Must-Know Advanced SQL Techniques for Data Science

Mastering the Art of Data Grouping, Pivoting, Truncating and More with SQL

SQL is the Swiss Army Knife of data science.

It’s a powerful tool that allows you to quickly and efficiently manipulate, process, and analyze large datasets.

However, to truly unleash its full potential, you need to master advanced techniques that go beyond the basics. In this article, we’ll dive into five key areas where you can enhance your SQL skills for data science.

1. Handling Missing Values

Missing values can be a real headache for data scientists. They can cause all sorts of problems, from skewed statistics to broken models. But don’t worry, SQL has got your back! With a few simple functions, you can get rid of those pesky missing values in no time.

COALESCE

The first function we’ll look at is COALESCE. This function takes a list of expressions and returns the first non-NULL expression. In other words, if you have several columns with missing values and you want to fill them with a default value, COALESCE is your friend.

SELECT COALESCE(column1, 0) AS column1,
       COALESCE(column2, 'Unknown') AS column2
FROM mytable;