🧠 Python for Data Science – Colab Notebooks Repository

This repository is a curated collection of Google Colab Notebooks and resources created by Lovnish Verma to learn and teach Python programming and Data Science concepts interactively. It covers foundational Python, Object-Oriented Programming, libraries like NumPy, Pandas, Matplotlib, Seaborn, Exception Handling, and real-world machine learning problems like the Titanic dataset.

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🔍 Overview

• 🔰 Python basics to advanced topics
• 📊 Data visualization with Matplotlib and Seaborn
• 🧮 Scientific computing with NumPy
• 🐼 Data manipulation using Pandas
• 🧠 Machine learning using Scikit-Learn
• 🚢 Real-life datasets (Titanic, Iris)
• ✅ Concepts with clear examples and explanations
• 📘 Includes handwritten notes and markdown guides

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📂 Repository Structure

Notebook/File	Description
🐍_Python_Getting_Started.ipynb	Getting started with Python: syntax, data types, control structures
python_basics.ipynb	Covers recursion, factorial, Fibonacci, and file handling
NumPY.ipynb	Introduction to NumPy arrays, indexing, and vectorized operations
🐼_Python_Pandas.ipynb	Data manipulation using Pandas: Series, DataFrames, missing values
Matplotlib_Visualization_with_Python.ipynb	Core Matplotlib visualizations and plot customizations
Matplotlib_Seaborn.ipynb	Seaborn for advanced statistical plots and data styling
Pandas.ipynb	Additional Pandas operations and advanced data analysis
Modules_and_Libraries_in_Python.ipynb	Importing and using Python standard and external libraries
Exception_Handling_in_Python.ipynb	Try-except blocks, raising exceptions, and custom error handling
Object_Oriented_Programming_(OOP).ipynb	Concepts like classes, objects, inheritance, and polymorphism
Oop_Python_Notebook.ipynb	Practice notebook for OOP concepts
Scikit_Learn_Machine_Learning_in_Python_.ipynb	Introduction to Scikit-Learn for machine learning tasks
TITANIC.ipynb	Machine learning project on Titanic survival prediction
iris(step_bystep).ipynb	Step-by-step ML classification on the Iris dataset
BDDS_17march_01.ipynb	Lecture notebook on data science topics covered in class
guide on Data Collection and Data Preprocessing.md	Guide on collecting, cleaning, and preparing data for ML
python programming handwritten notes.pdf	A PDF of handwritten notes for reference
hello.py	A basic Python script as a starter template
readme.md	You’re reading it 📘

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🎓 Learning Roadmap

1. Start with Python Basics → 🐍_Python_Getting_Started.ipynb and python_basics.ipynb

2. Explore OOP Concepts → Object_Oriented_Programming_(OOP).ipynb and Oop_Python_Notebook.ipynb

3. Work with NumPy & Pandas → NumPY.ipynb, 🐼_Python_Pandas.ipynb, and Pandas.ipynb

4. Master Data Visualization → Matplotlib_Visualization_with_Python.ipynb and Matplotlib_Seaborn.ipynb

5. Understand Modules & Errors → Modules_and_Libraries_in_Python.ipynb, Exception_Handling_in_Python.ipynb

6. Dive into ML with Scikit-Learn → Scikit_Learn_Machine_Learning_in_Python_.ipynb, TITANIC.ipynb, and iris(step_bystep).ipynb

7. Read the Guide & Notes → guide on Data Collection and Data Preprocessing.md → python programming handwritten notes.pdf

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👨‍🏫 About the Author

Lovnish Verma A passionate developer and educator in the field of Python, Data Science, Machine Learning, and Backend Development. I use these notebooks for teaching sessions, workshops, and personal experiments.

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🤝 Contributing

Have suggestions or want to contribute? Feel free to fork this repository and submit a pull request with improvements, fixes, or new notebooks!

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📜 License

This repository is licensed under the MIT License. Feel free to use, modify, and distribute with attribution.

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📫 Contact

For queries, collaborations, or feedback: 📧 princelv84@gmail.com

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Made with lots of ❤️...