8 SQL Techniques to Perform Data Analysis for Analytics and Data Science

SQL is a key cog in a data science professional's armory. I'm speaking from experience - you simply cannot expect to carve out a successful career in either analytics or data science if you haven't yet picked up SQL.

And why is SQL so important?

As we move into a new decade, the rate at which we are producing and consuming data is skyrocketing by the day. To make smart decisions based on data, organizations around the world are hiring data professionals like business analysts and data scientists to mine and unearth insights from the vast treasure trove of data.

And one of the most important tools required for this is - you guessed it - SQL!

Structured Query Language (SQL) has been around for decades. It is a programming language used for managing the data held in relational databases. SQL is used all around the world by a majority of big companies. A data analyst can use SQL to access, read, manipulate, and analyze the data stored in a database and generate useful insights to drive an informed decision-making process.

In this article, I will be discussing 8 SQL techniques/queries that will make you ready for any advanced data analysis problems. Do keep in mind that this article assumes a very basic knowledge of SQL.