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

📈 7 Mini Data Analytics Projects You Should Try 1. YouTube Channel Analysis – Use public data or your own channel. – Track views, likes, top content, and growth trends. 2. Supermarket Sales Dashboard – Work with sales + inventory data. – Build charts for daily sales, category-wise revenue, and profit margin. 3. Job Posting Analysis (Indeed/LinkedIn) – Scrape or download job data. – Identify most in-demand skills, locations, and job titles. 4. Netflix Viewing Trends – Use IMDb/Netflix dataset. – Analyze genre popularity, rating patterns, and actor frequency. 5. Personal Expense Tracker – Clean your own bank/UPI statements. – Categorize expenses, visualize spending habits, and set budgets. 6. Weather Trends by City – Use open API (like OpenWeatherMap). – Analyze temperature, humidity, or rainfall across time. 7. IPL Match Stats Explorer – Download IPL datasets. – Explore win rates, player performance, and toss vs outcome insights. Tools to Use: Excel | SQL | Power BI | Python | Tableau React ❤️ for more!

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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);

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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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