SQL for Data Science (2024)

Last Updated : 26 May, 2024

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SQL for Data Science: In the ever-evolving world of data science, mastering SQL (Structured Query Language) has become a fundamental necessity. As the most important part of data manipulation and analysis, SQL empowers data scientists to query and handle vast datasets efficiently.

Since Data Science is the Most In-Demand Profession in IT, a majority of companies are moving towards a data-centric approach. Learning Data Science with SQL can be the right move for your career.

This data is stored in a database and managed and processed through a Database Management System (DBMS), which simplifies and organizes our work. SQL is a fundamental tool in data management used in DBMS. It plays a vital role in the data science workflow, enabling professionals to extract valuable insights from large, intricate datasets.

In this article, we will go through the complete curriculum of SQL that a Data Science student or professional should learn to excel in this field.

What is SQL

SQL is a standard database language used to communicate with databases. It allows easy access to the database and is used to manipulate database data.

SQL stands for Structured Query Language. It was developed by IBM in the 1970s. By executing queries, SQL can create, update, delete, and retrieve data in databases like MySQL, Oracle, PostgreSQL, etc.

Need of SQL in Data Science

SQL is a fundamental tool in Data Science, essential for storing and managing data, making it a foundational skill. Proficiency in SQL is a prerequisite for any data science project, as it is the backbone of data management and analysis.

Reasons to Learn SQL for Data Science

SQL (Structured Query Language) is used to manipulate data. By performing different operations on the data stored in databases, such as updating, removing, creating and altering tables, views, etc.
Using SQL as the primary API for relational databases by big data platforms and organisations is standard.
Data science is the study of data in its entirety. We must extract data from the database in order to work with it and SQL helps us do that.
A key component of data science is relational database management. A data scientist can define, define, create, and query the database using SQL commands.
Many different industries and organisations have used NoSQL to manage their product data, yet SQL is still the best choice for many.

SQL Skills for Data Science

Following are the key topics and skills that you will learn in this tutorial on SQL for Data Science. We have studied industry trends and listed the most important skills you need to learn in SQL for Data Science.

Relational Database Model
SQL Query Commands
Handling Null Values
Joins
Key Constraints
Working with SubQuery
Creating Tables and Databases

Let’s discuss the syllabus of SQL for Data Science. We have provided the list of chapter along with the material on specific topics to provide a systematic and efficient learning Experience.

SQL For Data Science Page Index

Here is the list of chapters and important concepts that will be taught in this tutorial. This SQL syllabus covers all important concepts of SQL required in Data Science.

Introduction to SQL:

What is SQL?
Why is SQL important for data science?
Common database management systems (DBMS) that use SQL (e.g., MySQL, PostgreSQL, SQLite).
Different Datatypes used in SQL
Different Types of SQL Queries:- DDL, DML, DCL, and DCL

Setting Up the Environment:

Installing a DBMS (e.g., MySQL, PostgreSQL).
Connecting to the database.
Creating a sample database.

SQL Basics:

Introduction to relational databases and tables.
Basic SQL syntax: SELECT, FROM, WHERE, ORDER BY, LIMIT.
Retrieving data using SELECT statements.
Filtering data using WHERE clause.
Sorting data using ORDER BY.
Use of WITH clause to name Sub-Query
Grouping similar data using GROUP BY
Limiting the number of rows returned using LIMIT.
How to LIMIT the number of data points in output.
Avoid duplicates using Distinct Clause
SQL Operators
- Arithmetic Operators in SQL
- Wildcard operators in SQL
- AND and OR operators in SQL
- Alternative Quote Operator in SQL
- Concatenation Operator in SQL
- MINUS Operator in SQL
- DIVISION operator in SQL
- NOT Operator in SQL

Working with Data:

Creating a table in SQL using CREATE Command
Inserting data into tables using INSERT.
Updating existing data using UPDATE.
Deleting data using DELETE.
Modifying table structure using ALTER TABLE
ADD, DROP, or MODIFY a column in a table.

SQL Queries:

Joining three or more tables
Inner Join Vs Outer Join
How to Get the names of the table in SQL?
SUB Queries
How to print duplicate rows in a table?

Data Manipulation:

Aggregating data using GROUP BY.
Filtering groups using HAVING.
Joining tables using INNER JOIN, LEFT JOIN, RIGHT JOIN.
Combining result sets using UNION, UNION ALL, INTERSECT, EXCEPT.

Data Analysis:

Using functions: COUNT, SUM, AVG, MAX, MIN.
Working with dates and times: DATE, TIME, DATETIME functions.
Subqueries: nested SELECT statements.
- Sub queries in From Clause
- Nested Queries in SQL
Commonly used SQL functions for data analysis.
- Different Mathematical Functions in SQL
- Date functions in SQL
- String functions in SQL
- Numeric Functions in SQL
- Aggregate functions in SQL
- Scalar Functions in SQL

Data Visualization:

Exporting SQL query results to CSV or Excel.
Connecting SQL with visualization tools (e.g., Python libraries like pandas and matplotlib, Tableau, Power BI).

Connecting SQL with Python

Install Python MySQL connector
MySQL-Connector-Python module in Python
Connect MySQL database using MySQL-Connector Python
Create Database
Create Table
Insert into Table
Select Query
Where Clause
Order By Clause
Delete Query
Drop Table
Update Query
Limit Clause

Important topics of SQL in Data Science that you need to learn

Windowing functions
Output control statements
View and indexing
Query optimization
Connecting SQL with Python joins

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FAQs on SQL for Data Science

Is SQL for Data Science best ?

SQL is a very useful tool for the Data Science, using SQL databases for the database management it makes it easier for the user to see the code in a more organized and clean form. It can be one of the best tool for the management of databases in Data Science.

Is SQL better than Python ?

SQL is more faster than the Python for simple queries as SQLs databases have a well defined schema already embedded in it and also the data used at the computation level is also well defined in the SQL.

What is the salary of SQL developer in India ?

In general , salary of SQL developer in India ranges between 2.0 lakhs to 8.0 lakhs, average is 4.0 lakhs.

Is SQL easier than coding ?

Yes, SQL is easier than the general purpose coding languages as it is narrower domain than coding. SQL comprises of queries, data management while coding includes all the programming languages, their synatxes which it self a big thing to learn.

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