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Data Wrangling with SQL

You're reading from  Data Wrangling with SQL

Product type Book
Published in Jul 2023
Publisher Packt
ISBN-13 9781837630028
Pages 350 pages
Edition 1st Edition
Languages
Authors (2):
Raghav Kandarpa Raghav Kandarpa
Profile icon Raghav Kandarpa
Shivangi Saxena Shivangi Saxena
Profile icon Shivangi Saxena
View More author details

Table of Contents (21) Chapters

Preface 1. Part 1:Data Wrangling Introduction
2. Chapter 1: Database Introduction 3. Chapter 2: Data Profiling and Preparation before Data Wrangling 4. Part 2:Data Wrangling Techniques Using SQL
5. Chapter 3: Data Wrangling on String Data Types 6. Chapter 4: Data Wrangling on the DATE Data Type 7. Chapter 5: Handling NULL Values 8. Chapter 6: Pivoting Data Using SQL 9. Part 3:SQL Subqueries, Aggregate And Window Functions
10. Chapter 7: Subqueries and CTEs 11. Chapter 8: Aggregate Functions 12. Chapter 9: SQL Window Functions 13. Part 4:Optimizing Query Performance
14. Chapter 10: Optimizing Query Performance 15. Part 5:Data Science And Wrangling
16. Chapter 11: Descriptive Statistics with SQL 17. Chapter 12: Time Series with SQL 18. Chapter 13: Outlier Detection 19. Index 20. Other Books You May Enjoy

Percentage change

Calculating percentage change over time is an important analysis technique in time series analysis because it allows us to track the growth rate of a variable (such as sales, revenue, or profit) over time. By calculating the percentage change from one time period to another, we can identify trends, patterns, and anomalies in the data. For example, if we see a consistent positive percentage change in sales over time, we can conclude that our business is growing. On the other hand, if we see a consistent negative percent change, we may need to adjust our business strategies or identify potential issues affecting our sales. Moreover, calculating percentage change allows us to compare the relative change between two time periods, regardless of the absolute level of the variable being measured. This makes it easier to compare and analyze trends across different time periods, which is especially useful for identifying seasonality or cyclical patterns in the data. Overall...

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