SQL Elite: Prep for High Paying Tech Interviews with Advanced Qs - Data Science | Leetcode 1549

Welcome to this 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 50 Playlist: https://www.youtube.com/playlist?list=PLtfxzVLWb-B-aQ1U1AaOA8LBrCoE-3TX2

Question: https://leetcode.com/problems/the-most-recent-orders-for-each-product/description/?envType=study-plan-v2&envId=premium-sql-50

SQL Schema:
Create table If Not Exists Customers (customer_id int, name varchar(10))
Create table If Not Exists Orders (order_id int, order_date date, customer_id int, product_id int)
Create table If Not Exists Products (product_id int, product_name varchar(20), price int)
Truncate table Customers
insert into Customers (customer_id, name) values ('1', 'Winston')
insert into Customers (customer_id, name) values ('2', 'Jonathan')
insert into Customers (customer_id, name) values ('3', 'Annabelle')
insert into Customers (customer_id, name) values ('4', 'Marwan')
insert into Customers (customer_id, name) values ('5', 'Khaled')
Truncate table Orders
insert into Orders (order_id, order_date, customer_id, product_id) values ('1', '2020-07-31', '1', '1')
insert into Orders (order_id, order_date, customer_id, product_id) values ('2', '2020-7-30', '2', '2')
insert into Orders (order_id, order_date, customer_id, product_id) values ('3', '2020-08-29', '3', '3')
insert into Orders (order_id, order_date, customer_id, product_id) values ('4', '2020-07-29', '4', '1')
insert into Orders (order_id, order_date, customer_id, product_id) values ('5', '2020-06-10', '1', '2')
insert into Orders (order_id, order_date, customer_id, product_id) values ('6', '2020-08-01', '2', '1')
insert into Orders (order_id, order_date, customer_id, product_id) values ('7', '2020-08-01', '3', '1')
insert into Orders (order_id, order_date, customer_id, product_id) values ('8', '2020-08-03', '1', '2')
insert into Orders (order_id, order_date, customer_id, product_id) values ('9', '2020-08-07', '2', '3')
insert into Orders (order_id, order_date, customer_id, product_id) values ('10', '2020-07-15', '1', '2')
Truncate table Products
insert into Products (product_id, product_name, price) values ('1', 'keyboard', '120')
insert into Products (product_id, product_name, price) values ('2', 'mouse', '80')
insert into Products (product_id, product_name, price) values ('3', 'screen', '600')
insert into Products (product_id, product_name, price) values ('4', 'hard disk', '450')

Pandas Schema:
data = [[1, 'Winston'], [2, 'Jonathan'], [3, 'Annabelle'], [4, 'Marwan'], [5, 'Khaled']]
customers = pd.DataFrame(data, columns=['customer_id', 'name']).astype({'customer_id':'Int64', 'name':'object'})
data = [[1, '2020-07-31', 1, 1], [2, '2020-7-30', 2, 2], [3, '2020-08-29', 3, 3], [4, '2020-07-29', 4, 1], [5, '2020-06-10', 1, 2], [6, '2020-08-01', 2, 1], [7, '2020-08-01', 3, 1], [8, '2020-08-03', 1, 2], [9, '2020-08-07', 2, 3], [10, '2020-07-15', 1, 2]]
orders = pd.DataFrame(data, columns=['order_id', 'order_date', 'customer_id', 'product_id']).astype({'order_id':'Int64', 'order_date':'datetime64[ns]', 'customer_id':'Int64', 'product_id':'Int64'})
data = [[1, 'keyboard', 120], [2, 'mouse', 80], [3, 'screen', 600], [4, 'hard disk', 450]]
products = pd.DataFrame(data, columns=['product_id', 'product_name', 'price']).astype({'product_id':'Int64', 'product_name':'object', 'price':'Int64'})

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.
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.

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 to learn.

Видео SQL Elite: Prep for High Paying Tech Interviews with Advanced Qs - Data Science | Leetcode 1549 канала Everyday Data Science