Python Data Science Tutorial

Welcome to the world of Python Data Science! 🚀 Whether you're a beginner or looking to deepen your skills, this guide will help you explore the essentials of data analysis, visualization, and machine learning using Python.

📚 Core Libraries for Data Science

Python offers a rich ecosystem of libraries for data science tasks. Here are the most popular ones:

  1. NumPy - For numerical computations and array handling.
    NumPy
  2. Pandas - For data manipulation and analysis.
    Pandas
  3. Matplotlib - For creating static, animated, and interactive visualizations.
    Matplotlib
  4. Scikit-learn - For machine learning algorithms and tools.
    Scikit_learn

📊 Practical Example: Data Visualization

Let's create a simple plot using Matplotlib! 

import matplotlib.pyplot as plt  
plt.plot([1, 2, 3], [4, 5, 1])  
plt.title("Sample Plot")  
plt.xlabel("X-axis")  
plt.ylabel("Y-axis")  
plt.show()
Data_visualization

🧠 Expand Your Knowledge

❓ Frequently Asked Questions

  • What is the difference between NumPy and Pandas?
  • How to install Python data science libraries?
  • Are there any beginner-friendly projects?
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