Importance of SQL in Data Analytics.

A basic knowledge of SQL and its query language is essential for any aspiring data analyst and data scientist. This blog discusses SQL and its data analysis features.

SQL for Data Analysis is a powerful programming language that helps data analysts interact with data stored in Relational databases. SQL stands for Structured Query language.

Since most systems today capture the data using one or more databases (like MySQL, Oracle, Redshift, SQL Server, etc.), you need to know SQL to extract data from these systems and then work with it.

But before I explain why a data analyst should learn SQL, let me take you through what data analysis is and who data analysts are:

Data analysis refers to using and manipulating data to generate insights that can help businesses solve a problem or uncover opportunities. A data analyst is a professional who collects and analyzes data in order to turn it into meaningful information that companies can use to improve and grow. These individuals gather information from a number of sources and are skilled in researching, analyzing and reporting.

Historically, MS Excel was sufficient to handle these activities. But as businesses generate more data, something more than a spreadsheet is needed to keep up with the pace. And that is where SQL enters the picture, specifically when it comes to dealing with relational databases storing large volumes of data.

Differences between Excel and SQL

How can SQL help Data analyst:

You can use SQL to help you with the following work:

· Creating databases and tables.

· Adding data to a table. Selecting data.

· Editing data

· Deleting data.

· Sorting data.

· Finding unique values.

· Combining data from two or more tables.

SQL for Data Analysis: SQL Queries

SQL queries can be classified into five parts as they perform specific roles to execute queries on any RDBMS system, and they are:

a) Data Definition Language (DDL)

DDL commands include create, alter, drop, rename and truncate, dealing with the structure of the databases. It operates on database objects like views, tables, indexes, and triggers.

b) Data Manipulation Language (DML)

DML commands include insert, update, and delete operations to modify data in existing databases.

c) Data Query Language (DQL)

This command includes a select operation to retrieve data matching criteria specified by the user. To condense data efficiently, DQL commands also involve nested queries.

d) Data Control Language (DCL)

This command is used by data administrators to grant and revoke permission to access data in the organization’s database.

e) Transaction Control Language (TCL)

TCL commands help in managing transactions in databases to commit or roll back a current transaction. TCL command is used to commit a DML operation, and it has the ability to club multiple commands in a single operation.

If you want to dig deep into SQL, my recommendation is to study as follows:

1- Basic SQL command

2- SQL join

3- SQL aggregate function

4- SQL sub queries

5- Data cleaning with SQL

6- SQL window function

7- SQL performance tuning

Conclusion

This article talks about the importance of SQL for Data Analysis. It gives a brief overview of SQL and the way it facilitates the analysis of data in business processes.