SQL for Data Science

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 for data manipulation and analysis, SQL empowers data scientists to efficiently query and handle vast datasets. Nowadays, Data Science is the Most In-Demand Profession In the field of IT. Currently, a majority of companies are moving towards a data-centric approach.

This data is stored in a database and managed and processed through a Database Management System (DBMS), which simplifies and organizes our work.

This is where the Structured Query Language (SQL) becomes important. 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 and intricate datasets.

SQL is a standard database language used to communicate with databases more briefly it is used to access and manipulate database data. SQL stands for Structured Query Language. It was developed by IBM Computer Scientists in the 1970s. By executing queries, SQL can create, update, delete, and retrieve data in databases like MySQL, Oracle, PostgreSQL, etc.

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.

Need of SQL in 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. SQL enters the picture in this situation. 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.

Elements of SQL for Data Science

Following are the key points of SQL that are most helpful for Data Science. Every Data Scientist must know these necessary SQL skills and features.

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

SQL For Data Science Page Index

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
  • BETWEEN & IN 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
• Cartesian Join & Self Join in SQL
• 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 
• Commit & RollBack Operation in Python 

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

• Windowing functions
• Date & time operators
• Output control statements 
• View and indexing
• Query optimization
• Connecting SQL with Python joins

FAQs on SQL for Data Science

Q1. 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.

Q2. 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.

Q3. 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.

Q4.  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.