#day 13/75DAY Population Census (Mastering SQL Challenges on HackerRank)CODING CHALLENGE #sqljoins

Welcome to Awesome data In this video, we're diving into the exciting world of SQL problem-solving on HackerRank. Today, we'll tackle the "Population Census " challenge, providing you with a step-by-step solution and valuable tips to sharpen your SQL skills.

? Imagine you have a massive pile of data that you need to sift through and organize meaningfully. ??? It's like trying to find a needle in a haystack, but instead of just one needle, you're looking for hundreds or even thousands of them. ? How do you even begin to approach this task? ?
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? This is where SQL comes in. SQL stands for Structured Query Language, and it is a domain-specific programming language used for managing and manipulating relational databases, is like a magic wand that allows you to manipulate and extract information from large databases with ease. ✨??
With SQL, you can ask complex questions and get answers quickly, saving you time and energy. ?
SQL is important for data science because it is the most commonly used language for working with relational databases. It allows data scientists to store, integrate, clean, and analyze large volumes of data efficiently. ???
SQL provides a powerful set of tools for data manipulation and analysis, making it an essential skill for any data scientist working with large and complex datasets. By mastering SQL, data scientists can extract more insights from data and make more informed data-driven decisions. ???

1) SELECT: The SELECT statement retrieves data from one or more tables. It allows you to specify the columns you want to retrieve and can also include aggregate functions to perform calculations on the data.
Syntax:
SELECT column1, column2, ...

FROM table_name;


Example: To see all the columns in the film data in our dvdrent.tar file enter the below command :
Select *

From film;

2) FROM: The FROM clause specifies the table or tables from which to retrieve the data.
Syntax:
SELECT column1, column2, ...

FROM table_name;
3) WHERE: The WHERE clause filters the results based on a specific condition or set of conditions.
Syntax:
SELECT column1, column2, ...

FROM table_name

WHERE condition;
Example: To see the details of DVDs with rental_duration of 6hrs, enter the below command :
Select *

From film

Where rental_duration = 6;

GROUP BY: The GROUP BY clause groups the results based on one or more columns.
Syntax:
SELECT column1, column2, ...

FROM table_name

WHERE condition

GROUP BY column1, column2, ...;

ORDER BY: The ORDER BY clause is used to sort the results in either ascending or descending order based on one or more columns.
Syntax:
SELECT column1, column2, ...

FROM table_name

WHERE condition

GROUP BY column1, column2, ...

HAVING condition

ORDER BY column1, column2, ... ASC|DESC;
To join two or more tables in SQL, you need to use the JOIN keyword ? and specify the columns on which the tables are related using the ON keyword ?. Here is an example of how to join two tables using the INNER JOIN ?:
SELECT *
FROM table1
INNER JOIN table2
ON table1.column_name = table2.column_name;
This statement selects all columns from both tables where there is a match between the column_name in both tables.
There are different types of SQL joins available to perform this task, including: ??
INNER JOIN: Returns only the rows where there is a match between the columns in both tables.
LEFT JOIN: Returns all the rows from the left table and the matching rows from the right table. If there is no match, then NULL values are returned for the right table.
RIGHT JOIN: Returns all the rows from the right table and the matching rows from the left table. If there is no match, then NULL values are returned for the left table.
FULL OUTER JOIN: Returns all the rows from both tables. If there is no match, then NULL values are returned for the missing data.
CROSS JOIN: Returns the Cartesian product of both tables, which means all the possible combinations of rows from both tables.

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