𝗧𝗼𝗽 𝟴 𝗣𝘆𝘁𝗵𝗼𝗻 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲

1. NumPy
→ Fundamental library for numerical computing.
→ Used for array operations, linear algebra, and random number generation.

2. Pandas
→ Best for data manipulation and analysis.
→ Offers DataFrame and Series structures for handling tabular data.

3. Matplotlib
→ Creates static, animated, and interactive visualizations.
→ Ideal for line charts, scatter plots, and bar graphs.

4. Seaborn
→ Built on Matplotlib for statistical data visualization.
→ Supports heatmaps, violin plots, and pair plots for deeper insights.

5. Scikit-Learn
→ Essential for machine learning tasks.
→ Provides tools for regression, classification, clustering, and preprocessing.

6. TensorFlow
→ Used for deep learning and neural networks.
→ Supports distributed computing for large-scale models.

7. SciPy
→ Extends NumPy with advanced scientific computations.
→ Useful for optimization, signal processing, and integration.

8. Statsmodels
→ Designed for statistical modeling and hypothesis testing.
→ Great for linear models, time series analysis, and statistical tests.

𝗧𝗶𝗽: Start with NumPy and Pandas to build your foundation, then explore others as per your data science needs!

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