Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources

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🧠 SQL Concepts You'll Practice ✔ DDL & DML ✔ INNER JOIN ✔ LEFT JOIN ✔ GROUP BY ✔ HAVING ✔ Aggregate Functions ✔ CASE WHEN ✔ CTEs ✔ Window Functions ✔ Date & Time Functions 📊 Business KPIs You Can Build 📈 Total Orders 📈 Total Revenue 📈 Average Order Value (AOV) 📈 Average Delivery Time 📈 On-Time Delivery Rate 📈 Delayed Delivery Rate 📈 Orders by Hour 📈 Peak Ordering Hour 📈 Orders by Day of Week 📈 Revenue by Restaurant 📈 Revenue by City 📈 Top 10 Restaurants 📈 Top Customers by Spending 📈 Average Restaurant Rating 📈 Delivery Partner Performance 📈 Average Orders per Delivery Partner 📈 Highest Revenue Cuisine 📈 Customer Retention Rate 📈 Repeat Order Rate 📈 Cancellation Rate 📈 Delivery Time by City 📈 Delivery Time by Cuisine 📈 Revenue Trend (Daily & Monthly) 📈 Restaurant Market Share 📈 Customer Lifetime Value (CLV) 🎯 This project simulates the SQL work performed by Data Analysts at companies like Swiggy, Zomato, Uber Eats, and DoorDash, making it an excellent portfolio project for analytics interviews. 💡 Double Tap ❤️ For More

🚀 SQL Project Series #8 Food Delivery Analytics 🍔 Analyze customer orders, restaurant performance, delivery efficiency, and rider operations using SQL. 🎯 Business Objectives ✅ Analyze food orders and revenue ✅ Measure delivery performance ✅ Evaluate restaurant performance ✅ Analyze customer ordering behavior ✅ Track delivery partner efficiency ✅ Identify peak order hours ✅ Reduce delivery delays ✅ Improve customer satisfaction 📂 Step 1: Create Database

CREATE DATABASE food_delivery_db;

USE food_delivery_db;

📂 Step 2: Create Customers Table

CREATE TABLE customers (
    customer_id INT PRIMARY KEY,
    customer_name VARCHAR(100),
    city VARCHAR(50),
    signup_date DATE
);

📂 Step 3: Create Restaurants Table

CREATE TABLE restaurants (
    restaurant_id INT PRIMARY KEY,
    restaurant_name VARCHAR(100),
    cuisine VARCHAR(50),
    city VARCHAR(50),
    rating DECIMAL(3,2)
);

📂 Step 4: Create Delivery Partners Table

CREATE TABLE delivery_partners (
    partner_id INT PRIMARY KEY,
    partner_name VARCHAR(100),
    vehicle_type VARCHAR(30)
);

📂 Step 5: Create Orders Table

CREATE TABLE orders (
    order_id INT PRIMARY KEY,
    customer_id INT,
    restaurant_id INT,
    partner_id INT,
    order_date TIMESTAMP,
    delivery_time_minutes INT,
    order_amount DECIMAL(10,2),
    order_status VARCHAR(20),
    FOREIGN KEY (customer_id) REFERENCES customers(customer_id),
    FOREIGN KEY (restaurant_id) REFERENCES restaurants(restaurant_id),
    FOREIGN KEY (partner_id) REFERENCES delivery_partners(partner_id)
);

📂 Step 6: Insert Sample Customers

INSERT INTO customers VALUES
(1,'Rahul','Mumbai','2025-01-10'),
(2,'Priya','Delhi','2025-01-12'),
(3,'Amit','Pune','2025-01-18'),
(4,'Sneha','Bangalore','2025-01-22'),
(5,'Rohan','Hyderabad','2025-01-25');

📂 Step 7: Insert Sample Restaurants

INSERT INTO restaurants VALUES
(101,'Pizza Hub','Italian','Mumbai',4.6),
(102,'Spice Villa','Indian','Delhi',4.4),
(103,'Burger Point','Fast Food','Pune',4.2),
(104,'Sushi World','Japanese','Bangalore',4.8);

