Why SQL is Important for Data Science?

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Data science is hot right now. The world is moving towards digitalization and the data has a very crucial role to play in every field. Various industries are collecting billions of customers’ data every day. The management and analysis of this data require a certain skill set for extracting the meaning out of it. SQL for Data Science is therefore very important for handling such large amounts of data. Here’s the reason Why SQL is Important for 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

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.

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.

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).

So, that’s how important SQL is for data science. Hope it’s useful for you.

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