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Four Reasons You Must Learn SQL in Data Science

Is SQL important for data science? It certainly is! This language can help you build a foundation for your analytical career. Let’s see how you use SQL in data science.

Data science is hot right now. What if you could predict the next market crash? Or contain the spread of Ebola? Or accurately predict a health crisis months or even years before it happens? Data scientists are working hard on these kinds of projects, and they are earning healthy salaries in the process. No wonder that data scientist has been crowned the Sexiest Job of the 21st Century by the Harvard Business Review.

Let’s go back to the idea of predicting problems and finding solutions with data science. For this to happen, a mountain (or two) of data is needed. Many countries have adopted open data initiatives, so public data repositories are becoming more complex and more common. Tapping into all this information requires being able to communicate with the databases that store it.

There are several programming languages you can use for your analyses, e.g. Python or R. Is SQL important for data science if you can choose another one? Of course, you are not obliged to use SQL, but it’s a good choice for those that want to start learning their first language. I will explain the reasons later on.

SQL in Data Science Starts withDatabase

Before I explain why you would use SQL in data science, I’ll clarify basic data concepts. If your eyes are glazing over at the notion of databases, stay with me. Databases aren’t new; it’s only that the Big Data era has injected a sense of newness and urgency into the world of databases.

Basically, there are three common types of database: hierarchical, network, and relational. A relational database is independent of its applications – the database structure can be modified without impacting any connected applications. In a relational database, you can define complex relationships between tables, and you can access the relations directly.

In contrast, a hierarchical or network database is often designed for a specific application. These two database types are considered legacy solutions.

In short, relational databases have become the most common data storage mechanism, and SQL is the most common way to communicate with them.

What Is SQL?

This article talks about SQL in data science, but what is SQL exactly? Structured Query Language, commonly abbreviated to SQL, is a powerful programming language that can add, delete, extract, or operate on information within a relational database. You can even use SQL to perform complicated analytical functions and change the structure of the database itself – adding or deleting tables, for instance. It became an ANSI standard in 1986 and an ISO standard in 1987.

There are different “flavors” of SQL that work with different database engines. For example, PostgreSQL complies as closely as possible with the SQL standard, while other engines use their own variant, e.g. Microsoft SQL Server uses Transact-SQL, or T-SQL. Like dialects in a spoken language, these SQL variants occasionally use different words or structures. They can also have additional functionalities that are unique to that variant. However, they are still firmly recognizable as SQL

Four Reasons Why SQL is Awesome

Now that we have answered the question ‘How important is SQL for data science? and have explained what it is, let’s dig into four reasons why any aspiring professional needs SQL in data science:

  1. It’s Becoming a Standard to Use SQL in Data Science
    SQL proficiency is a basic requirement for many data science jobs, including data analyst, business intelligence developer, programmer analyst, database administrator, and database developer. You’ll need SQL to communicate with the database and work with the data. Many technical interviews for these jobs test SQL skills in some way, usually in the whiteboard test (i.e. where you solve a problem by writing code on a whiteboard).
  2. SQL Integrates with Scripting Languages
    Is SQL important in data science? Sometimes it will give you all the insights you need. But you may want to take it further. Maybe you want to summarize the data in a particular way and then create a nice data visualization for your web application. Or maybe you want to use the query result as one of the inputs for the next step in some code you’re writing. Or maybe you have a working script package and you want to integrate it into the SQL environment.
    Luckily, you can convert the result set into an XML or JSON format and use it for subsequent data consumption. Depending on the version of SQL you use, specialized connection libraries (such as SQLite and MySQLdb) allow you to connect a client app to your database. You can even integrate your code package as a stored procedure. This makes exploratory data analysis, algorithm building and tuning, and model evaluation and deployment a lot easier.
  3. SQL is Declarative
    Machine learning
    involves self-learning algorithms – algorithms that can adjust their performance without having the process hard-coded in a set of logical rules. In other words, machine learning lets you specify your objective without specifying how it is done. SQL works in a similar way.
    SQL is nonprocedural and designed specifically for accessing data. The primary difference between SQL and conventional programming languages (R, Python, Java, etc.) is that SQL statements specify WHAT data operations should be performed rather than HOW to perform them. When you write Python script, the Python interpreter reads your program line by line and carries out the instructions in each line. If you’ve ever written any code, you know how long that takes!
    In contrast, SQL’s concise set of commands save time and reduce the amount of programming required to perform complex queries. Instead of directing a compiler along each step of the way, you simply tell it what you want it to do.
  4. SQL Prepares You for NoSQL
    How important is SQL for data science? If you’re planning a serious data career, there’s one more reason to start with this language. Big Data’s velocity and volume have made NoSQL databases more popular. NoSQL is prized for its scalability and flexibility, but because it has evolved so quickly there is currently no standard engine or interface. Tackle SQL first, and learning NoSQL will be a lot easier. Once you have a solid SQL foundation, you’ll appreciate the limitations as well as the advantages of NoSQL (i.e. NoSQL uses flexible document objects rather than SQL’s predetermined, fixed tabular schema).

Using SQL in Data Science Opens Doors

After going through my article, you are able to answer the question “How important is SQL for data science?”. Many people are rushing headlong into data science, machine learning, and artificial intelligence. It is vitally important that you set yourself apart by mastering the foundations of this field as well as the flashier concepts. Mastering SQL in data science will give you a good understanding of relational databases, which are the bread and butter of this field. It will also boost your professional profile, especially compared to those with limited database experience.

There are many ways you can get started with SQL in data science, including LearnSQL.com’s SQL Basics course. The important thing is to start soon, test your comprehension along the way, and build yourself a quality skill set that can serve as a launching pad for your career in data science.

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