Become Familiar with SQL For Data Science

Posted by vinod on August 16th, 2022

Introduction

There are many work prospects in the rapidly developing field of data science. The best data science talents must have been widely publicized. The first and most important skill that every aspirant to a career in data science should learn is SQL.

Most businesses these days are moving toward being data-driven. These data are handled and processed by a database management system and are kept in a database. Our work is so structured and straightforward, thanks to DBMS. The most widely used programming language must be integrated with the outstanding DBMS technology.

With relational database systems like MySQL, SQL Server, and Oracle, SQL is the most popular programming language for working with databases. However, many database management systems implement some of the SQL standard's capabilities differently. So, one of the essential ideas in this area of data science to understand is SQL.

What is SQL?

Structured Query Language (SQL), a standard query language created for maintaining and querying relational databases, goes by the name SQL. With this, you can create, support, and retrieve data from a relational database. With the aid of several straightforward commands, you can enter, update, remove, change, and retrieve data with SQL.

Numerous databases are accessible, including SQLite, MySQL, Oracle, Microsoft SQL Server, etc. Depending on the needs of the data, each of them works best in various situations.

SQL Skills required for Data Science

1. Knowledge of Relational Database Model

A prospective data scientist's fundamental and most crucial idea is a relational database model system (RDBMS). You need to be well-versed in RDBMS to store structured data. SQL can then be used to access, retrieve, and modify the data.

Every data platform must use an RDBMS as a minimum. Even the most sophisticated big data solutions have an RDBMS portion for handling structured data.

To master data science, one must master all of these SQL tools. Our SQL data science course  will teach you more about them.

2. Basic SQL Commands

The following SQL instructions are necessary for a data scientist.

i) DDL (Data Definition Language)

The SQL statements used to define the structure of the database are known as DDL statements. Creating and altering database objects allows you to manage the database schema using the various DDL procedures.

The DDL commands are :

• Create – This command facilitates the creation of new databases and database objects such as tables.

• Alter – The database structure can be changed using the alter command. You can add, remove, drop, and modify a database table's columns with the alter command.

• Drop –You can eliminate the database objects using the drop command. The user-specified object or the database's complete structure is removed.

• Truncate – You can delete every row or record from a table with the truncate command.

• Rename –You can rename an existing database object with the rename command.

• Comment –The data dictionary's metadata for the database is added using the comment statements.

ii)  DQL (Data Query Language)

The group of SQL commands utilized to get data from the database falls under the DQL command category.

The DQL command consists of:

Select –Data from a database is retrieved using the various select statements.

iii)  DML (Data Manipulation Language)

DML statements are a subset of the SQL commands used for data manipulation.

• Insert – You can update the database by using the insert command.

• Update – You can update the database's current data with the update statement.

• Delete –A table's records or rows can be deleted using delete statements.

iv) DCL (Data Control Language)

The DCL commands deal with tasks pertaining to rights, permissions, and database system control. The DCL instructions are:

• Grant – The grant command grants a user access to the database.

• Revoke – The grant command's access rights are revoked with the revoke command.

3. Null Value

Null is the symbol for a value that is missing. A field in a table with a Null value is empty. A Null value is distinct from a zero value or a domain with empty spaces, though.

4. Indexes

An easy way for a database search engine to find values in a row is to use customized lookup tables. We can fast load the data into the database using SQL indexing.

5. Joins

Table joins are the most crucial relational database fundamentals a data scientist must understand. Both inner and outer joins are different types of joins. Then they are separated further into Inner, Left, Right, Full, etc.

6. Primary and Foreign Key

Every row in a table can be uniquely identified by using the primary key, which can be a single column or a collection of columns. A foreign key is a column or group of columns that serve as a bridge between two different tables.

7. SubQuery

The nested query included within another query is referred to as a subquery. SELECT, INSERT, UPDATE, and DELETE are the four crucial subqueries in SQL. To the original query, it will return the data.

  Summary

Understanding and effectively managing your data will enable you to make better data-driven decisions, and this is something that SQL will help you with. After reading this article, you now better understand the value of SQL for data science and how to get started with data science by studying SQL. To become an expert in SQL, check out this data science course  in Canada offered by Learnbay. Become an SQL pro and ace the data science interviews.