DATA-SCIENCE-

Empowering Insights, Accelerating Innovation in Data Science

Built with the tools and technologies:

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Table of Contents

• Overview
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Testing

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Overview

Data-Science- is an all-in-one developer tool that consolidates data science resources, datasets, and code examples to facilitate learning and practical application of AI and data mining techniques.
It serves as a central hub for exploring diverse topics such as deep learning, big data, and large language models, empowering developers to build and refine intelligent systems.

Why Data-Science-

This project aims to support developers in mastering data-driven insights through comprehensive resources and hands-on examples.
The core features include :

• 📚 Resource Hub: Organized overview of key data science topics and tools for quick reference.
• 🗂️ Datasets & Code: Extensive collection of datasets and source codes for real-world experimentation.
• ⚙️ Model Implementations: Demonstrations of regression, neural networks, social network analysis, and more.
• 📓 Interactive Notebooks: Jupyter notebooks that guide through practical data mining and analysis techniques.
• 🎯 Educational Focus: Designed to accelerate learning and development in AI and data analysis.

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Getting Started

Prerequisites

This project requires the following dependencies:

• Python: 3.8 or higher
• Jupyter Notebook - Visual Studio Code

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Installation

Build Data-Science- from the source and install dependencies:

1. Clone the repository:

   git clone https://github.com/Aribybar/Data-Science-.git

2. Navigate to the project directory:

   cd Data-Science-

3. Create a virtual environment (recommended):

   python -m venv venv

4. Activate the virtual environment:

   • Windows:

     venv\Scripts\activate

5. Install the dependencies:

   pip install -r requirements.txt