📂 Step 8: Insert Sample Delivery Partners

INSERT INTO delivery_partners VALUES
(201,'Aman','Bike'),
(202,'Rohit','Scooter'),
(203,'Vikas','Bike'),
(204,'Ankit','Bicycle');

📂 Step 9: Insert Sample Orders

INSERT INTO orders VALUES
(1001,1,101,201,'2025-02-01 12:30:00',28,850,'Delivered'),
(1002,2,102,202,'2025-02-01 13:10:00',42,620,'Delivered'),
(1003,3,103,201,'2025-02-01 19:45:00',35,480,'Delivered'),
(1004,4,104,203,'2025-02-02 20:15:00',55,1200,'Delayed'),
(1005,5,101,204,'2025-02-03 18:20:00',25,760,'Delivered');

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🚀 SQL Project Series #7 Hospital Management Analysis 🏥 Learn how hospitals use SQL to analyze patient data, optimize operations, improve resource utilization, and generate healthcare insights. 🎯 Business Objectives ✅ Analyze patient admissions ✅ Monitor doctor performance ✅ Track appointment trends ✅ Measure bed occupancy ✅ Analyze treatment costs ✅ Identify readmitted patients ✅ Calculate average length of stay ✅ Improve hospital efficiency 📂 Step 1: Create Database

CREATE DATABASE hospital_db;

USE hospital_db;

📂 Step 2: Create Patients Table

CREATE TABLE patients (
    patient_id INT PRIMARY KEY,
    patient_name VARCHAR(100),
    gender VARCHAR(10),
    age INT,
    city VARCHAR(50),
    registration_date DATE
);

📂 Step 3: Create Doctors Table

CREATE TABLE doctors (
    doctor_id INT PRIMARY KEY,
    doctor_name VARCHAR(100),
    specialization VARCHAR(100),
    department VARCHAR(100)
);

📂 Step 4: Create Appointments Table

CREATE TABLE appointments (
    appointment_id INT PRIMARY KEY,
    patient_id INT,
    doctor_id INT,
    appointment_date DATE,
    status VARCHAR(20),
    consultation_fee DECIMAL(10,2),
    FOREIGN KEY (patient_id) REFERENCES patients(patient_id),
    FOREIGN KEY (doctor_id) REFERENCES doctors(doctor_id)
);

📂 Step 5: Create Admissions Table

CREATE TABLE admissions (
    admission_id INT PRIMARY KEY,
    patient_id INT,
    admission_date DATE,
    discharge_date DATE,
    diagnosis VARCHAR(100),
    treatment_cost DECIMAL(12,2),
    FOREIGN KEY (patient_id) REFERENCES patients(patient_id)
);

📂 Step 6: Insert Sample Patients

INSERT INTO patients VALUES
(1,'Rahul Sharma','Male',34,'Mumbai','2024-01-05'),
(2,'Priya Verma','Female',29,'Delhi','2024-01-10'),
(3,'Amit Patel','Male',42,'Pune','2024-02-15'),
(4,'Sneha Joshi','Female',37,'Bangalore','2024-03-01'),
(5,'Rohan Gupta','Male',51,'Hyderabad','2024-03-20');

📂 Step 7: Insert Sample Doctors

INSERT INTO doctors VALUES
(101,'Dr. Mehta','Cardiology','Heart Care'),
(102,'Dr. Singh','Orthopedics','Bone Care'),
(103,'Dr. Rao','Neurology','Neuro Care'),
(104,'Dr. Shah','General Medicine','General');

📂 Step 8: Insert Sample Appointments

INSERT INTO appointments VALUES
(1001,1,101,'2025-01-05','Completed',800),
(1002,2,104,'2025-01-06','Completed',500),
(1003,3,102,'2025-01-08','Cancelled',700),
(1004,4,103,'2025-01-09','Completed',1000),
(1005,5,101,'2025-01-12','Completed',800);

📂 Step 9: Insert Sample Admissions

INSERT INTO admissions VALUES
(201,1,'2025-01-05','2025-01-10','Heart Surgery',250000),
(202,2,'2025-01-08','2025-01-11','Fever',12000),
(203,3,'2025-01-15','2025-01-22','Fracture',85000),
(204,5,'2025-01-18','2025-01-21','Cardiac Checkup',45000);

