Importance of SQL for Data Science

1. Easy to Learn and Use

SQL is always appreciated for its simplicity because of its easy syntax that makes use of the English language words. It helps you to easily understand the concepts, unlike some other complex programming languages which require a lot more effort and conceptual understanding.

If you are a newbie in the field of Data Science then SQL is the perfect starting point for you. You can easily query and manipulate your data for extracting insights from it with just a few lines of code.

2. Understanding your Data

Data is the core element in Data Science. For doing Data Science, you must be able to extract the real meaning out of your data in which SQL is going to help you.

SQL for Data Science provides you with the ability to explore and visualize your dataset efficiently for producing accurate results. It will help you to handle the missing and null values, outliers and other anomalies in the data.

SQL for Data Science also helps you to have a better understanding of your dataset and organize it according to your needs.

3. SQL is Everywhere

SQL for Data Science has become the first choice of almost all the leading organizations. It is becoming a standard to use SQL for Data Science and many of the business giants like Facebook, Google, Amazon, Netflix, Uber, etc. All of the above mentioned are using SQL for performing various Data Science processes.

For any job related to Data like Data Scientist, Data Analyst, Database Administrator, Business Analyst, etc. you must have SQL in your tool kit because you will definitely require SQL for interacting with your data.

4. SQL Integrates with Scripting Languages

Along with data querying and manipulation, SQL also helps in data visualization to some extent.

While working on a project as a Data Scientist, you will sometimes need to explain your findings to the other team members of the organization. The explanation should be in such a way that it becomes easy to understand.

In such cases, SQL for Data Science will help you as it easily integrates with the most commonly used scripting languages such as R programming and Python. Some SQL libraries like SQLite, MySQLdb, etc. also allow you to connect the client application with your database. It makes the development process a bit easier.

5. SQL is Declarative

SQL is a nonprocedural language. One of the important advantages of SQL over other conventional programming languages like R, Python is that in SQL you only need to specify what you want to do without specifying the necessary steps for doing it.

Using SQL for Data Science allows you to perform complex operations in comparatively less time and code.

6. Manage Large Volumes of Data

Data Science involves the collection and management of huge volumes of data in the database. But using spreadsheets for such large amounts of data becomes a tedious job. Thus, SQL provides you the suitable resources for dealing with such large amounts of data and gaining insights from it.

Learning SQL for Data Science will also make it easy for you to learn NoSQL databases. These are popular for working with big volumes of data and provides better flexibility and scalability.

7. Never Ending Scope

Despite being old, SQL is still preferred by a large number of Data Scientists for handling the tasks related to data storage.

According to the 2017 and 2018 Developer Survey of StackOverflow, SQL for Data Science proved to be more popular than the widely used programming languages R and Python.

After the introduction of many new technologies in the market like NoSQL, Hadoop, etc., SQL is still preferred by Data Scientists with all levels of experience.