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
Working with Data:
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.
- Commonly used SQL functions for data analysis.
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
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
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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 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.