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SQL for Data Science

SQL for Data Science:

We all know the importance of SQL in various aspects. This article highlights the importance of SQL for Data Science. Let’s learn about the connection between SQL and Data Science and how it works.

Due to the increase in the amount of data being collected, people who can work effectively with data are becoming increasingly necessary. Companies need professionals who perform critical thinking and make insight-driven decisions for maximum profits.

What is SQL?

“SQL” stands for Structured Query Language, a specially designed programming language for managing data in relational databases. Along with database management systems, SQL is used in various applications, data warehouses, and e-commerce and is most commonly useful for web servers.

  • This is used to perform various operations, such as storing, accessing, and extracting large amounts of data.
  • It is a querying language used to manage a relational database.
  • SQL controls the data stored in the database and retrieves only specific data as per the requirement.

What is a Relational Database?

A relational database contains a group of well-defined tables. These tables help the data to perform multiple operations. This database is useful to edit, access, update, delete, etc., the data. SQL acts as a standard API for a relational database.

Importance of SQL?

Databases are the basis of everything we do in this technological age. According to a recent study, most Data Scientist jobs are only available for SQL database professionals.  According to LinkedIn statistics, the most widely used skill in big companies and start-ups is SQL in India and worldwide. The role of SQL in data science will remain significant as long as there is ‘data’ involved.

What is Data Science?

Data Science is the study of all parts of data. It is an approach to data analysis that uses ideas and methods from math, statistics, computer science, artificial intelligence, and other fields. This method gives you information to help you make better decisions.

Data scientists should understand the Relational Database Management System. Using SQL commands, a Data Scientist can control, define, manipulate, create, and query a database. Many everyday activities use data science, such as Siri and Alexa commands or making complex applications like self-driving cars. This analysis helps data scientists ask and answer questions such as what and why it happened and the solutions.

Importance of Data Science:

Data Science combines methods, tools, and technology to generate the appropriate data. Data Scientists select the most suitable combinations for faster and more accurate results based on the problem. It is widely used in business to:

  • Identify unknown transformative patterns
  • Develop new products and solutions
  • Real-time data optimizations

Different types of Data Science Technologies:

Expert data scientists work on multiple complex technologies, such as

  • Artificial Intelligence
  • Cloud computing
  • IoT- Internet of Things
  • Quantum Computing and many more.

Why SQL is needed for Data Science?

Did you know that we generate over 2.5 quintillion bytes of data every day? The rapid generation of data is the driving force behind the popularity of cutting-edge technologies such as Data Science, Machine Learning, Artificial Intelligence, and so on.

The goal of Data Science is to study and analyze data. To analyze the data, the data should be extracted. SQL plays a key role where data is concerned. This makes the whole data science process run smoothly.

SQL is a powerful database programming language. This enables you to manipulate and query data stored in databases easily. With SQL, you can update records, delete records, create and modify tables, views, etc. Many big data platforms use SQL as their API key for their relational databases.

Most of the popular database platforms are modeled after SQL. The following steps give a brief understanding of SQL and its importance:

  • It is easy to understand and quick to learn.
  • SQL allows data to be accessed directly from the stored database without requiring it to be copied into other applications.
  • Compared to other spreadsheet tools, SQL data analysis is simple to audit and replicate.
  • Using SQL as the standard tool, various data experiments are performed to create a test environment.
  • It is mainly used to perform analytical operations on the stored data present in relational databases such as Oracle, MySQL, Microsoft SQL, etc.
  • SQL also plays a prominent role in some of the essential tools such as data preparation, data wrangling, etc. Further BigData tools can be developed with this.

How many SQL Skills are required for Data Science?

Aspiring Data Scientists must have the following SQL skills to gain proficiency and ability.

  1. Relational Database Management System (RDBMS):

RDBMS is required for data platforms. Even the most sophisticated big data platforms have an RDBMS section. RDBMS is needed to store structured data. Then only, SQL can access, retrieve, and manipulate data.

