Essential Python Libraries for Data Analytics

1. NumPy:

   - Efficient numerical operations and array manipulation.

2. Pandas:

   - Data manipulation and analysis with powerful data structures (DataFrame, Series).

3. Matplotlib:

   - 2D plotting library for creating visualizations.

4. Scikit-learn:

   - Machine learning toolkit for classification, regression, clustering, etc.

5. TensorFlow:

   - Open-source machine learning framework for building and deploying ML models.

6. PyTorch:

   - Deep learning library, particularly popular for neural network research.

7. Django:

   - High-level web framework for building robust, scalable web applications.

8. Flask:

   - Lightweight web framework for building smaller web applications and APIs.

9. Requests:

   - HTTP library for making HTTP requests.

10. Beautiful Soup:

    - Web scraping library for pulling data out of HTML and XML files.

As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc.