Python for Data Science

A structured, notebook-first repository for learning Python and applying it to data science through hands-on practice, projects, and real datasets.

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Table of Contents

• Overview
• What You Will Learn
• Repository Structure
• Learning Sequence
• Datasets
• Quick Start
• Tools and Technologies
• Contributing
• License

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Overview

This repository is designed as a complete Python-for-data-science learning track. It begins with core Python concepts and gradually progresses to data analysis and visualization using NumPy, Pandas, Matplotlib, and Seaborn.

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What You Will Learn

• Python fundamentals and syntax
• Intermediate and advanced Python concepts
• Object-oriented programming (OOP)
• Exception and file handling
• NumPy fundamentals and advanced operations
• Data analysis with Pandas
• Data visualization with Matplotlib and Seaborn
• Practice exercises and mini projects

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Repository Structure

Folder	Focus Area
Fundamentals of Python/	Core Python basics, control flow, data structures, functions
Advance Python/	Decorators, scope, and advanced language concepts
Exception Handling in Python /	Error handling patterns and custom exceptions
File Handling in Python/	File I/O, binary files, and serialization
OOPS in Python/	Classes, inheritance, polymorphism, abstraction, encapsulation
Python fundamental Questions/	Practice questions for core and intermediate topics
My projects/	Mini projects for applied learning
NumPy For Python /	NumPy fundamentals and array operations
NumPy For Practice/	NumPy practice notebooks
NumPy Advance/	Broadcasting, advanced indexing, utilities
Matplotlib in Python/	Plotting and charting exercises
Seaborn in Python/	Statistical and relational visualizations
Pandas in Python/	Series, DataFrames, DateTime operations, and practice
Database from Kaggle/	Dataset collection used across notebooks
Exploratory Data Analysis (EDA)/	EDA-focused notebook work

Note: Some folder names include spaces, and a few intentionally include trailing spaces (for example, Exception Handling in Python / and NumPy For Python /) to preserve the repository's original notebook and dataset paths. Copy paths carefully when using terminal commands.

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Learning Sequence

Recommended study order:

1. Fundamentals of Python
2. Advance Python
3. Exception Handling in Python
4. File Handling in Python
5. OOPS in Python
6. Python fundamental Questions
7. My projects
8. NumPy For Python
9. NumPy For Practice
10. NumPy Advance
11. Matplotlib in Python
12. Seaborn in Python
13. Pandas in Python
14. Exploratory Data Analysis (EDA)

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Datasets

The Database from Kaggle/ directory contains datasets used throughout NumPy, Pandas, and visualization notebooks, including:

• IPL match and delivery datasets
• Movie and IMDb datasets
• Student performance and expense datasets
• Additional CSV/Excel files in Database from Kaggle/datasets / (note the intentional trailing space after datasets).

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Quick Start

git clone https://github.com/aayushmanz/Python-For-Data-Science.git
cd Python-For-Data-Science
python -m venv .venv
source .venv/bin/activate
pip install jupyter numpy pandas matplotlib seaborn
jupyter notebook

For Windows PowerShell:

.venv\Scripts\Activate.ps1

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Tools and Technologies

• Python 3
• Jupyter Notebook
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Git and GitHub

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Contributing

Contributions are welcome.

1. Fork the repository
2. Create a feature branch
3. Make and commit your changes
4. Open a pull request with a clear summary

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License

This project is available under the MIT License.

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Maintained by Ayush Suthar.