Top 10 SQL Projects for Data Analysis in 2024 | Enhance Your SQL Skills

Introduction

SQL Projects for Data Analysis are essential for honing skills and gaining practical experience in handling data effectively. In data science, SQL is a powerful tool, crucial for analyzing and manipulating large datasets to derive valuable insights. This article presents the top 10 SQL projects for data analysis in 2023, offering diverse opportunities across various domains to sharpen SQL abilities and tackle real-world challenges.

Table of Contents

1. - Why SQL is Crucial in Data Science?
  - Top 10 SQL Projects

Why SQL is Crucial in Data Science?

SQL plays a crucial role in data science due to its versatility and efficiency in managing and querying data in relational database management systems (RDBMS). Here’s why SQL is indispensable:

1. 1. Data Retrieval: SQL enables efficient retrieval and manipulation of data from relational databases, common sources of structured data.
  2. Data Exploration: SQL facilitates data exploration by allowing users to query and analyze datasets to understand their structure, relationships, and patterns.
  3. Data Cleaning: SQL aids in cleaning and preprocessing data by performing operations like filtering, joining, and aggregating to prepare it for analysis.
  4. Data Integration: SQL enables integration of data from multiple sources by combining tables or databases using join operations.
  5. Statistical Analysis: SQL can be used to perform basic statistical analysis directly within the database, such as calculating averages, counts, and distributions.
  6. Modeling and Machine Learning: SQL helps prepare data for modeling and machine learning tasks by selecting relevant features, creating derived variables, and partitioning data for training and testing.
  7. Big Data: SQL is vital in managing, accessing, integrating, and analyzing big data, making it an essential skill for working with large-scale data environments.

Top 10 SQL Projects

Whether you’re a beginner or an experienced data professional, these SQL projects will enable you to refine your SQL expertise and contribute meaningfully to data analysis. Here are some SQL project ideas with GitHub source code:

1. Sales Analysis: Analyze sales data to identify trends, patterns, and insights that can drive business decisions. GitHub Source
2. Customer Segmentation: Segment customers based on purchasing behavior to tailor marketing strategies. GitHub Sou
3. Fraud Detection: Detect fraudulent transactions using SQL queries to analyze transaction data. GitHub Source
4. Inventory Management: Manage and optimize inventory levels using SQL for efficient supply chain management. GitHub Source
5. Website Analytics: Analyze website traffic data to understand user behavior and improve user experience. GitHub Source
6. Social Media Analysis: Examine social media data to derive insights into user engagement and sentiment. GitHub Source
7. Movie Recommendations: Develop a recommendation system based on user ratings and preferences. GitHub Source
8. Healthcare Analytics: Analyze healthcare data to improve patient outcomes and operational efficiency. GitHub Source
9. Sentiment Analysis: Perform sentiment analysis on text data to understand public opinion. GitHub Source
10. Library Management System: Create a system to manage library resources efficiently. GitHub Source
  Expanding your SQL knowledge through these projects will enhance your data analysis skills, making you a valuable asset in the data science field.

SQL Queries

1. Sales Data Retrieval

Retrieve the sale date, product name, and product price from the "sales" table.

SELECT sale_date, product_name, product_price
FROM sales;

2. Count Unique Products Sold

Calculate the total number of unique products sold.

SELECT COUNT(DISTINCT product_id) AS unique_products_sold
FROM sales;

3. Total Revenue

Calculate the total revenue generated from all sales.

SELECT SUM(product_price) AS total_revenue
FROM sales;

4. List Sales by Date

List all sales, sorted by the sale date in descending order.

SELECT sale_id, sale_date
FROM sales
ORDER BY sale_date DESC;

5. Contact Details of Sales Persons

Retrieve the first name, last name, and email of all sales persons.

SELECT first_name, last_name, email
FROM sales_person;

Intermediate Queries

6. Top Selling Products

Retrieve the top 5 selling products by quantity.

SELECT product_name, SUM(product_quantity) AS total_quantity_sold
FROM sales_detail
GROUP BY product_name
ORDER BY total_quantity_sold DESC
LIMIT 5;

7. Sales by Location

Calculate total sales amount by location.

SELECT sp.location, SUM(s.amount) AS total_sales_amount
FROM sales s
JOIN sales_person sp ON s.sales_person_id = sp.ID
GROUP BY sp.location;

8. Monthly Sales Trend

Analyze monthly sales trend.

SELECT DATE_FORMAT(s.sale_date, '%Y-%m') AS month, SUM(s.amount) AS total_sales_amount
FROM sales s
GROUP BY month;

9. Product with Highest Revenue

Identify the product with the highest revenue.