🧠 SQL Concepts You'll Practice ✔ DDL & DML ✔ Joins ✔ Aggregate Functions ✔ GROUP BY ✔ HAVING ✔ CASE WHEN ✔ CTEs ✔ Window Functions ✔ Date Functions ✔ Ranking Functions 📊 Business KPIs You Can Build 📈 Total Patients 📈 Total Admissions 📈 Total Appointments 📈 Appointment Completion Rate 📈 Appointment Cancellation Rate 📈 Doctor-wise Patient Count 📈 Department-wise Revenue 📈 Average Consultation Fee 📈 Average Treatment Cost 📈 Average Length of Stay 📈 Daily Patient Admissions 📈 Monthly Admission Trend 📈 Readmission Rate 📈 Bed Occupancy Rate 📈 Top Doctors by Patient Volume 📈 Revenue by Department 📈 Revenue by Doctor 📈 Most Common Diagnosis 📈 Patient Distribution by City 📈 Average Patient Age 🎯 This project reflects the type of SQL analysis performed by Healthcare Analysts, Hospital Operations teams, Business Intelligence Analysts, and Data Analysts working in hospitals and health-tech companies. 💡 Double Tap ❤️ For More

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🚀 SQL Project Series #6: Banking Transaction Analysis 🏦 Learn how banks use SQL to analyze customer transactions, monitor account activity, detect fraud, and generate business insights. 🎯 Business Objectives ✅ Analyze customer transactions ✅ Calculate account balances ✅ Identify high-value customers ✅ Detect suspicious transactions ✅ Monitor transaction trends ✅ Analyze deposits vs withdrawals ✅ Measure customer activity ✅ Identify dormant accounts ✅ Generate banking KPIs 📂 Step 1: Create Database CREATE DATABASE banking_db; USE banking_db; 📂 Step 2: Create Customers Table CREATE TABLE customers ( customer_id INT PRIMARY KEY, customer_name VARCHAR(100), gender VARCHAR(10), city VARCHAR(50), account_open_date DATE ); 📂 Step 3: Create Accounts Table CREATE TABLE accounts ( account_id INT PRIMARY KEY, customer_id INT, account_type VARCHAR(30), branch_name VARCHAR(100), opening_balance DECIMAL(12,2), FOREIGN KEY (customer_id) REFERENCES customers(customer_id) ); 📂 Step 4: Create Transactions Table CREATE TABLE transactions ( transaction_id INT PRIMARY KEY, account_id INT, transaction_date TIMESTAMP, transaction_type VARCHAR(20), amount DECIMAL(12,2), FOREIGN KEY (account_id) REFERENCES accounts(account_id) ); 📂 Step 5: Insert Sample Customers INSERT INTO customers VALUES (1,'Rahul Sharma','Male','Mumbai','2023-01-10'), (2,'Priya Verma','Female','Delhi','2023-02-18'), (3,'Amit Patel','Male','Pune','2023-04-12'), (4,'Sneha Joshi','Female','Bangalore','2023-05-22'), (5,'Rohan Gupta','Male','Hyderabad','2023-08-01'); 📂 Step 6: Insert Sample Accounts INSERT INTO accounts VALUES (101,1,'Savings','Mumbai',50000), (102,2,'Savings','Delhi',35000), (103,3,'Current','Pune',100000), (104,4,'Savings','Bangalore',45000), (105,5,'Current','Hyderabad',75000); 📂 Step 7: Insert Sample Transactions INSERT INTO transactions VALUES (1001,101,'2025-01-05 10:15:00','Deposit',15000), (1002,101,'2025-01-06 12:30:00','Withdrawal',5000), (1003,102,'2025-01-07 14:20:00','Deposit',25000), (1004,103,'2025-01-08 16:10:00','Withdrawal',30000), (1005,104,'2025-01-09 09:45:00','Deposit',12000), (1006,105,'2025-01-10 11:00:00','Withdrawal',10000); 🧠 SQL Concepts You'll Practice ✔ DDL Commands ✔ DML Commands ✔ Primary & Foreign Keys ✔ Joins ✔ Aggregate Functions ✔ CASE WHEN ✔ GROUP BY ✔ HAVING ✔ Window Functions ✔ CTEs ✔ Date & Time Functions 📊 Business KPIs You Can Build 📈 Total Transaction Amount 📈 Total Deposits 📈 Total Withdrawals 📈 Net Cash Flow 📈 Current Account Balance 📈 Average Transaction Amount 📈 Daily Transaction Volume 📈 Monthly Transaction Trend 📈 Branch-wise Transactions 📈 Account Type Analysis 📈 Top Customers by Transaction Value 📈 Most Active Customers 📈 Dormant Accounts 📈 High-Value Transactions 📈 Deposit vs Withdrawal Ratio 📈 Fraud Detection Alerts 📈 Average Balance by Branch 📈 Customer Growth 📈 Transaction Success Rate 📈 Peak Transaction Hours 💡 Double Tap ❤️ For More

