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🚀 Master SQL Aggregation with SUM and GROUP BY in Airbnb Data
In this video, we break down an SQL query that calculates the total number of beds per nationality from Airbnb data using the SUM() function and the GROUP BY clause. You’ll learn how to:
Use SUM() to aggregate data and calculate totals, like the total number of beds.
Join tables effectively using the JOIN condition to combine data from Airbnb apartments and hosts.
Group data by nationality to get insights into how many beds are available per nationality in the Airbnb platform.
📌 What You’ll Learn:
How to write SQL queries for data aggregation using SUM() and GROUP BY.
Joining multiple tables with conditions for better insights.
Analyzing Airbnb data for market trends and host information.
Whether you’re an SQL beginner or preparing for data analysis interviews, this video will guide you step-by-step in using aggregation functions and SQL joins. Perfect for anyone working with large datasets or preparing for a SQL job interview.
💡 Why Watch?
Understand how to work with Airbnb data, aggregate important metrics, and optimize your SQL queries for real-world applications.
🔗 Don't forget to like, share, and subscribe for more SQL tutorials, data analysis tips, and interview prep!
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-----------------------Create Table----------------------------
CREATE TABLE airbnb_apartments(host_id int,apartment_id varchar(5),apartment_type varchar(10),n_beds int,n_bedrooms int,country varchar(20),city varchar(20));
INSERT INTO airbnb_apartments VALUES(0,'A1','Room',1,1,'USA','NewYork'),(0,'A2','Room',1,1,'USA','NewJersey'),(0,'A3','Room',1,1,'USA','NewJersey'),(1,'A4','Apartment',2,1,'USA','Houston'),(1,'A5','Apartment',2,1,'USA','LasVegas'),(3,'A7','Penthouse',3,3,'China','Tianjin'),(3,'A8','Penthouse',5,5,'China','Beijing'),(4,'A9','Apartment',2,1,'Mali','Bamako'),(5,'A10','Room',3,1,'Mali','Segou')
CREATE TABLE airbnb_hosts(host_id int,nationality varchar(15),gender varchar(5),age int);
INSERT INTO airbnb_hosts VALUES(0,'USA','M',28),(1,'USA','F',29),(2,'China','F',31),(3,'China','M',24),(4,'Mali','M',30),(5,'Mali','F',30);