Preparing for a SQL interview?

Focus on mastering these essential topics:

1. Joins: Get comfortable with inner, left, right, and outer joins.
Knowing when to use what kind of join is important!

2. Window Functions: Understand when to use
ROW_NUMBER, RANK(), DENSE_RANK(), LAG, and LEAD for complex analytical queries.

3. Query Execution Order: Know the sequence from FROM to
ORDER BY. This is crucial for writing efficient, error-free queries.

4. Common Table Expressions (CTEs): Use CTEs to simplify and structure complex queries for better readability.

5. Aggregations & Window Functions: Combine aggregate functions with window functions for in-depth data analysis.

6. Subqueries: Learn how to use subqueries effectively within main SQL statements for complex data manipulations.

7. Handling NULLs: Be adept at managing NULL values to ensure accurate data processing and avoid potential pitfalls.

8. Indexing: Understand how proper indexing can significantly boost query performance.

9. GROUP BY & HAVING: Master grouping data and filtering groups with HAVING to refine your query results.

10. String Manipulation Functions: Get familiar with string functions like CONCAT, SUBSTRING, and REPLACE to handle text data efficiently.

11. Set Operations: Know how to use UNION, INTERSECT, and EXCEPT to combine or compare result sets.

12. Optimizing Queries: Learn techniques to optimize your queries for performance, especially with large datasets.

If we master/ Practice in these topics we can track any SQL interviews..

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Getting started with SQL comparison operators.

If you're new to SQL, understanding comparison operators is one of the first things you'll need to learn.

They’re really important for filtering and analyzing your data. Let’s break them down with some simple examples.

Comparison operators let you compare values in SQL queries. Here are the basics:
1. = (Equal To): Checks if two values are the same.
Example: SELECT * FROM Employees WHERE Age = 30; (This will find all employees who are exactly 30 years old).

2. <> or != (Not Equal To): Checks if two values are different.
Example: SELECT * FROM Employees WHERE Age <> 30; (This will find all employees who are not 30 years old).

3. > (Greater Than): Checks if a value is larger.
Example: SELECT * FROM Employees WHERE Salary > 50000; (This will list all employees earning more than 50,000).

4. < (Less Than): Checks if a value is smaller.
Example: SELECT * FROM Employees WHERE Salary < 50000; (This will show all employees earning less than 50,000).

5. >= (Greater Than or Equal To): Checks if a value is larger or equal.
Example: SELECT * FROM Employees WHERE Age >= 25; (This will find all employees who are 25 years old or older).

6. <= (Less Than or Equal To): Checks if a value is smaller or equal.
Example: SELECT * FROM Employees WHERE Age <= 30; (This will find all employees who are 30 years old or younger).

These simple operators can help you get more accurate results in your SQL queries.

Keep practicing and you’ll be great at SQL in no time.

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SQL data cleaning methods you should know for Data Science:

1. Identifying Missing Data
2. Removing Duplicate Records
3. Handling Missing Data
4. Standardizing Data
5. Correcting Data Entry Errors

SQL Roadmap 👇👇
https://www.linkedin.com/posts/sql-analysts_sql-activity-7235150234706690048-ydnI

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To be honest, I also wasn't aware of this fullform until today 😂

5 Key SQL Aggregate Functions for data analyst

🍞SUM(): Adds up all the values in a numeric column.

🍞AVG(): Calculates the average of a numeric column.

🍞COUNT(): Counts the total number of rows or non-NULL values in a column.

🍞MAX(): Returns the highest value in a column.

🍞MIN(): Returns the lowest value in a column.

Examples ⬇️⬇️⬇️