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📊 Essential SQL Concepts Every Data Analyst Must Know 🚀 SQL is the most important skill for Data Analysts. Almost every analytics job requires working with databases to extract, filter, analyze, and summarize data. Understanding the following SQL concepts will help you write efficient queries and solve real business problems with data. 1️⃣ SELECT Statement (Data Retrieval) What it is: Retrieves data from a table.

SELECT name, salary
FROM employees;

Use cases: Retrieving specific columns, viewing datasets, extracting required information. 2️⃣ WHERE Clause (Filtering Data) What it is: Filters rows based on specific conditions.

SELECT *
FROM orders
WHERE order_amount > 500;

Common conditions: =, >, <, >=, <=, BETWEEN, IN, LIKE 3️⃣ ORDER BY (Sorting Data) What it is: Sorts query results in ascending or descending order.

SELECT name, salary
FROM employees
ORDER BY salary DESC;

Sorting options: ASC (default), DESC 4️⃣ GROUP BY (Aggregation) What it is: Groups rows with same values into summary rows.

SELECT department, COUNT(*)
FROM employees
GROUP BY department;

Use cases: Sales per region, customers per country, orders per product category. 5️⃣ Aggregate Functions What they do: Perform calculations on multiple rows.

SELECT AVG(salary)
FROM employees;

Common functions: COUNT(), SUM(), AVG(), MIN(), MAX() 6️⃣ HAVING Clause What it is: Filters grouped data after aggregation.

SELECT department, COUNT(*)
FROM employees
GROUP BY department
HAVING COUNT(*) > 5;

Key difference: WHERE filters rows before grouping, HAVING filters groups after aggregation. 7️⃣ SQL JOINS (Combining Tables) What they do: Combine tables. -- INNER JOIN

SELECT orders.order_id, customers.customer_name
FROM orders
INNER JOIN customers
ON orders.customer_id = customers.customer_id;

-- LEFT JOIN

SELECT customers.customer_name, orders.order_id
FROM customers
LEFT JOIN orders
ON customers.customer_id = orders.customer_id;

Common types: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN 8️⃣ Subqueries What it is: Query inside another query.

SELECT name
FROM employees
WHERE salary > (SELECT AVG(salary) FROM employees);

Use cases: Comparing values, filtering based on aggregated results. 9️⃣ Common Table Expressions (CTE) What it is: Temporary result set used inside a query.

WITH high_salary AS (
  SELECT name, salary
  FROM employees
  WHERE salary > 70000
)
SELECT *
FROM high_salary;

Benefits: Cleaner queries, easier debugging, better readability. 🔟 Window Functions What they do: Perform calculations across rows related to current row.

SELECT name, salary, RANK() OVER (ORDER BY salary DESC) AS salary_rank
FROM employees;

Common functions: ROW_NUMBER(), RANK(), DENSE_RANK(), LAG(), LEAD() Why SQL is Critical for Data Analysts • Extract data from databases • Analyze large datasets efficiently • Generate reports and dashboards • Support business decision-making SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v Double Tap ♥️ For More

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