T-SQL Tutorial

Everything You Need To Master in SQL For Data Science

SQL (Structured Query Language) is a popular and frequently used programming language for managing and analysing relational databases. It is an useful tool for data scientists that need to deal with enormous data sets, and it is utilised in a variety of applications, including corporate intelligence and financial analysis, as well as scientific research and social media analysis.

Data input, queries, updating and deleting, schema design and change, and data access control are all things that SQL can perform, but writing your own SQL code is more efficient and can result in more easily reproducible scripts. Learning SQL can also provide you an advantage over people transitioning from academia to the data science industry, who usually lack database knowledge.

In this article, we will look at the necessary SQL abilities and principles for data science, as well as some pointers on how to become adept in this powerful language.

Important SQL topics for Data Science

Before we go into the materials, let’s go through the main themes. Make sure you address the following topics, but don’t limit yourself to just those.

Learning the Fundamentals of SQL

Understanding the fundamentals of SQL is the first step towards mastery. SQL is a declarative language, which means you tell it what you want it to do rather than how. In other words, you provide the desired outcome, and SQL determines how to get there.

SQL is used to administer relational databases, which are composed of tables that hold data in rows and columns. Each table represents a collection of connected data, and each row in the table represents a single record. Names, addresses, and dates are examples of data that may be found in columns.

SQL allows you to modify this data using operations like SELECT, INSERT, UPDATE, and DELETE. Each of these instructions operates on the data in a specific way, such as obtaining data from a database, putting new data into a table, updating existing data, or removing data from a table.

SELECT Statement Data Retrieval

The SELECT statement is one of the most significant and often used SQL statements. It is used to generate queries that return specified sets of data and lets you to access data from one or more tables in a database.

A SELECT statement has the following fundamental syntax:

SELECT column5, column6, ...
FROM table
WHERE condition;

The SELECT statement provides the columns to obtain from the table, whereas the FROM clause indicates which table or tables to retrieve data from. The WHERE clause specifies a criteria that filters the query’s results.

In addition to the fundamental syntax, there are several sophisticated capabilities and strategies for retrieving and manipulating data in SQL. They include the use of aggregate functions, the merging of numerous tables, the use of subqueries, and the handling of complicated data types.

INSERT, UPDATE, and DELETE Statements for Data Manipulation

SQL allows you to change data in a number of ways in addition to accessing it. The INSERT command adds new data to a table, whereas the UPDATE statement modifies existing data. To delete data from a table, use the DELETE statement.

An INSERT statement has the following basic syntax:

INSERT INTO table (column5, column6, ...)
VALUES (value5, value6, ...);

The INSERT statement defines the table to which data will be added, as well as the columns and values to be inserted.

An UPDATE statement has the following basic syntax:

UPDATE table
SET column5 = value5, column6 = value6, ...
WHERE condition;

The UPDATE statement is an SQL statement defines the table and columns to be modified, as well as the new values to be assigned to those columns.

A DELETE statement has the following basic syntax:

WHERE condition;

The DELETE statement defines the table from which you wish to delete data as well as the criterion that determines which rows should be eliminated.

Data Joins

A relational database’s data is frequently split over numerous tables. SQL commands are available for joining data from several tables. INNER JOIN, LEFT JOIN, and RIGHT JOIN are the three most prevalent forms of joins. INNER JOIN returns just rows with matching values in both tables, whereas LEFT JOIN delivers all rows from the left table as well as matching rows from the right table. RIGHT JOIN retrieves all rows from the right table as well as any matching rows from the left table.


Subqueries are queries that are contained within larger queries. They can be used to filter data or to compute on subsets of data. Subqueries can be used in a variety of situations, including the WHERE clause of a SELECT statement and the HAVING clause of a GROUP BY query.


Indexes are data structures that help SQL queries run faster. They enable SQL to obtain data from a database rapidly without having to scan the full table. Indexes are established on one or more table columns and are used to speed up queries that filter, sort, or group data. Knowing how indexes operate and how to construct them is critical for boosting SQL query performance.


Views are virtual tables that are produced in response to a query result. Views are useful for simplifying complex searches, protecting sensitive data, and providing a consistent presentation of the data. Views are frequently used in data science to offer analysts and other stakeholders with a simplified picture of the data.

Stored Procedures

Stored procedures are SQL statements that have been precompiled and may be used to conduct sophisticated actions on a database. They may be used to automate repetitive processes, enforce business rules, and compute on enormous datasets. Stored procedures are very helpful for data scientists that work with huge datasets or sophisticated data structures.


Transactions are groups of SQL statements that are run as a single unit of work. Transactions can be used to guarantee that various database operations are done consistently and reliably. Transactions are used in SQL to ensure that a sequence of statements is executed as a single atomic operation. If one of the statements fails, the entire transaction is reverted, and the database is reset to its former state.

Performance Enhancement

SQL performance optimization is a critical part of data science. SQL searches may be time-consuming and resource-intensive, particularly when dealing with huge datasets. SQL query optimization may increase the efficiency of data science applications while also reducing the time and cost of data processing. Several approaches exist for optimising SQL queries, including the use of indexes, streamlining data retrieval, and decreasing the number of joins.

Normalization of Data

Data normalisation is a method of organising data in a database in order to eliminate redundancy and increase data consistency. Normalized data is structured into tables that adhere to a set of standards that assure data storage efficiency and the avoidance of data abnormalities. Normalizing data can enhance the performance of SQL queries and lower the likelihood of data analysis mistakes.

