Don’t Sleep on SQL – 5 Reasons Why it’s a Must-Have Skill in 2022 Don’t Sleep on SQL – 5 Reasons Why it’s a Must-Have Skill in 2022
While we mostly hear about Python, R, and Julia in regards to coding for data science, SQL (Structured Query Language) still... Don’t Sleep on SQL – 5 Reasons Why it’s a Must-Have Skill in 2022

While we mostly hear about Python, R, and Julia in regards to coding for data science, SQL (Structured Query Language) still has its place as a fundamental skill that supplements more popular languages. Given its ease of use and ability to quickly get started, its versatile use in data analysis, and the fact that it’s still in demand for data science and related roles, SQL shouldn’t be slept on in 2022. We could write an entire book on why SQL is a critical skill for data science, data engineering, machine learning, and data analysis. but for now, let’s focus on five reasons why you should be using SQL for data science.

1) It’s fast and easy to learn

If you’re looking to gain a new skill or enter data science & data analytics quickly, then it’s not recommended to jump right into Python or R. While those two languages will be better in the long run, they take a lot of time to learn – and SQL is a core skill that you’ll likely be using anyway. SQL is much easier to learn as an entryway into data science as it has an essential tool for manipulating data, unlike the broader applications of other languages. Plus, SQL can likely do a lot of the simpler tasks you’d be tasked with doing in a job role, such as basic data analysis, data wrangling, and building pipelines.

2) It’s an end-to-end data analysis tool

If you’re just looking to analyze data without getting into the nitty gritty of machine learning, then SQL is more than enough. Almost any data professional will have to wrangle data at some point or another, and SQL has plenty of ways to do that. Plus, as more and more companies are realizing the potential of data, then even non-technical folks may find themselves needing to view, profile, transform, and analyze data.

Another thing that makes SQL so easy to use is that while other programming languages need libraries and frameworks to get the job done, SQL can do it all by itself. With SQL, you can easily access the data, apply business logic, and display the data. It’s a one-stop shop for analysis.


3) SQL for data science is a transferable and in-demand skill.

Earlier this year, we looked into the most in-demand skills for a number of jobs and fields, such as machine learning engineering, data engineering, data analysis, and NLP, and saw that many employers are actively looking for people who know SQL. Especially for data analysts and data engineers, SQL is highly sought after as these jobs will often be wrangling data and analyzing it.

The skills someone learns with SQL are easily transferable, as small syntax changes or functions between SQL versions don’t differ greatly between them, making these changes easy to learn and accommodate. Plus, every company that takes their data seriously has a relational database, meaning they use SQL.

Even for folks in other industries, there’s going to be a need for data analysis. Marketers, journalists, etc can use data to add substance to their writing and strategies, and SQL can do more than enough to gather, analyze, and visualize data for reports or campaigns.

4) It’s a must-have for data scientists, data engineers, ML engineers, and other related roles

As we noted above, almost any role under the data science umbrella will require using SQL or other fundamental data tasks. Before any data professional starts to build models, they need to properly understand the data they’re working with. SQL is great for exploratory data analysis so you can see what you’re working with.

This is especially applicable to data and machine learning engineers, as these roles are often required to build pipelines along the entire data workflow. From start to finish, these roles are required to understand the entire flow of data so that they can debug any issues with the data they may encounter along the way.

5) It’s an evergreen skill

Did you know that SQL began in 1979? That proves its longevity and staying power. It’s not showing any signs of going away, either. As the above points illustrate, there will always be a need for data analysis, building pipelines, and transforming data.

There will always be debates about what programming language is better for certain tasks, such as the legendary battle of Python vs. R, but there’s nothing competing with SQL. As the amount of structured data stored in relational databases continues to grow, there will always be a need for SQL. Even the unstructured data movement has embraced SQL, including building SQL-like syntax to access unstructured data. Even NoSQL shares many syntax similarities with SQL, thus making it easier to learn.

Get started with SQL for data science and add it to your skillset at ODSC West 2022

If you’re looking to add an in-demand, evergreen, and broad-use skill to your repertoire, then maybe it’s time to learn SQL or other core data science skills. At ODSC West 2022, we’ll have an entire mini bootcamp track where you can start with core beginner skills and work your way up to more advanced data science skills, such as working with NLP or neural networks. By registering now, you’ll also gain access to Ai+ Training on demand for a year. Sign up now, start learning today!



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