When you are working as a data scientist, there is an important procedure, called the Data Science process. This process includes a structured way and a practical approach to performing experiments with data. First, you need to define the problem and find what exactly you have to do. Once you define the problem, the data science process will begin. Some important steps in this process are collecting data, cleaning and organizing data, and exploring data. To accomplish these steps, you need to know how to work with some tools like SQL. In other words, data scientists can use SQL to manipulate data and store them in tables and databases.

Once obtaining and collecting data accomplish, data scientists have to organize data and clean them before doing any analysis. Cleaning data can help to enhance the quality of data and get rid of some errors with different techniques and utilizing built-in SQL functions and clauses. Missing values, corrupted values, timezone differences, and data range errors are some kinds of common errors in a dataset that need to be checked. Data scientists may use some test values to make sure that all values in datasets are totally clean.

When data scientists are done with these steps, they will be able to play with the data! By utilizing many of the built-in SQL functions, they can explore the data and analyze deeply to achieve meaningful insights.

Using SQL is a fast and easy way to store and retrieve structured data. With many built-in functions, data scientists can use it as the main tool for their work.