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
إظهار المزيد📈 نظرة تحليلية على قناة تيليجرام Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources
تُعد قناة Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources (@sqlproject) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 39 615 مشتركاً، محتلاً المرتبة 4 706 في فئة التعليم والمرتبة 9 734 في منطقة الهند.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 39 615 مشتركاً.
بحسب آخر البيانات بتاريخ 25 أكتوبر, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار -62، وفي آخر 24 ساعة بمقدار -6، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 2.30%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 0.70% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 911 مشاهدة. وخلال اليوم الأول يجمع عادةً 277 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 3.
- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل analytic, dataset, visualization, sql, learning.
📝 الوصف وسياسة المحتوى
يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“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”
بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 26 أكتوبر, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التعليم.
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