SQL for Data Analysis A Beginners Guide to Querying and Database Mastery (Data Decoded The Beginners Journey) (Aniket Jain) (z-library.sk, 1lib.sk, z-lib.sk)
Author: Aniket Jain
SQL
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SQL for Data Analysis: A Beginner's Guide to Querying and Database Mastery By Aniket Jain
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Copyright © 2025 by Aniket Jain All rights reserved. No part of this book may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the publisher, except in the case of brief quotations embodied in critical reviews and certain other non- commercial uses permitted by copyright law. For permission requests, please contact the author at aniketjain8441@gmail.com Disclaimer The views and opinions expressed in this book are solely those of the author and do not necessarily reflect the official policy or position of any organization, institution, or entity. The information provided in this book is for general informational purposes only and should not be construed as professional advice. Publisher Aniket Jain
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Table of Contents
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Chapter 1: Introduction to SQL and Data Analysis What is SQL and Why is it Important for Data Analysis? Role of SQL in Modern Data Science Real-World Applications of SQL in Data Analysis Overview of Relational Databases and SQL Tools
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Chapter 2: Setting Up Your SQL Environment Installing SQL Databases (MySQL, PostgreSQL, SQLite) Introduction to SQL Clients (DBeaver, pgAdmin, MySQL Workbench) Configuring SQL in Python (SQLAlchemy, pandas) Overview of Cloud-Based SQL Solutions (BigQuery, AWS RDS)
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Chapter 3: SQL Basics for Data Analysis Understanding Databases, Tables, and Schemas SQL Syntax and Basic Commands (SELECT, FROM, WHERE) Data Types in SQL (INTEGER, VARCHAR, DATE, etc.) Writing Your First SQL Query
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Chapter 4: Querying Data with SELECT Statements Retrieving Data with SELECT Filtering Data Using WHERE Clauses Sorting Results with ORDER BY Limiting Results with LIMIT and OFFSET
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Chapter 5: Working with Multiple Tables Understanding Relationships (One-to-One, One-to-Many, Many-to-Many) Joining Tables (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN) Combining Data with UNION and UNION ALL Subqueries and Nested Queries
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Chapter 6: Aggregating and Grouping Data Aggregation Functions (COUNT, SUM, AVG, MIN, MAX) Grouping Data with GROUP BY Filtering Groups with HAVING Using DISTINCT for Unique Values
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Chapter 7: Data Cleaning and Transformation in SQL Handling Missing Data (NULL Values) String Manipulation (CONCAT, SUBSTRING, REPLACE) Date and Time Functions (DATE_FORMAT, DATE_ADD, DATEDIFF) Case Statements for Conditional Logic
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Chapter 8: Advanced SQL Techniques Window Functions (ROW_NUMBER, RANK, OVER) Common Table Expressions (CTEs) Recursive Queries Pivoting Data with CASE and GROUP BY
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Chapter 9: Optimizing SQL Queries Understanding Query Execution Plans Indexing for Performance Improvement Avoiding Common Pitfalls (e.g., N+1 Problem) Best Practices for Writing Efficient Queries
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Chapter 10: Working with Large Datasets Partitioning and Sharding Data Using Temporary Tables and Views Optimizing Joins and Subqueries Introduction to Distributed SQL Databases
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Chapter 11: Integrating SQL with Python Connecting to Databases with SQLAlchemy Querying Data Using pandas and SQL Automating SQL Workflows with Python Scripts Building Data Pipelines with SQL and Python
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Chapter 12: Data Visualization with SQL and Python Exporting SQL Results for Visualization Visualizing Data with Matplotlib and Seaborn Creating Dashboards with Plotly and SQL Storytelling with Data Using SQL Insights
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Chapter 13: Time Series Analysis in SQL Working with Date and Time Data Aggregating Time Series Data (GROUP BY DATE) Calculating Moving Averages and Trends Forecasting with SQL and Python
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Chapter 14: Case Study: SQL for Business Analysis Analyzing Sales Data Customer Segmentation with SQL Financial Data Analysis (Revenue, Profit, etc.) Deriving Insights and Reporting
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Chapter 15: SQL for Machine Learning Preparing Data for Machine Learning with SQL Feature Engineering Using SQL Queries Integrating SQL with Scikit-Learn Case Study: Predictive Modeling with SQL and Python
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Chapter 16: Geospatial Data Analysis with SQL Introduction to Geospatial Data Types Querying Geospatial Data (PostGIS, MySQL Spatial) Visualizing Geospatial Data with Python Case Study: Location-Based Insights
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