An Intro to SQL for Data Scientists

Data scientists are still in high demand, with more and more businesses unlocking the power of big data to improve their operations and profits. It’s a great career option for anyone who enjoys working with data and finding answers to challenging problems. However, there are some specific skills data scientists need to succeed in the field.

Although it’s not their primary job, data scientists must know how to code and use a suite of tools for collecting, managing, and analyzing data, including SQL. But what is SQL, and how can you learn to use it for a career in data science? Here’s what you need to know if you’re a complete beginner.

SQL, or Structured Query Language, (also called Sequel) is a powerful and
versatile open-source programming tool. It is used for relational databases,
meaning databases storing data that has a pre-defined relationship. Relational databases are maintained in tables of columns and rows, which can have relationships to one another, without being combined. This is extremely useful for large databases, which are primarily what data scientists work with.

Relational databases are typically managed using SQL. SQL can
perform a number of functions within a database, ranging from extremely basic to advanced. Without SQL, the usefulness of large datasets is extremely limited.  

First, it can modify a database table, by adding, deleting, or changing information. Some of these commands can be undone; others are permanent. SQL can also be used to find specific information within a database. Controlling access to the database is also possible using SQL.

There’s a saying about data scientists: “a Data Scientist is someone who’s better at statistics than any Software Engineer, and better at software engineering than any Statistician.” It’s not enough to be curious and good with numbers. Data scientists need to know their tech and know it well. That’s where learning SQL and other programming languages comes in.

Data scientists are tasked with extracting insights from large databases. Relational databases are ideal for this, as they are able to store large amounts of data in a fairly accessible way, thanks to SQL. Beyond basic data management tasks, however, SQL can be used to create machine learning models, pre-process data, and in data mining.

Really, the entire foundation of data science relies on SQL. It is an essential skill for data scientists and is one of the most in-demand programming skills in the United States. Anyone can benefit from learning SQL, but data scientists will use it every day in their work.

Since some changes made using SQL cannot be undone, data scientists need to achieve mastery of the tool before working on live databases. Data is valuable, and if some of it is deleted by mistake, it can be a costly error.

One of the biggest benefits of SQL is that it can integrate with popular programming languages like Python and R. This is why so many businesses rely on it—it’s extremely versatile.

Raw data isn’t useful, which is why data scientists are in high demand. SQL is an important tool for organizing and analyzing data once it’s collected. It’s efficient, allowing a skilled data scientist to pull up the information they need quickly, which is a must for businesses processing large amounts of data.

SQL is also a great tool for troubleshooting. It can flag errors for you and allows you to fix problems as you go, saving a lot of time. In today’s fast-paced world, saving time is a huge bonus for businesses and data scientists.

Because SQL can be used in any industry for leveraging the power of data, it is helpful for any kind of problem or opportunity an organization may be exploring. A data scientist could use SQL to figure out where a business is losing money, for example, or analyze consumer behavior to improve sales. There are limitless applications for business optimization using data, and SQL is essential for making that process possible.

Because SQL is open source and standardized, anyone can use it, from an individual to huge companies like Amazon. It has been around for decades and is so widely used that there are countless excellent tutorials, courses, and books available for those who want to learn it. Because there are so many resources, however, it can get a little daunting.

The great news about SQL is that it is relatively simple to learn. Commands are typically English words, making it easier for novice programmers to pick them up and understand how the language functions. Additionally, SQL serves as a great base language for learning other programming languages later, on, especially those that integrate with SQL.

So how should you get started? Take it one step at a time. Start with a basic tutorial and go from there. IBM’s tutorial is a great place to begin, as the language was originally developed by two IBM employees.

Like any skill, learning SQL takes practice. Learning the fundamentals
probably won’t take you that long—maybe a few weeks to learn the basics. Mastering the tool, however, will likely take much longer.

Keep in mind that although companies are actively hiring data scientists and some are having trouble finding candidates, they will expect a certain level of proficiency in skills like SQL. Making sure you have enough practice and confidence in SQL before applying will help ensure that your job search is more successful. Once you’ve landed a job, you’ll have the ability to practice and become even more proficient in using SQL.

Whether you’re interested in becoming a data scientist or you just want to break into the tech industry and start coding, SQL is a great skill to develop. As languages go, it is fairly easy to learn, versatile, powerful, and in-demand. Data runs the world now, and the demand for people who can unlock its potential will only grow in the coming years.