SELECT product_name, SUM(amount) AS revenue
FROM sales
GROUP BY product_name
ORDER BY revenue DESC
LIMIT 1;

10. Sales Person Performance

Calculate the performance of each sales person.

SELECT sp.ID AS reps_ID, CONCAT(sp.first_name, ' ', sp.last_name) AS name, SUM(s.amount) AS total_sales_amount
FROM sales s
JOIN sales_person sp ON sp.ID = s.sales_person_id
GROUP BY sp.ID, name
ORDER BY total_sales_amount DESC;

11. Daily Sales Trend

Analyze daily sales trend.

SELECT DATE_FORMAT(s.sale_date, '%Y-%m-%d') AS day, SUM(s.amount) AS total_sales_amount
FROM sales s
GROUP BY day;

12. Sales by Customer

Calculate total sales amount by customer.

SELECT c.customer_ID AS ID, CONCAT(c.first_name, ' ', c.last_name) AS name, SUM(s.amount) AS total_sales_amount
FROM sales s
JOIN customers c ON c.customer_id = s.customer_id
GROUP BY ID, name
ORDER BY total_sales_amount DESC;

13. Customer Purchases

Retrieve details of customer purchases.

SELECT c.customer_ID AS ID, CONCAT(c.first_name, ' ', c.last_name) AS name, p.product_name, sd.product_quantity
FROM sales_detail sd
JOIN sales s ON sd.sale_id = s.sale_id
JOIN customers c ON c.customer_id = s.customer_id
JOIN products p ON sd.product_id = p.product_id;

14. Sales Person Sales Count

Calculate the number of sales by each sales person.

SELECT sp.ID AS ID, CONCAT(sp.first_name, ' ', sp.last_name) AS name, COUNT(s.sale_id) AS sales_count
FROM sales s
JOIN sales_person sp ON sp.ID = s.sales_person_id
GROUP BY ID, name;

15. Average Quantity Sold

Calculate the average quantity sold per transaction.

SELECT AVG(product_quantity) AS average_quantity_sold
FROM sales_detail;

Advanced SQL Queries

16. Customer with Highest Spending

Identify the customer who spent the most.

SELECT c.customer_ID AS ID, CONCAT(c.first_name, ' ', c.last_name) AS name, SUM(s.amount) AS total_spending
FROM sales s
JOIN customers c ON c.customer_id = s.customer_id
GROUP BY ID, name
ORDER BY total_spending DESC
LIMIT 1;

17. Repeat Customers

List all customers who made more than one purchase.

SELECT c.customer_ID AS ID, CONCAT(c.first_name, ' ', c.last_name) AS name, COUNT(DISTINCT s.sale_id) AS purchase_count
FROM sales s
JOIN customers c ON c.customer_id = s.customer_id
GROUP BY ID, name
HAVING purchase_count > 1;

18. Sales Person Efficiency

Calculate the average sales amount per transaction for each sales person.

SELECT sp.ID AS ID, CONCAT(sp.first_name, ' ', sp.last_name) AS name, AVG(s.amount) AS average_sales_amount_per_transaction
FROM sales s
JOIN sales_person sp ON sp.ID = s.sales_person_id
GROUP BY ID, name;

19. Seasonal Sales Analysis

Compare sales performance between different seasons of the year.


SELECT
    CASE
        WHEN DATE_FORMAT(s.sales_date, '%m') IN (1, 2, 3) THEN 'Q1'
        WHEN DATE_FORMAT(s.sales_date, '%m') IN (4, 5, 6) THEN 'Q2'
        WHEN DATE_FORMAT(s.sales_date, '%m') IN (7, 8, 9) THEN 'Q3'
        WHEN DATE_FORMAT(s.sales_date, '%m') IN (10, 11, 12) THEN 'Q4'
    END AS quarter,
    SUM(s.amount) AS total_sales_amount
FROM sales s 
GROUP BY quarter
ORDER BY total_sales_amount DESC;

20. Profit Analysis

Calculate the total profit earned from all sales transactions. (Note: Requires cost information for products, which is not available in the provided schema.)

SELECT SUM(amount - cost) AS total_profit
FROM sales s
JOIN products p ON s.product_id = p.product_id;

21. Complex Sales Trend Analysis

Analyze sales trends by combining multiple factors such as product category, location, and sales person.


SELECT
    p.product_category, sp.location, CONCAT(sp.first_name, ' ', sp.last_name) AS sales_person_name,
    SUM(s.amount) AS total_sales_amount
    FROM sales s
    JOIN products p ON s.product_id = p.product_id
    JOIN sales_person sp ON s.sales_person_id = sp.ID
    GROUP BY p.product_category, sp.location, sales_person_name;