Learn SQL with ME Part - 1

1. What is SQL?

• SQL (Structured Query Language) is a standard language used to manage and manipulate databases.
• It enables users to query, insert, update, delete, and retrieve data from relational database systems like MySQL, PostgreSQL, and SQL Server.

2. Why is SQL Required for Data Science?

• Data Access: Most real-world data is stored in databases; SQL is essential for extracting it efficiently.
• Data Manipulation: Allows filtering, aggregating, and transforming data for analysis.
• Data Integration: Enables merging data from multiple sources into a unified format.

3. How Important is SQL in Data Science?

• Essential Skill: SQL is a foundational skill for data scientists, often tested in interviews.
• Frequent Use: Approximately 60-80% of a data scientist's time is spent preparing and querying data.
• Industry Demand: SQL is a prerequisite in almost every data science job description.

4. Real-World Use Case

• Customer Behavior Analysis: A retail company stores customer purchase data in a database. SQL queries are used to extract insights like most purchased products, peak shopping hours, and customer segmentation based on purchase history.
• This helps businesses optimize inventory and improve marketing strategies.

Real-World Case: E-Commerce Sales Analysis

Scenario:

An e-commerce company wants to analyze its sales data to identify top-performing products, understand customer preferences, and optimize inventory.

Practical Example:

1. Database Structure:

- customers table: Contains customer details (`customer_id`, name, location).

- products table: Contains product details (`product_id`, product_name, category, price).

- sales table: Stores sales transactions (`sale_id`, customer_id, product_id, quantity, sale_date).

2. SQL Queries:

- Find Top-Selling Products:

SELECT

p.product_name,

SUM(s.quantity) AS total_quantity

FROM

sales s

JOIN

products p

ON

s.product_id = p.product_id

GROUP BY

p.product_name

ORDER BY

total_quantity DESC

LIMIT 5;

```

Output: Top 5 products based on total quantity sold.

- Analyze Monthly Sales Trends:

SELECT

DATE_FORMAT(sale_date, '%Y-%m') AS month,

SUM(quantity * p.price) AS total_revenue

FROM

sales s

JOIN

products p

ON

s.product_id = p.product_id

GROUP BY

month

ORDER BY

month ASC;

```

Output: Monthly revenue trends for the company.

- Identify Loyal Customers:

SELECT

c.name,

COUNT(s.sale_id) AS purchase_count

FROM

sales s

JOIN

customers c

ON

s.customer_id = c.customer_id

GROUP BY

c.name

ORDER BY

purchase_count DESC

LIMIT 5;

```

Output: Top 5 customers with the most purchases.

3. Insights:

- Helps the business stock popular products.

- Assists in planning marketing campaigns during high-revenue months.

- Enables rewarding loyal customers with personalized offers.