SQL for Data Analysis

A comprehensive, hands-on SQL learning repository designed to build strong foundations in data analysis using SQL. This project features structured notebooks covering core SQL concepts, analytical queries, and real-world data manipulation techniques using Jupyter Notebook + MySQL.

Note: This repository documents my personal learning journey in Data Analytics, featuring practical implementations and exercises I've completed to master SQL for data analysis.

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Repository Objective

This repository serves as a complete learning path to:

• Master SQL from basics to intermediate/advanced level
• Practice real-world analytical queries used across industries
• Understand data-driven decision making through SQL
• Integrate SQL with Python for enhanced visualization and experimentation
• Build a portfolio of SQL skills applicable to data analyst roles

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Technologies Used

Technology	Purpose
SQL	Core query language for data manipulation
MySQL	Relational database management system
Python	Environment support and data processing
Jupyter Notebook	Interactive coding and documentation
IPython SQL Magic	Seamless SQL execution within notebooks

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Repository Structure

SQL-for-Data-Analysis/
│
├── 01_Basics_of_SQL.ipynb                    # Introduction to SQL fundamentals
├── 02_Data_Retrieval.ipynb                   # SELECT, sorting, and limiting
├── 03_Filtering_&_Logical_Conditions.ipynb   # WHERE, AND, OR, LIKE operators
├── 04_Aggregate_Functions_&_GROUP_BY.ipynb   # Statistical aggregations
├── 05_Joins.ipynb                            # Combining multiple tables
├── 06_Subqueries.ipynb                       # Nested and correlated queries
├── 07_Window_Functions.ipynb                 # Advanced analytical functions
├── 08_SQL_CTE.ipynb                          # Common Table Expressions
├── 09_CASE_WHEN.ipynb                        # Conditional logic
├── 10_Date_Time_Analysis.ipynb               # Temporal data handling
├── 11_String_Functions.ipynb                 # Text manipulation
└── README.md                                 # Project documentation

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Topics Covered

Module	Topic	Concepts Covered
1️⃣	SQL Fundamentals	Database & table concepts, Schema design basics, Basic query structure, SELECT statements
2️⃣	Data Retrieval	Column selection, ORDER BY, LIMIT / TOP, Column aliasing (AS)
3️⃣	Filtering & Logical Conditions	WHERE clause, AND / OR / NOT, BETWEEN, LIKE & wildcards, IN operator
4️⃣	Aggregate Functions & GROUP BY	COUNT(), SUM(), AVG(), MIN(), MAX(), GROUP BY, HAVING vs WHERE, Multi-level grouping
5️⃣	Joins	INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, CROSS JOIN, Self joins, Multi-table joins
6️⃣	Subqueries	Nested queries, Correlated subqueries, Subqueries in WHERE / FROM / SELECT, EXISTS & IN
7️⃣	Window Functions	ROW_NUMBER(), RANK(), DENSE_RANK(), PARTITION BY, Running totals, LEAD() & LAG()
8️⃣	Common Table Expressions (CTE)	WITH clause, Recursive CTEs, Query readability, Multiple CTE usage
9️⃣	CASE WHEN Logic	Conditional expressions, Data categorization, Derived columns
🔟	Date & Time Analysis	Date extraction, Date arithmetic, Formatting, Time filtering, Period analysis
1️⃣1️⃣	String Functions	CONCAT(), SUBSTRING(), UPPER(), LOWER(), TRIM(), Pattern matching, Data cleaning

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Key Learning Outcomes

By working through this repository, you will gain:

✅ Proficiency in writing complex SQL queries
✅ Expertise in data filtering, transformation, and aggregation
✅ Analytical mindset for deriving insights from datasets
✅ Deep understanding of relational database concepts
✅ Problem-solving skills applicable to real-world business scenarios
✅ Portfolio-ready SQL projects for job applications

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Getting Started

Prerequisites

• MySQL Server (8.0 or higher recommended)
• Python 3.7+
• Jupyter Notebook

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Installation Steps

1️⃣ Install MySQL

Download from: https://www.mysql.com
Follow installation instructions for your OS.

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2️⃣ Install Python Dependencies

pip install jupyter notebook ipython-sql mysql-connector-python pandas

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3️⃣ Clone Repository

git clone https://github.com/pranay-surya/SQL.git
cd SQL

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4️⃣ Launch Jupyter Notebook

jupyter notebook

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5️⃣ Configure Database Connection

In each notebook, update the connection string:

%load_ext sql
%sql mysql+mysqlconnector://username:password@localhost/database_name