Databases that do not use SQL

While SQL is a necessary skill for data science, other types of databases, such as NoSQL databases, are also employed in data science. NoSQL databases are built to manage unstructured and semi-structured data, both of which are frequent in big data applications. Understanding NoSQL databases and how to utilise them is a must for data scientists working with `big data applications.

SQL Advanced Functions

In addition to the core SQL functions stated above, there are other additional SQL features available for data manipulation and analysis. Stored procedures, triggers, and user-defined functions are among the capabilities available. Stored procedures are SQL statements that have been precompiled and may be used to automate complicated activities, whereas triggers are used to execute SQL statements when certain events occur. Custom functions that may be used in SQL queries to execute complicated computations and data transformations are known as user-defined functions.

What role does SQL play in data science?

Data science is the analysis and study of data. We must first extract the data from the database before we can study it. This is where SQL comes in. We research and evaluate the supplied data with the aid of data science. The initial stage in carrying out this analytical technique is to extract data from the database. We require SQL in order to accomplish this.

Additionally, SQL serves as the foundational language for many data platforms that mimic relational database structures. Many NoSQL queries are designed after the declarative syntax of SQL, including those in Cassandra, MongoDB, and Redis.

SQL (Structured Query Language) is a standard language for working with relational databases and so plays a major role in data science. These are some examples of how SQL is used in data science:

Data retrieval: SQL is used to retrieve information from databases. Data scientists frequently work with enormous datasets, and SQL offers a useful tool for filtering, sorting, and aggregating data.

Data manipulation: SQL has a number of commands for manipulating data. SQL is used by data scientists to alter data by building new tables, updating old tables, and eliminating data that is no longer required.

Data analysis: SQL is frequently used to do basic data analysis, such as computing averages, counting occurrences of specified values, and categorising data based on specific criteria.

Integration with other tools: Several data science tools, like Python and R, may be used with SQL to give a more comprehensive data analysis environment. This enables data scientists to leverage the skills of numerous technologies to get the most value from their data.

Overall, SQL is a valuable tool for data scientists because it allows them to access, alter, and analyze massive volumes of data. By enrolling in a comprehensive data science course, such as Scaler's Data Science program, aspiring data scientists can master SQL and leverage its capabilities to extract meaningful insights from complex datasets. The course provides hands-on training and practical examples, equipping students with the skills necessary to handle and manipulate data efficiently, making them well-rounded data science professionals.

Platforms for SQL Query Practice

There are several online sites for practicing SQL queries. These are some popular choices:

1) HackerRank
HackerRank is a well-known tool for honing coding abilities, especially SQL queries. It provides a variety of challenges and tournaments for users to put their talents to the test.

2) LeetCode
This platform focuses mostly on coding difficulties, although it also includes SQL exercises and issues.

Codecademy is an online learning platform that provides a range of coding classes, including SQL. It offers customers interactive courses and tasks to practise.

3) Master SQL For Data Science on Udemy
You will get the knowledge and abilities necessary to draw important conclusions from database-stored data in this course. There are several opportunities for practise throughout the course’s more than 100 puzzles, all of which provide in-depth solutions.

4) SQL Bolt
It is simply a set of interactive lectures and activities designed to help people learn SQL quickly. The training and topics on this site are extensive and cover all of the major aspects of using SQL.

5) Mode Analytics
This platform is particularly built for data analysts and includes a SQL training as well as a sandbox environment for performing SQL queries.

6) DataCamp
This platform provides a variety of data science and data analysis courses, including SQL. It allows users to develop their coding abilities through hands-on activities and interactive coding challenges.

All of these platforms include varying levels of difficulty and workout kinds, allowing you to select the one that best matches your level of skill and learning style.

Six Useful SQL Functions for Data Scientists

SQL includes a wide range of functions that data scientists may employ. Below are the six most often used SQL functions for data analysis:

  • COUNT(): This function counts the number of rows in a table or the number of rows that satisfy a specified criterion. COUNT(), for example, may be used to determine the number of customers in a sales database.
  • AVG(): This function computes the average value of a numeric column. For example, you can use AVG() to get the average income of a company’s employees.
  • SUM(): This function computes the sum of values in a numeric column. SUM(), for example, may be used to calculate a company’s total revenue.
  • MAX() and MIN(): MAX() and MIN() are methods that are used to find the highest and lowest values in a column. MAX() and MIN(), for example, may be used to identify the top and lowest sales statistics for a product.
  • CONCAT(): CONCAT() is a function that joins two or more strings together. For example, you may use CONCAT() to combine a customer’s initial and last name.
  • DATE functions: SQL also has a number of functions for working with dates and times, including DATE(), MONTH(), YEAR(), DAY(), and many more. You may use these functions to retrieve and alter date and time data from your tables.

Data scientists may execute a broad range of data analysis activities using these and other SQL methods, from simple aggregations and computations to more complicated data transformations and feature engineering.


SQL is a necessary skill for data scientists, and learning SQL is critical for anybody who works with data. Understanding the fundamental syntax of SQL, as well as data retrieval, modification, joins, subqueries, indexes, views, stored procedures, transactions, performance optimization, data normalisation, NoSQL databases, and advanced SQL features, is critical for data scientists to be effective in their work. Data scientists with these talents can swiftly and effectively extract insights from huge and complicated information, allowing them to make educated decisions and provide important insights for their businesses.