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Learn SQL for Data Science With

If you are interested in a data science career and have looked at a few relevant job descriptions, you've probably noticed many leading tech companies expect their data scientists to know SQL. Let's see how you can learn SQL for data science and become more valuable to any data-driven organization.

A data scientist is a complex technical role that expects a combination of the skills of a statistician, a business analyst, and an IT expert. Collecting data is also part of the job. So, no wonder SQL, the primary programming language for communicating with relational databases, is one of the critical skills needed for data scientists.

The demand for such specialists has grown significantly in recent years. This is no wonder since organizations are increasingly realizing that it pays to base their strategic decisions on data. The popular phrase "be data-driven" did not come out of nowhere; it is a feature of today's successful business. Want to know more about it? Read this great article.

Let's explore in depth what SQL is and why data scientists should learn this programming language.

SQL for Data Science

SQL, or Structured Query Language, is a programming language used to interact with databases. You use SQL to store, manipulate, and retrieve data from relational databases.

Since most organizations today capture and store data using one or more relational databases like SQL Server, Oracle, and MySQL, the ability to extract data from these databases is one of the essential skills for data scientists.

But SQL is much more than just data extraction from relational databases. First, with SQL, you can process large amounts of data (e.g., millions of rows), really fast.

Second, you can reuse SQL queries over and over again to analyze your data and produce periodical reports in a matter of minutes. SQL's high speed, scalability, and repeatability make it an extremely valuable tool for data analysts and data scientists across different industries.

Finally, mastering SQL reduces the data scientist's dependence on database administrators. This gives you more freedom in performing data analysis of small datasets as well as big data stored in relational databases. You can write an SQL query as soon as a new idea comes up for extracting valuable insights from your data. You get your results fast and reliably without the need to request information from another team.

Learn SQL for Data Science With

So, let's acquire some new skills! In this article, I'll show how you can learn the basics of SQL for data science with interactive SQL courses at

Why Learn SQL for Data Science With

We are lucky to have tons of learning resources and courses available online these days. However, with this huge variety, comes a new challenge – selecting the most effective and efficient learning path for mastering SQL for data science.

The platform along with its online SQL courses is a great choice. Here's why:

  1. Interactive exercises with detailed explanations. Each course on consists of dozens or even hundreds of coding exercises. However, before you are asked to write code (i.e., SQL query), you are provided with a piece of reading that explains the relevant concepts in detail and with examples. The platform automatically verifies the SQL code you submit in your exercises, and you get feedback immediately. Learn SQL for Data Science With
  2. SQL experience from the comfort of your browser. You don't need to install any additional software on your computer to practice on the platform. After you complete an exercise in your browser, a real database runs your code, checks your solution, and tells you if it's correct or not and if not, where to look for an error.
  3. Real-world examples. The datasets and coding challenges are designed to resemble the problems you are likely to encounter in your actual job assignments. So, you are not just learning SQL syntax. You are also gaining an understanding of how data is usually organized in relational databases at retail stores, manufacturing companies, educational institutions, and other organizations.
  4. Suitable for IT newbies. You can take courses on without any prior exposure to IT or other programming languages. They are designed for analysts across different industries and areas of expertise, including marketing, sales, HR, logistics, etc. No IT background is needed.

I'm sure you'll have a wonderful experience studying on This is simply the best place to learn SQL. The creators of these interactive courses are passionate people who love to share their knowledge with others.

Now, let's discuss the learning path to learn SQL for data science.

SQL Courses for Data Science

There are many great books, guides, and tutorials for aspiring data analysts and data scientists. However, when learning a new programming language, practice is a key to success.

To master any programming language, you need to code from the very beginning. Interactive courses that provide introductions to new topics along with relevant coding challenges are the best way to learn SQL.

Let's see which SQL courses for data science are the most relevant.

1.     SQL Basics

Whether you learn SQL for data science, marketing, or logistics analysis, you need to start with the basics. The SQL Basics course teaches how to get data from a database.

Learn SQL for Data Science With

With 129 interactive exercises, it covers the fundamental SQL topics such as retrieving data from a database, combining information from multiple tables, aggregating and grouping data, performing simple computations on data, etc. The course is designed for beginners, but it is also useful for those who have basic SQL knowledge and want to refresh and consolidate what they've learned in the past.

The SQL Basics course is an awesome, thorough, and practical introduction to SQL. On, it is available for standard SQL as well as for the MS SQL Server, PostgreSQL, and MySQL dialects. This is valuable if your organization uses one of these relational database management systems (RDBMSs).

After you learn the basics, you are ready to continue with other practical courses.

2.     How to INSERT, UPDATE, and DELETE Data in SQL

As a data scientist, you need to do more than just retrieve data from a database. If you want to learn how to add, modify, and remove data from a database, I recommend taking the course How to INSERT, UPDATE, and DELETE Data in SQL.

With 52 interactive exercises, it teaches basic and advanced features of the data manipulation language (DML) commands INSERT, UPDATE, and DELETE. It covers topics such as default values in INSERT and UPDATE, working with NULL values and conditions in DML commands, etc.

What you learn in this course allows you to do data cleaning. According to Anaconda's 2021 State of Data Science survey, data scientists spend about 39% of their time on data preparation and cleaning. This is more than the total time spent on training, selecting, and deploying models.

After completing the course, you'll know how to put data into a database, modify it, perform some computations if necessary, and then pull it from the database with the SQL queries mastered in the SQL Basics course.

The course How to INSERT, UPDATE, and DELETE Data in SQL is available for standard SQL as well as for the MS SQL Server, PostgreSQL, and MySQL dialects.

3.     Standard SQL Functions

The Standard SQL Functions course is essential for data scientists. It teaches how to process numerical, text, and other types of data with the most widely used functions in SQL. With 211 interactive exercises, the course covers working with text values in SQL, mastering mathematical operators in SQL, using date and time functions, dealing with NULL values, and more.

After completing this course, you can do more advanced data cleaning as well as more computations on your data like adding computed columns to the table, for example.

The platform offers the Standard SQL Functions course for standard SQL, where you learn the functions available in the most popular database engines. However, it also offers the same course in a few popular dialects including MS SQL Server, PostgreSQL, and MySQL.

Learn SQL for Data Science With

The three courses introduced above are combined in the SQL Fundamentals track. You may take this learning track instead of completing each of the three courses separately.

4.     Advanced SQL: Window Functions

After you complete the track that covers SQL fundamentals, you can choose to master more advanced topics like window functions, for example.

The Window Functions course is aimed at advanced data scientists and data analysts. While window functions are a relatively new concept in SQL, they have already become very popular among more advanced SQL users. These functions allow you to compute statistics and aggregations while keeping details of individual rows.

With 218 interactive exercises, the course teaches how to compute running totals and running averages, how to build rankings and find best and worst performers, how to investigate trends across time, and how to calculate contributions to the whole such as commission percentages.

Window functions often make complex queries simpler. So, if you prepare a lot of reports in SQL, mastering window functions can speed up your work significantly.

The Window Functions course is available in standard SQL as well as in the MS SQL Server, PostgreSQL, and MySQL dialects. Is the Best Place to Learn SQL for Data Science!

Having read this article, you now know becoming a data scientist is worth it. Moreover, if you want to advance and be successful in this field, you should learn SQL. And is the best place to do so.

We create our interactive SQL courses by paying attention to the smallest details to give you the best learning experience.

What to do to start learning? It's easy:

Thanks for reading, and good luck in achieving your goals!