Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources
Covering all technical and popular stuff about anything related to Data Science: AI, Big Data, Machine Learning, Statistics, general Math and the applications of former. Ads/ Promo: @love_data
Mostrar más📈 Análisis del canal de Telegram Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources
El canal Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources (@sqlproject) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 39 615 suscriptores, ocupando la posición 4 706 en la categoría Educación y el puesto 9 734 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 39 615 suscriptores.
Según los últimos datos del 25 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -62, y en las últimas 24 horas de -6, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.30%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.70% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 911 visualizaciones. En el primer día suele acumular 277 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
- Intereses temáticos: El contenido se centra en temas clave como analytic, dataset, visualization, sql, learning.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Covering all technical and popular stuff about anything related to Data Science: AI, Big Data, Machine Learning, Statistics, general Math and the applications of former.
Ads/ Promo: @love_data”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 26 octubre, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.
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');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 MoreSELECT 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