on a small Python project that interests you.
    • Day 15-20: Review and refine your Python skills, explore advanced topics further.

    Week 21-22: Deployment and Version Control (Optional)

    • Day 1-2: Version control with Git and GitHub
    • Day 3-4: Preparing your Python project for deployment
    • Day 5-7: Deploying your project to a cloud platform (e.g., Heroku, AWS)

    Week 23-24: Data Science and Machine Learning (Optional)

    • Day 1-2: Introduction to data analysis with Pandas
    • Day 3-4: Introduction to machine learning with scikit-learn
    • Day 5-7: Building a simple machine learning model

    Week 25-26: Final Projects and Portfolio

    • Day 1-14: Work on a more complex Python project to showcase your skills.
    • Day 15-20: Create a portfolio showcasing your Python projects and what you’ve learned.