Download Clean Data by Megan Squire PDF

By Megan Squire

ISBN-10: 1785284010

ISBN-13: 9781785284014

Save time through learning easy options for cleansing, organizing, and manipulating your data

About This Book

  • Grow your facts technology services via filling your toolbox with confirmed suggestions for a large choice of cleansing challenges
  • Familiarize your self with the the most important facts cleansing tactics, and proportion your individual fresh facts units with others
  • Complete real-world initiatives utilizing info from Twitter and Stack Overflow

Who This publication Is For

If you're a info scientist of any point, newcomers integrated, and attracted to cleansing up your info, this is often the ebook for you! adventure with Python or Hypertext Preprocessor is thought, yet no earlier wisdom of knowledge cleansing is needed.

In Detail

Is a lot of a while spent doing tedious projects equivalent to cleansing soiled information, accounting for misplaced facts, and getting ready info for use through others? if that is so, then having the ideal instruments makes a severe distinction, and may be an excellent funding as you develop your information technology expertise.

The booklet begins by way of highlighting the significance of knowledge cleansing in info technological know-how, and may make it easier to acquire rewards from reforming your cleansing strategy. subsequent, you are going to cement your wisdom of the fundamental strategies that the remainder of the ebook depends upon: dossier codecs, info kinds, and personality encodings. additionally, you will how one can extract and fresh info kept in RDBMS, internet documents, and PDF records, via sensible examples.

At the top of the ebook, you may be given an opportunity to take on a few real-world projects.

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Additional resources for Clean Data

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These are so common; they are like a chef's knife. But some of these concepts, like character encodings, are more special-purpose and exotic, like a tomato shark! in other words, the kind of data you will not find in those carefully constructed datasets that so many books rely on. Here, we encounter some strategies and limitations to interacting with the most common file formats, and then we review the various compression and archiving formats you are likely to run into. Text files versus binary files When collecting data from online sources, you are likely to encounter data in one of these ways: The data will be downloadable in a fileThe data will be available via an interactive frontend to a storage system, for example, via a database system with a query interfaceThe data will be available through a continuous streamThe data will be available through an Application Programming Interface (API).

Customer support Now that you are the proud owner of a Packt book, we have a number of things to help you to get the most from your purchase. Downloading the color images of this book We also provide you with a PDF file that has color images of the screenshots/diagrams used in this book. The color images will help you better understand the changes in the output. zip. Errata Although we have taken every care to ensure the accuracy of our content, mistakes do happen. we would be grateful if you could report this to us.

What you might not have heard, though, is that all of these data science hopes and dreams are predicated on the fact that data is messy. Usually, data has to be moved, compressed, cleaned, chopped, sliced, diced, and subjected to any number of other transformations before it is ready to be used in the algorithms or visualizations that we think of as the heart of data science. In this chapter, we will cover: A simple six-step process you can follow for data science, including cleaningHelpful guidelines to communicate how you cleaned your dataSome tools that you might find helpful for data cleaningAn introductory example that shows how data cleaning fits into the overall data science process A fresh perspective We recently read that The New York Times called data cleaning janitor work and said that 80 percent of a data scientist's time will be spent doing this kind of cleaning.

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