Role of SQL in Data Science
We all know data science has been stood no. 1 for being the most assuring job of the time. So, we are willing to learn Data Science. You must be aware of the top skills needed for Data Science. Are you worried about where to start? The easiest and most essential skill that you can learn is SQL. Before working on this skill, you should understand the role of SQL in data science. Why every Data Science expert considers SQL as an essential element for data scientists. So, let’s see how SQL is important for data science.
Why learn SQL For Data Science?
Do you know that we produce more than 2.5 quintillion bytes of data every day? Yes, this huge amount of data generation resulted in the popularity of advanced technologies like Data Science, Artificial Intelligence, Machine Learning, and so on.
Data science is referred to as extracting useful insights from the given data. Data Science includes the extraction, processing, and analysis of lots of data. Currently, we need the tools to store and handle this huge amount of data. SQL is used to store, access, and extract huge amounts of data. It helps in carrying out the complete Data Science process smoothly. Various database platforms are modeled using SQL. It has become a standard for several database systems. The point to ponder is modern big data systems like Hadoop, Spark uses SQL for maintaining relational database systems, and processing the data. Hadoop provides features for batch SQL, Impala, and Apache Drill resulting in interactive query skills.
So, we covered the need for SQL for data science. Now, let me tell you about SQL and basic concepts related to this.
What is SQL?
SQL stands for Structured Query Language. It is a querying language proposed to handle Relational Databases. It is a powerful programming language used for adding, deleting, and extracting information within a relational database. We can use SQL to perform complicated analytical functions too.
Hey wait!, what is a Relational Database?
A relational database is a collection of well-defined relations(Tables) from which data can be accessed, edited, updated, and so on, without having to alter the database tables. SQL is the standard for relational databases.
Relational databases using SQL are MySQL Database, Oracle, etc.
SQL Skills required for Data Science
A Data Scientist must be aware of the given SQL skills :
Relational Database Model
A Relational Database Model System (RDBMS) is the principal and foremost necessary concept for a Data Scientist. You must grasp RDBMS in-depth to store and retrieve structured data. You can also access, retrieve, and manipulate the data using SQL. An RDBMS is a standard for all the data platforms. The advanced big data platforms also consist of an RDBMS segment for treating structured information.
Understanding of the SQL commands
A Data Scientist should know given SQL commands –
- Data Query Language: DQL is used for extracting data from the database. Data query language uses one commend only i.e., Select.
- Data Manipulation Language: DML commands are used for modifying the database. It is efficient for all kinds of changes in the database. DML commands are not auto-committed. So, it can’t permanently keep all the changes in the database. They can be revert back.
Some commands that are under DML given below:
- Data Definition Language – DDL modifies the structure of the table like making a table, removing a table, modifying a table, etc. All the commands of DDL are auto-committed. Commands that come under DDL are::
- Data Control Language- DCL commands are used for granting and revoking back authority from the database user. Commands that come under DCL are:
Indexes are schema objects. They help the server in speeding up the retrieval of rows. Indexes use a rapid path access method for locating the data quickly. Data can be quickly loaded into the database using SQL indexing.
Join is a very essential concept of a relational database. A data scientist should be aware of these concepts. We have two types of joins in RDBMS – Inner Join and Outer Join. Joins can be further divided as Inner, Left, Right, Full, etc.
Concept of Keys
KEYS helps you to uniquely identify a tuple in a table. They let you determine the relationship between the given tables. Key is also necessary for finding unique records or rows in a table. The two most important keys in a database are – primary key and foreign key.
A query that is embedded in another query is known as a subquery. The most important subqueries in SQL are – SELECT, INSERT, UPDATE, and DELETE. It provides the information back to the primary query.
Data Science uses structured relational tables, and hence, it is essential to know how to create tables in SQL.
From the above discussion, we concluded that:
- A Data Scientist requires SQL to manage structured data. This data is stored in relational databases. To query these databases, a data scientist should have a deep knowledge of SQL.
- Data scientists use SQL as their standard tool.
- We need SQL to carry out data analytics with the data stored in relational databases like Oracle, Microsoft SQL, MySQL.
- SQL is also necessary for data wrangling and preparation. Therefore, we use SQL when dealing with various Big Data tools.