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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.
Best of all, there is no technical jargon it is written in plain English and is perfect for beginners!
By the time you've read this short book, you'll know more about data collection and cleaning than most people around you!
Discover how to clean your data quickly and effectively. Get this book, TODAY!
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