Data Science Libraries in Python

This repository is written in English to reach a wider audience.

Welcome to the Data Science Libraries in Python repository! This project is designed as a didactic resource to explore and understand essential Python libraries to help with your Data Science studies. Here, you'll find useful informations that'll help you master these tools.

Objectives

• Learn the purpose of each library: Understand what each library is used for and its importance in the data science workflow.
• Practice with code examples: Explore clear, didactic examples using fictitious datasets.
• Access curated resources: Find links to documentation, books, and courses for deeper learning.

---

Repository Structure

The repository is organized into folders by theme to make navigation intuitive:

├── README.md
├── Data Science Workflow/
    ├── Data Manipulation/
    │   ├── numpy_basics.ipynb
    │   ├── pandas_data_cleaning.ipynb
    ├── Data Visualization/
    │   ├── matplotlib_basics.ipynb
    │   ├── seaborn_heatmaps.ipynb
    │   ├── plotly_interactive_charts.ipynb
    ├── Machine Learning/
        ├── sklearn_regression.ipynb
        ├── sklearn_classification.ipynb
        ├── xgboost_basics.ipynb

Each folder contains Jupyter notebooks that:

• Introduce the library: Highlight its main features and applications.
• Provide code examples: Demonstrate common tasks and workflows.
• Include comments: Explain each step of the code for better understanding.

---

Themes Covered

1. Data Manipulation

   • numpy: Numerical computing with multi-dimensional arrays.
   • pandas: Data manipulation and analysis with DataFrames.

2. Data Visualization

   • matplotlib: Creating static, animated, and interactive plots.
   • seaborn: Statistical data visualization built on Matplotlib.
   • plotly: Interactive visualizations and dashboards.

3. Machine Learning

   • scikit-learn: Essential tools for machine learning (classification, regression, clustering, etc.).
   • xgboost: Gradient boosting for structured data.

---

Resources

• Official Documentation:

  • NumPy Documentation
  • Pandas Documentation
  • Matplotlib Documentation
  • Seaborn Documentation
  • Plotly Documentation
  • Scikit-learn Documentation
  • XGBoost Documentation

• Books:

  • "Python for Data Analysis" by Wes McKinney
  • "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron

• Courses:

  • Data Science with Python on Coursera
  • Interactive Python for Data Science on Kaggle

---

Roadmap

This is a dynamic project with ongoing updates. Here's the plan:

1. Initial Setup

   • ✅ Create folders and templates for each theme.
   • 🔄 Add basic examples for NumPy and Pandas. (In Progress ⬅️)

2. Expand Visualization Examples

   • Add advanced plots in Seaborn.
   • Create interactive dashboards with Plotly.

3. Machine Learning Use Cases

   • Include real-world scenarios for regression and classification.
   • Add examples with XGBoost.

4. Polishing and Documentation

   • Refine code comments.
   • Add Markdown explanations for workflows.

---

Happy learning! 🚀