SQL is a query language, which means it’s used to write queries. Queries are instructions that tell our database what information we need to retrieve.

SQL databases let us store very large tables and retrieve smaller tables that are useful, manageable, and easy to understand.

This seemingly simple task lies at the heart of data science because it lets us work with large amounts of data efficiently.

Retrieving tables is not a passive process. Most of SQL’s power comes from building tables that display new insights into our data.

Why learn SQL?

You’ll need SQL to become a strong data scientist.

You’ll need it because it’s the best language to manipulate data on a large scale. SQL was designed to be efficient in speed and memory usage, while other mainstream languages used for analytics are not (like Python or R).

• Google Sheets allows you to manipulate and visualize data, but it can’t be used to store much information.
• Other programs, like Hadoop or Spark, can scale further than SQL, but SQL is far more user-friendly and better for getting insights from data.

Most analytics teams prefer SQL because it’s the easiest tool that allows us to manage and process large data sets.

Pandas, Julia, and NoSQL have attempted to dethrone SQL as the new de-facto data tool. They all failed consistently.

Understanding SQL will also help you beyond SQL:

• You’ll need it to get into data engineering, which is building products and infrastructure that help people process data.
• Most of the concepts you’ll learn can be associated with Pandas and R as well.

If you’re starting your path toward data science, SQL might be the best place to start because of its independence. You can use SQL to retrieve product and business metrics, dashboards, and aggregate statistics, and you’ll almost always need SQL to pull the data you need when working on real-world problems with Python or R.

This makes SQL questions extremely common in data science interviews.