SQL for Data Science: Why SQL, List of Benefits & Commands

Introduction to Data Science

Data today is the crux of every single process, from businesses to process flows. Each day large measures of structured and unstructured data are produced. Data Science enters the field here. It is a multi-disciplinary domain that includes statistical and mathematical functions to reason every single piece of information.

The data in hand is from several sub-domains, each relating to a broader set of problem areas and functions. This data, although available, needs to be solved to interpret what it implies. Data science penetrates the problem areas for business by obtaining them in the first place. The methods in the process include detecting the untapped difficulty areas and then finding solutions to the ones that will help improve the business.

By deriving all the knowledgeable insights from the data available, you can find solutions to critical problems and help advance your business. It covers Artificial Intelligence, Machine Learning as well as Natural Programming.

What is SQL?

SQL is a querying language that aims to manage a relational database. Relational databases are a compilation of structured tables from which data can be retrieved, modified, and restructured. The functionality of relational databases that allows users not necessarily to alter the tables in the databases is proven advantageous. SQL is one of the important technical skill to have if you want to master data science.

SQL is a standard API for the relational databases. The programming in SQL is helpful in a wide array of activities that include questioning, including updating and eliminating data. All of which form the critical steps to the final analysis results in the data science purpose. Its numerous data types cover integers and floating points of varied kinds and accuracies.

SQL is hence deployed usefully to manipulate and analyze the data in specific methods aiming to derive useful results. Examples of databases that use SQL include MySQL, Oracle, SQLite, etc. Learn more how SQL is a must tool for Big data engineers.

Why is SQL needed for data science?

The concept underlying data science is the uprooting, processing, and interpretation of the massive amount of data produced. The following step is to procure useful insights from it. The need of the hour is tools to use to store and manage this substantial, comprehensive measure of data.

This is where SQL comes in. SQL or Structured Query Language is a querying language. As a computer programming language, it is applied to collect, manage, and recover the data that is stored in the database. It is used to perform a lot of querying operations, research developments, extractions, editing, and transforming the data.

Read: Top 9 Data Science Tools in 2020

For the accurate processing of data, we require a smooth management system to design the individual steps in handling and a language that will allow us to present the methods that we need while working with our data.

Must Read: SQL Interview Questions. 

Which attributes favour SQL for Data Science?

Several characteristics of SQL make it suitable for the detailed interpretation and analysis purpose after data extraction in data science. The different attributes of SQL for data science include:

1. It is an easy tool with a set of commands and data types which once understood, become seamless to operate. The primary objective is to extract data from larger chunks of files from the database. MySQL is recognized as one of the most basic and understandable languages used in querying language to communicate the best with the data repository. 

2. Apart from the ease of functioning, the SQL platform provides security to your data. MySQL has a robust data security layer that takes the delicacy and confidentiality of your data into account. The password encryption feature of the SQL platform makes it protected and blocks invasion of all kinds.

3. MySQL is an open-source type that allows you to download the application free of cost from anywhere, only by visiting their official website. The download gets completed in a few minutes by speed offered.

4.  Massive capacity to handle data. SQL databases are repositories that can hold millions of rows and columns of data in them. 

5. MySQL trails a client-server architecture. In this, MySQL acts as a database, and the various applications function as clients, which will then communicate with the server. In the communication channel, data is shared, changes are saved and updated as well.

6. SQL platforms are agreeable with almost every operating system. Simple to run on Windows, Linux, or Unix, the SQL, is composed of numerous APIs and libraries, helps to develop MySQL applications. Adopting languages such as C, C++, Java, Python, etc. you can program the data with other clients on a local network or through the internet. The combination of Python and MySQL is considered useful across all systems.

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7. The customizable property of MySQL is beneficial to making it platform-independent. MySQL, along with client applications, has the liberty to operate under various operating systems.

8. The high-speed operating tool of MySQL makes it considerably a secure database operating program. Being backed up by numerous benchmark tests, it allows the developer to construct high productivity by using triggers and reserved procedures.

SQL commands

To functionally operate the tool, following are the important commands that are essential in SQL for Data Science:

1. The first command is SQL is CREATE DATABASE. As the name suggests, this command creates a database for you.

Syntax:

CREATE DATABASE name;

USE name;

  • The semicolon acts as a terminator here.
  • The USE command activates the database that has been created.
  • Writing the commands in capital letter will help you distinguish the command from the name of table of values

 2. The second command is the CREATE TABLE. This is considered one of the primary commands to set the data correctly for analysis in data science. It can contain a lot of data variables of different data types.

 Syntax:

CREATE TABLE name (variable1 data_type1, variable2 data_type2);

  • This function will create the table as essential.

3. The third command here is INSERT INTO. This command is used to insert new command into your table.

Syntax:

INSERT INTO name VALUES (value1, value2, value3…..);

  • The values that are included must arrange with the assigned data types.

4. The next command is SELECT. This is considered one of the most important commands in SQL for data science. The reason for its high implication is that it is used to extract the particular set of data that is required from the database. It picks a defined column/table and obtains the demanded data.

Syntax:

SELECT*FROM table_name

  • The command can be adjusted as per utility.

5. Following SELECT is the UPDATE command. This will allow modification of any value that is stored in your table. The WHERE command will select the exact data that you intend to modify.

Syntax:

Update table_name SET variable1=’’ WHERE condition;

6. The DELETE command follows the UPDATE. As the name suggests, it will delete the data from your dataset.

Syntax:

DELETE FROM table WHERE condition;

  • The WHERE command will help you define a condition following the delete command to delete the data from the desired data set.

7. The DROP TABLE command functions to delete all the contents of a specified table.

Syntax:

DROP TABLE table_name;

Conclusion

Data Science uses tools to derive, mine, and analyze data to solve business problems. The handling and perception of individual units from the considerable volume of data demand a blend of skills and technology power. 

SQL is a querying language tool that aims to manipulate and handle relational databases to manage and analyze the data in specific methods- seeking to derive useful results. It is a smooth management system aimed at simplifying the strenuous process of extracting data from the massive pile of databases by acting as a language communicator between the human operating the collection and the computer system carrying the load. The commands are the language inputs that the other end of the software understands.

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