Benefits of SQL in Data Science

Author: Anitha Dv

Introduction

One emerging field in the modern era with ample job opportunities for the youth in data science. Data scientists are required to possess many skills. The foremost and basic skill that needs to be acquired by all data science candidates is SQL. In the present day, almost all companies are driven by data. This data is stored in a large database and processed and managed via a database management system (DBMS). You can make your task more organized using DBMS. So DBMS model needs integration with this popular programming language. SQL is a diverse programming language that is widely used, especially when you have to work with a database. Many relational databases support SQL, such as Oracle, MySQL, SQL Server, etc. SQL is known to be a valuable concept in the data science field as the standard of SQL has certain features that are differently implemented in various types of database system

Need of SQL in Data Science

SQL stands for a structured query language that helps perform a wide variety of operations on different data stored in database systems such as views, updated records, creation of tables, deleting the records, and modification of tables. Many big data platforms use SQL for relational databases as API. Data science is the study of different type of data that needs to be extracted from the database. This is where SQL is required. SQL commands help data scientists query, define, create, control, and manipulate the database. SQL is considered the best choice for in-office operations and business kit intelligence tools in the modern industry. SQL is now a standard for several database systems. Modeling of several database platforms is done after SQL. Spark and Hadoop are some big and modern data systems that processes structured data and maintain the relational database by using SQL. Click here to learn Data Science Training in Bangalore

SQL – Data Science Statistics

The third demanding skill for a data scientist is SQL, which helps data scientists churn raw data and construct valuable insights. It is more popular than Python and R among data engineers and data scientists. It is a language of choice and is known to hold great significance. When there’s structured data in table form, SQL is needed. However, for the not so well relational or structured database, SQL is not used, and so NoSQL database is used there.

Some interesting facts about SQL that should Know

One important fact about SQL is that it contains descriptive words. In easy words, SQL commands are comparatively much easier to understand than other programming languages. This makes this programming language simple to learn and easy to understand. For instance, if you want to choose a column AGE from the PERSON table, then you have to write the SQL command in the following way-

SELECT AGE FROM PERSON; SQL language contains ISO standards. The implementation is not similar for all syntax. You may see that query that may not work in MySQL but works in SQL server. It is a simple, understandable, and non-procedural language with the help of this. You can communicate and interact with data. You may not write a whole application using this language.

Why SQL for Data Science?

Approximately 2.5 quintillion data bytes are produced each day, and hence database is needed to store such huge amounts of data. Direct accessibility is one of the key features of SQL while manipulation is being performed on data. This is one of the important benefits of SQL as it helps in fastening workflow implementation and execution. Beginners need to know the relational model before diving deep into SQL.

Basics Of SQL

SQL provides simple commands to modify/change data tables. Some basic SQL commands are as follows

SELECT – data extraction from database

DROP TABLE – table gets deleted

DELETE- data gets deleted from the database

CREATE DATABASE- a new database is created

CREATE INDEX – an index is created to look for an element

ALTER TABLE- a table is modified

INSERT INTO- new data is inserted into the database

CREATE TABLE – a new table is created

What elements of SQL do data scientists need to know?

Following are the SQL skills that data scientists must know-

  • SQL indexing
  • SubQuery
  • SQL Joins
  • Knowledge about relational database model
  • Primary as well as a foreign key
  • Tables creation as well as retrieval of data from tables
  • Knowledge about SQL commands

What types of SQL databases are best for data science?

There are many relational databases; however, among them, MySql is known to be the famous database for all business organizations. Some also prefer PostgreSQL.

Steps for learning SQL

  • Data understanding

The first and most important step to learning SQL is data understanding. A data science candidate must invest their time in modeling diagrams and knowing about data association since the prime key to writing correct and successful queries is to know about data. It is better to know about data than simply interpreting. You need to know dependencies and all associations of data

  • Business understanding

After familiarizing yourself with data, the next step is to know about a business problem that you have to solve. If you can understand the data and identify the problem, then writing queries will simply fill in the blanks. Understanding a business problem makes you more comfortable in query writing.

  • Profiling data

Profiling data is where data science professionals are required to perform descriptive statistics. This step helps in classifying data quality problems before performing analysis. If obtaining data is a regular phenomenon, then you have to start with a select statement.

  • Start with select

It is important to know that you always have to begin with the SELECT statement. This shows that SQL language is consistent. If you are a beginner, you need to start simply. So start with a single table, include more data, add the next table, check the outcome, and go back then. While using queries, it is always important to start with inner queries before building.

  • Test and troubleshoot

The query must be tested. If you have to write an assumption related to the average selling price, then search the number of values you get back for the calculation from that table. You have to combine the outcome with different tables and then carefully examine. Ensure accuracy in the order of the manipulation. Troubleshooting helps to begin short as well as simple. It is important for reconstructing the query to check where things have gone wrong.

  • Format & Comment

The most important thing to consider while query writing is to ensure that you format it correctly and comment accurately. To ensure that the query is easy to read, use comments wherever needed and recommended indentation. Keeping the code quite clean and strategically formatting the comments wherever required is important.

SQL Queries

There are five parts of SQL queries in query execution on any RDBMS system. They are as follows-

  • DDL (Data definition language)

It contains commands that handle the database structures like alter, truncate, create, drop, rename.

  • DML (Data Manipulation Language)

It includes commands for doing operations like delete, insert and update to change the existing data in databases.

  • DQL (Data Query language)

It includes select operation that matches the specification of users by retrieving the data and contains nested queries.

  • DCL (Data control language)

Data administrators use this command for revoking and granting permission for data accessing in the organization's database.

  • TCL (Transaction Control Language)

Transaction present in the database can be effectively managed with this command. It is used for performing DML operations and helps in clubbing multiple commands in one operation.

SQL Views and Stored Procedures

Virtual tables that help optimize the database and come from the existing table are SQL views. It enhances security by limiting users from obtaining all the database information. Data science needs to make continuous processes for making reports, and stored procedures help overcome this problem. DML operations are processed and created on the database using the stored procedure and perform SQL commands by taking user input. Click here to learn Data Science Course in Bangalore

SQL Joins

Different tables are combined in the database using SQL join clause where with the help of foreign key and primary key JOIN is made. The four joins combined with the ‘from' clause is full, inner, right, and left.

SQL aggression

The main aim of data science is to get meaningful insight, and SQL aggression query helps to perform a combination of several entities. A deterministic function that helps calculate a set of values is aggression, which gives a single entity. The SQL aggression function helps extract insights from days because it takes place on several rows. Some standard function of SQL is min, count, avg, sum, and max operation. Click here to learn Data Science Course in Bangalore with Placement

Benefits of SQL for Data Science

  • SQL for data science is a user-friendly language and helps users to learn and understand easily.
  • SQL for data science helps to retrieve big data from several databases quite effectively, and so it is quite efficient at quick query processing.
  • SQL supports exceptional handling since it offers users standard documentation.