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SQL Practice Repository for Data Analysis

Welcome to the SQL Practice Repository for Data Analysis! This repository is designed to help you sharpen your SQL skills specifically for data analysis tasks. Below, you'll find a breakdown of the branches available in this repository, each focusing on a different aspect of SQL commonly used in data analysis workflows.

Branches:

1. master

The master branch serves as the main branch of this repository. It contains general information, guidelines, and this README file.

2. common_table_expressions

The common_table_expressions branch focuses on practicing Common Table Expressions (CTEs) in SQL. CTEs are useful for creating temporary result sets that can be referenced within a query, making complex queries more readable and manageable.

3. window_functions

The window_functions branch is dedicated to practicing Window Functions in SQL. Window functions allow you to perform calculations across a set of rows related to the current row, enabling advanced analytical operations such as ranking, cumulative sums, and moving averages.

4. aggregate_functions

The aggregate_functions branch covers practice exercises involving Aggregate Functions in SQL. Aggregate functions are essential for summarizing data and deriving insights, including calculations like sum, average, minimum, maximum, and count.

5. subqueries

The subqueries branch focuses on practicing Subqueries in SQL. Subqueries are queries nested within another query and are useful for performing operations based on the result of another query, enabling more complex data manipulations and analysis.

6. joins

The joins branch is dedicated to practicing different types of Joins in SQL, including Inner Joins, Outer Joins, and Cross Joins. Joins are fundamental for combining data from multiple tables, allowing you to correlate information and perform analysis across related datasets.

7. advanced_sql_techniques

The advanced_sql_techniques branch covers advanced SQL techniques beyond the basics, such as recursive queries, pivot tables, and advanced filtering techniques. These techniques are valuable for tackling complex data analysis tasks and optimizing query performance.

Getting Started:

To get started with SQL practice for data analysis, simply clone or fork this repository and checkout the branch corresponding to the SQL concept you want to practice. Each branch contains practice exercises and sample datasets to help you apply and reinforce your SQL skills.

Feel free to explore, experiment, and expand upon the exercises provided. Happy coding and data analyzing!

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