📊 data-science-python-practice

Practice exercises and examples in Python to build strong foundations in data science: NumPy, Pandas, Matplotlib, machine learning, and more.

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🚀 Getting Started

Follow these steps to set up your environment and run the exercises.

1. Clone the Repository

git clone https://github.com/DavFilsDev/data-science-python-practice.git
cd data-science-python-practice

2. Create a Virtual Environment

On Linux / MacOS:

python3 -m venv venv
source venv/bin/activate

On Windows:

python -m venv venv
venv\Scripts\activate

3. Install Required Libraries

Install all dependencies from the requirements.txt file:

pip install -r requirements.txt

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4. Run an Example File

Example with the first NumPy exercise:

python numpy/numpy_intro.py

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📂 Folder Structure

data-science-python-practice/
├── numpy/
├── pandas/
├── matplotlib/
├── requirements.txt          # Project dependencies
└── README.md                 # Project instructions

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🛠️ Topics Covered

• NumPy: Arrays, math operations, random numbers, linear algebra
• Pandas: Data manipulation
• [Coming Soon] Matplotlib & Seaborn: Data visualization
• [Coming Soon] Scikit-learn: Machine learning basics
• [Coming Soon] Mini-projects on real datasets

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🤝 Contributing

This repository is for personal learning, but feel free to fork it and practice as well.