  1. SQL Commands:
    Every Data Scientist should have a deep understanding of the below-listed SQL commands:

    • Data Query Language (DQL): It fetches data from the database using only the “SELECT command.”
    • Data Definition Language (DDL): It is used to manipulate or update a database structure. The commands include CREATE, ALTER, DROP, and RENAME.
  • Data Control Language (DCL): It is used to configure database privilege and permission parameters. It contains commands such as GRANT and REVOKE.
  • Data Manipulate Language (DML): It enables alterations to the database. It uses commands such as INSERT, UPDATE, and DELETE.
  • Transaction control language (TCL): It is used to manage DML-generated changes. It permits the grouping of these changes into logical transactions. The available commands are COMMIT, ROLLBACK, and SAVEPOINT. 
  1. Null Value:
    Null is used to show that a value is missing. In a table, a field with a “Null” value is empty. But a Null value is not the same as a value of 0 or a field with blank spaces.

SELECT column_names
FROM table_name
WHERE column_name IS NULL;

  1. Indexes:

Lookup tables make it easy for a database search engine to find values in a row. With SQL indexing, data can be quickly added to a database.
CREATE INDEX index_name
ON table_name (column1, column2, …);

  1. SQL Joins:

A data scientist must know table joins to use relational databases. Two types of joins are Inner Join and Outer Join. They’re then divided into 4 types:

  • SQL Inner Join or Join:

SELECT columns
FROM table1
ON table1.column = table2.column;

  • SQL Left Outer Join or Left Join:

SELECT columns
FROM table1
ON table1.column = table2.column;

  • SQL Right Outer Join or Outer Join:

SELECT columns
FROM table1
ON table1.column = table2.column;

  • SQL Full Outer Join or Full Join

SELECT columns
FROM table1
ON table1.column = table2.column;

  1. Primary & Foreign Key:

A database’s primary key is unique. With a primary key, we can identify each database line and record it apart. Foreign Keys connect two tables.
CREATE TABLE table_name
column1 datatype [ NULL | NOT NULL ],
column2 datatype [ NULL | NOT NULL ],

CONSTRAINT constraint_name PRIMARY KEY (pk_col1, pk_col2, … pk_col_n)
CREATE TABLE table_name
column1 datatype CONSTRAINT constraint_name PRIMARY KEY,
column2 datatype [ NULL | NOT NULL ],


  1. Sub Query:

A nested query that is part of another query is called a subquery. In SQL, the SELECT, INSERT, UPDATE, and DELETE subqueries are the most important ones. It will send the data back to the primary query.
SELECT column_name
FROM table_name
WHERE column_name expression operator

Different types of SQL Database for Data Science:

There are a variety of SQL-compatible database management systems. The most well-known open-source SQL databases are included in the list below:

  1. Microsoft SQL Server:

Microsoft SQL Server is a reliable and high-performing data management system. Azure and Microsoft BI products are compatible. It efficiently stores, retrieves, and analyses data. SQL Server’s powerful tools and features make managing large data warehouses and BI apps easy. This database is for big data researchers. It queries large datasets quickly.

  1. MySQL:

MySQL is a versatile and reliable open-source SQL database. This offers many benefits for businesses and individuals. It is compatible with a variety of operating systems. This makes it a popular choice for those who need a powerful yet affordable database solution. MySQL’s robust features and scalability make it ideal for businesses of all sizes, from small start-ups to large enterprises.

  1. SQLite:

SQL lite is used by mobile app and phone developers. SQLite is a powerful SQL database that doesn’t need a server. This makes data migration easy. SQLite is fast, efficient, and easy to use, making it ideal for data scientists with large datasets.

  1. IBM Db2 Database:

IBM’s database services and programmes are respected in RDBMS. They offer many benefits, making them a good choice for all businesses. IBM provides secure and reliable database services. They’re scalable, so a growing business can use them. Db2 databases are reliable and secure. Db2 offers many platforms and editions for your needs. You can trust your data to safety-focused services.

  1. PostgreSQL:

PostgreSQL is a relational database system and another open-source SQL database. It is renowned for its high level of performance and ability to handle large data stores. Also, to be flexible and scalable, this database can also be programmed in a variety of programming languages, including Python. It is perfect for managing both structured and unstructured data.

Which Data Science jobs require SQL?

SQL is an invaluable skill for data science professionals. It is a vital tool for managing, manipulating, and extracting data from databases. SQL is easy to learn and use and a powerful tool for data analysis. The most famous Data Science jobs that need SQL are:

  • Data Analyst
  • Business Intelligence Developer
  • Data Engineer
  • Data Architect and
  • Software Engineer.

Finally, we conclude that SQL plays a critical role in Data Science. Nowadays, big data platforms emulate SQL. It will process structured and unstructured data together. In addition, I hope we gain a deeper understanding of the various SQL skills needed in Data Science.



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