Become Advanced SQL Pro: 50 Interview Questions You Must Know! - Leetcode 1607 | Data Science

Welcome to the first episode of our groundbreaking series designed to take your SQL skills to the next level! In this series, we're tackling 50 of the most challenging and insightful interview questions on #advanced SQL, aimed at transforming you from an intermediate user to a bona fide #sql expert. Whether you're preparing for a job #interview , seeking to enhance your data manipulation capabilities, or simply passionate about mastering the complexities of SQL, this series is your ultimate roadmap.

SQL Schema:
Create table If Not Exists Customer (customer_id int, customer_name varchar(20))
Create table If Not Exists Orders (order_id int, sale_date date, order_cost int, customer_id int, seller_id int)

Create table If Not Exists Seller (seller_id int, seller_name varchar(20))

Truncate table Customer
insert into Customer (customer_id, customer_name) values ('101', 'Alice')
insert into Customer (customer_id, customer_name) values ('102', 'Bob')
insert into Customer (customer_id, customer_name) values ('103', 'Charlie')
Truncate table Orders
insert into Orders (order_id, sale_date, order_cost, customer_id, seller_id) values ('1', '2020-03-01', '1500', '101', '1')
insert into Orders (order_id, sale_date, order_cost, customer_id, seller_id) values ('2', '2020-05-25', '2400', '102', '2')
insert into Orders (order_id, sale_date, order_cost, customer_id, seller_id) values ('3', '2019-05-25', '800', '101', '3')
insert into Orders (order_id, sale_date, order_cost, customer_id, seller_id) values ('4', '2020-09-13', '1000', '103', '2')
insert into Orders (order_id, sale_date, order_cost, customer_id, seller_id) values ('5', '2019-02-11', '700', '101', '2')
Truncate table Seller
insert into Seller (seller_id, seller_name) values ('1', 'Daniel')
insert into Seller (seller_id, seller_name) values ('2', 'Elizabeth')
insert into Seller (seller_id, seller_name) values ('3', 'Frank')

Pandas Schema:
data = [[101, 'Alice'], [102, 'Bob'], [103, 'Charlie']]
customer = pd.DataFrame(data, columns=['customer_id', 'customer_name']).astype({'customer_id':'Int64', 'customer_name':'object'})
data = [[1, '2020-03-01', 1500, 101, 1], [2, '2020-05-25', 2400, 102, 2], [3, '2019-05-25', 800, 101, 3], [4, '2020-09-13', 1000, 103, 2], [5, '2019-02-11', 700, 101, 2]]
orders = pd.DataFrame(data, columns=['order_id', 'sale_date', 'order_cost', 'customer_id', 'seller_id']).astype({'order_id':'Int64', 'sale_date':'datetime64[ns]', 'order_cost':'Int64', 'customer_id':'Int64', 'seller_id':'Int64'})
data = [[1, 'Daniel'], [2, 'Elizabeth'], [3, 'Frank']]
seller = pd.DataFrame(data, columns=['seller_id', 'seller_name']).astype({'seller_id':'Int64', 'seller_name':'object'})

In this first episode, we kick things off with a deep dive into a question that stumps many aspiring SQL professionals. We'll explore the intricacies of advanced query writing, focusing on techniques that go beyond the basics of SELECT statements and WHERE clauses. You'll learn about advanced joins, window functions, recursive queries, and much more.

What to Expect:

Detailed Explanations: We don't just solve the question; we dissect it. You'll understand not only the 'how' but also the 'why' behind each solution, ensuring you can apply these principles to a variety of SQL challenges.
Step-by-Step Approach: Our tutorials are designed to be easy to follow. We break down complex concepts into manageable parts, making advanced SQL more accessible than ever.
Real-World Applications: These aren't just theoretical exercises. Each question is selected for its relevance to real-world SQL problems, ensuring you gain practical skills that you can apply in a professional setting.
Interactive Learning: Engage with the content through quizzes and challenges presented throughout the series. Test your knowledge, practice new skills, and solidify your understanding of advanced SQL techniques.

Why This Series?

SQL remains one of the most in-demand skills in the tech industry, and for a good reason. It's the backbone of data analysis, database management, and many forms of programming. As data continues to drive decision-making in businesses worldwide, the ability to manipulate and retrieve data efficiently becomes increasingly valuable. This series is designed to equip you with these skills, ensuring you're not just ready for your next job interview but also prepared to tackle real-world data challenges.

Who Should Watch?

Intermediate SQL users looking to advance their skills.
Job seekers preparing for technical interviews.
Data professionals seeking to deepen their understanding of SQL.
Anyone with a passion for data and an eagerness to learn.
How to Get the Most Out of This Series:

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[kamalkrsnadas]

Sir, When are you planning to upload videos of stratascratch projects?

[arunkaiser]

Looks simple but great content I am ur fan from Bengaluru ❤

[abhijeetjain]

select seller_name from Seller
where seller_id not in (select seller_id from Orders_l where DATEPART(YEAR, sale_date) = 2020)
order by seller_name

[vijaygupta]

Look like simple but making logic is little hard

[Vala]

select seller_name
from Seller d
INNER JOIN Orders c
on d.seller_id = c.seller_id
WHERE seller_name NOT IN
(

select seller_name
from Seller a
INNER JOIN Orders b
on a.seller_id = b.seller_id

where YEAR(b.sale_date) = 2020
)

[roopeshroope]

SELECT s.seller_name
FROM Seller s
LEFT JOIN Ordes o
ON s.seller_id = o.seller_id
AND EXTRACT(YEAR FROM o.sale_date) = 2020
WHERE o.order_id IS NULL
ORDER BY s.seller_name ASC;