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SQL Essential Concepts for Data Analyst Interviews ✅

1. SQL Syntax: Understand the basic structure of SQL queries, which typically include SELECT, FROM, WHERE, GROUP BY, HAVING, and ORDER BY clauses. Know how to write queries to retrieve data from databases.

2. SELECT Statement: Learn how to use the SELECT statement to fetch data from one or more tables. Understand how to specify columns, use aliases, and perform simple arithmetic operations within a query.

3. WHERE Clause: Use the WHERE clause to filter records based on specific conditions. Familiarize yourself with logical operators like =, >, <, >=, <=, <>, AND, OR, and NOT.

4. JOIN Operations: Master the different types of joins—INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN—to combine rows from two or more tables based on related columns.

5. GROUP BY and HAVING Clauses: Use the GROUP BY clause to group rows that have the same values in specified columns and aggregate data with functions like COUNT(), SUM(), AVG(), MAX(), and MIN(). The HAVING clause filters groups based on aggregate conditions.

6. ORDER BY Clause: Sort the result set of a query by one or more columns using the ORDER BY clause. Understand how to sort data in ascending (ASC) or descending (DESC) order.

7. Aggregate Functions: Be familiar with aggregate functions like COUNT(), SUM(), AVG(), MIN(), and MAX() to perform calculations on sets of rows, returning a single value.

8. DISTINCT Keyword: Use the DISTINCT keyword to remove duplicate records from the result set, ensuring that only unique records are returned.

9. LIMIT/OFFSET Clauses: Understand how to limit the number of rows returned by a query using LIMIT (or TOP in some SQL dialects) and how to paginate results with OFFSET.

10. Subqueries: Learn how to write subqueries, or nested queries, which are queries within another SQL query. Subqueries can be used in SELECT, WHERE, FROM, and HAVING clauses to provide more specific filtering or selection.

11. UNION and UNION ALL: Know the difference between UNION and UNION ALL. UNION combines the results of two queries and removes duplicates, while UNION ALL combines all results including duplicates.

12. IN, BETWEEN, and LIKE Operators: Use the IN operator to match any value in a list, the BETWEEN operator to filter within a range, and the LIKE operator for pattern matching with wildcards (%, _).

13. NULL Handling: Understand how to work with NULL values in SQL, including using IS NULL, IS NOT NULL, and handling nulls in calculations and joins.

14. CASE Statements: Use the CASE statement to implement conditional logic within SQL queries, allowing you to create new fields or modify existing ones based on specific conditions.

15. Indexes: Know the basics of indexing, including how indexes can improve query performance by speeding up the retrieval of rows. Understand when to create an index and the trade-offs in terms of storage and write performance.

16. Data Types: Be familiar with common SQL data types, such as VARCHAR, CHAR, INT, FLOAT, DATE, and BOOLEAN, and understand how to choose the appropriate data type for a column.

17. String Functions: Learn key string functions like CONCAT(), SUBSTRING(), REPLACE(), LENGTH(), TRIM(), and UPPER()/LOWER() to manipulate text data within queries.

18. Date and Time Functions: Master date and time functions such as NOW(), CURDATE(), DATEDIFF(), DATEADD(), and EXTRACT() to handle and manipulate date and time data effectively.

19. INSERT, UPDATE, DELETE Statements: Understand how to use INSERT to add new records, UPDATE to modify existing records, and DELETE to remove records from a table. Be aware of the implications of these operations, particularly in maintaining data integrity.

20. Constraints: Know the role of constraints like PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, and CHECK in maintaining data integrity and ensuring valid data entry in your database.

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Essential Python Libraries for Data Analytics 😄👇

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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. in your data projects.

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Must-know Pandas Functions for Data Analysis

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