Data Cleaning: The Ultimate Practical Guide
By Lee Baker
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About this ebook
Transform your data woes into wins with "Data Cleaning: The Ultimate Practical Guide - From Dirty Data to Clean Data." No more staring blankly at error messages or struggling to make sense of messy datasets. This friendly and approachable guide is your passport to mastering the art of data cleaning.
Ever wondered what makes data 'dirty' or 'clean'? This book dives deep into demystifying these concepts, equipping you with the knowledge to identify and eliminate errors efficiently. Learn how to prevent common data pitfalls from sneaking into your analyses, ensuring your data is not just clean but also primed for impactful insights.
Forget dense technical jargon—this guide speaks your language. Perfect for beginners and seasoned professionals alike, it breaks down complex processes into simple, actionable steps. From understanding the phases of data cleaning to mastering essential pre-processing techniques, each chapter is crafted to empower you with practical skills.
Discover:
- The 4 crucial phases of data cleaning
- 6 common types of dirty data and how to address them
- Insights into 5 data collection methods and a streamlined 5-step cleaning process
- Effective data pre-processing using straightforward summary statistics
Whether you're a researcher, analyst, or simply curious about optimizing your data practices, this book is your go-to resource. By the time you finish reading, you'll possess a comprehensive understanding of data preparation—empowering you to unleash the true potential of your analyses.
Ready to elevate your data skills? Don't wait—order "Data Cleaning: The Ultimate Practical Guide" today and take the first step towards cleaner, more impactful data analysis!
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Data Cleaning - Lee Baker
Preface
Data visualisation is sexy. So are Bayesian Belief Nets and Artificial Neural Networks.
You can’t get to do any of these things, though, if your data are dirty. Your analysis package will just stare back at you, saying ‘computer says no’.
But just how do you get the clean data that these packages need?
What is ‘clean data’?
And, for that matter, what is ‘dirty data’?
Data Cleaning: The Ultimate Practical Guide is a guide to understanding what dirty data is, and how it gets into your dataset.
More than that, it is a guide to helping you prevent most types of dirty data getting into your dataset in the first place, and cleaning out quickly and efficiently the remaining errors, so you can have clean, fit-for-purpose and analysis-ready data.
So that your data are ready to change the world!
Data Cleaning: The Ultimate Practical Guide is a snappy little non-threatening book about everything you ever wanted to know (but were afraid to ask) about the craft of cleaning and preparing your data for the sexier parts of your analysis.
First, I’ll explain about the 4 phases of data cleaning.
Then I’ll show you the 6 different types of dirty data that tend to find a way into your dataset.
You’ll learn about the 5 data collection methods typically used in research, and you’ll get a 5 step method of cleaning data.
Finally, you’ll learn about the 4 data pre-processing steps using summary statistics that will help you get your data fit-for-purpose and analysis-ready.
By the time you’ve read this short book, you’ll know more about data collection and cleaning than most people around you!
This book is not written for statisticians. Nor is it written by a statistician. I may have worked as a statistician for several years, but I was actually trained as a Physicist, and these days I have my own Data Science company.
My lack of formal training in statistics is not a weakness, though. On the contrary, it is a strength. I have my own struggles with statistics, so I understand where the hard bits are, and I know how to explain them to others in plain English without using difficult to understand technical terminology.
While this version of the book is complete, it remains a work-in-progress in the sense that in this digital, online, always-connected world we’re living in, nothing is ever truly finished.
So, as this book is for you, I want you to reach out to me and tell me what you think of Data Cleaning: The Ultimate Practical Guide:
Tell me how I can improve it
Tell me which bits I didn’t explain very well
Tell me what I’ve missed out that would have helped you
The next version will be so much better for it.
I hope you enjoy this book, are inspired by it and will check out my other books.
At the end of this book is a link where you can leave your feedback, and I look forward to hearing from you!
Lee Baker
Introduction
If you want to transform your data from dirty to clean, fit-for-purpose and analysis-ready, you’re going to have to roll up your sleeves and be prepared for a messy time!
Part 1:
In Part 1 of this book, I’ll introduce you to the 4 phases of data cleaning that you will follow to get your data clean and ready for analysis.
Part 2:
Clean data doesn’t just happen, and neither does dirty data. In Part 2 of this book, you’ll learn about the 6 common types of dirty data, and what you can do about each of them.
Part 3:
Dirty data is the result of poor data collection methods, and