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Essential Python Libraries to build your career in Data Science 📊👇

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. Seaborn:
- Statistical data visualization built on top of Matplotlib.

5. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.

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

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

8. SciPy:
- Library for scientific and technical computing.

9. Statsmodels:
- Statistical modeling and econometrics in Python.

10. NLTK (Natural Language Toolkit):
- Tools for working with human language data (text).

11. Gensim:
- Topic modeling and document similarity analysis.

12. Keras:
- High-level neural networks API, running on top of TensorFlow.

13. Plotly:
- Interactive graphing library for making interactive plots.

14. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.

15. OpenCV:
- Library for computer vision tasks.

As a beginner, you can start with Pandas and NumPy for data manipulation and analysis. For data visualization, Matplotlib and Seaborn are great starting points. As you progress, you can explore machine learning with Scikit-learn, TensorFlow, and PyTorch.

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SQL From Basic to Advanced level

Basic SQL is ONLY 7 commands:
- SELECT
- FROM
- WHERE (also use SQL comparison operators such as =, <=, >=, <> etc.)
- ORDER BY
- Aggregate functions such as SUM, AVERAGE, COUNT etc.
- GROUP BY
- CREATE, INSERT, DELETE, etc.
You can do all this in just one morning.

Once you know these, take the next step and learn commands like:
- LEFT JOIN
- INNER JOIN
- LIKE
- IN
- CASE WHEN
- HAVING (undertstand how it's different from GROUP BY)
- UNION ALL
This should take another day.

Once both basic and intermediate are done, start learning more advanced SQL concepts such as:
- Subqueries (when to use subqueries vs CTE?)
- CTEs (WITH AS)
- Stored Procedures
- Triggers
- Window functions (LEAD, LAG, PARTITION BY, RANK, DENSE RANK)
These can be done in a couple of days.
Learning these concepts is NOT hard at all

- what takes time is practice and knowing what command to use when. How do you master that?
- First, create a basic SQL project
- Then, work on an intermediate SQL project (search online) -

Lastly, create something advanced on SQL with many CTEs, subqueries, stored procedures and triggers etc.

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