Practical Data Cleaning: 19 Essential Tips to Scrub Your Dirty Data

Practical Data Cleaning: 19 Essential Tips to Scrub Your Dirty Data
ePUB
  • eBook:
    Practical Data Cleaning: 19 Essential Tips to Scrub Your Dirty Data
  • Author:
    Lee Baker
  • Edition:
    -
  • Categories:
  • Data:
    January 30, 2019
  • ISBN:
    1795483458
  • ISBN-13:
    9781795483452
  • Language:
    English
  • Pages:
    45 pages
  • Format:
    ePUB

Book Description
Data cleaning is a waste of time.If the data had been collected properly in the first place there wouldn’t be any cleaning to do, and you wouldn’t now be faced with the prospect of weeks of cleaning to get your dataset analysis-ready.Worse still, your boss won’t understand why your analysis report isn’t on his desk yet, a mere 48 hours after he’s asked for it. Bless him, he doesn’t understand – he thinks that cleaning data is just about clicking a few buttons in Excel and – ta da! – it’s all done. Even a monkey can do that, right? And – for good reason – you won’t get any help from statistics books either. Data is messy and cleaning it can be difficult, time-consuming and costly. Not to mention it’s the least sexy thing you can do with a dataset. Yet you’ve still got to do it, because, well, someone has to…But it doesn’t have to be so difficult. If you're organised and follow a few simple rules your data cleaning processes can be simple, fast and effective. Not to mention fun!Well, not fun exactly, just not quite as coma-inducing.Practical Data Cleaning (now in its 5th Edition!) explains the 19 most important tips about data cleaning with a focus on understanding your data, how to work with it, choose the right ways to analyse it, select the correct tools and how to interpret the results to get your data clean in double quick time.Best of all, there is no technical jargon – it is written in plain English and is perfect for beginners! Discover how to clean your data quickly and effectively. Get this book, TODAY!

Content

Chapter 1: Data Collection
Chapter 2: Data Cleaning
Chapter 3: Data Codification & Classification
Chapter 4: Data Integrity
Chapter 5: Work Smarter, Not Harder

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