SQL vs Python

SQL is great for managing and querying structured databases, especially when dealing with large datasets. It excels in tasks like filtering, sorting, and aggregating data.

Python, on the other hand, is a versatile programming language used for a broader range of tasks. In the context of data, Python is powerful for data manipulation, analysis, and machine learning. It offers libraries like Pandas for data manipulation, NumPy for numerical operations, and Scikit-Learn for machine learning.

In summary, SQL is essential for efficient database querying, while Python provides a more comprehensive solution for various data-related tasks, making them often used together in data-related workflows.

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Instead, for eg, let's say you are working on a SQL case study, download a dataset from Kaggle (usually a CSV file), set up a Postgre/MySQL database, connect it with the data, and prompt ChatGPT with questions ranging from basic to advanced SQL.

Solve the questions step by step. When using PowerBI, connect to the database and create a compelling dashboard. Don't just upload the dataset; employ DAX queries, statistical functions, and avoid relying solely on drag-and-drop features. Use Formatting section to do creative stuff and add your unique element in the project.

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Best practices for writing SQL queries:

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1- Write SQL keywords in capital letters.

2- Use table aliases with columns when you are joining multiple tables.

3- Never use select *, always mention list of columns in select clause.

4- Add useful comments wherever you write complex logic. Avoid too many comments.

5- Use joins instead of subqueries when possible for better performance.

6- Create CTEs instead of multiple sub queries , it will make your query easy to read.

7- Join tables using JOIN keywords instead of writing join condition in where clause for better readability.

8- Never use order by in sub queries , It will unnecessary increase runtime.

9- If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance.

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