Get Clean Data - Data Science Strategies for Tackling Dirty Data PDF

By Megan Squire

ISBN-10: 1785284010

ISBN-13: 9781785284014

Key Features

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

Book Description

Is a lot of it slow spent doing tedious projects equivalent to cleansing soiled facts, accounting for misplaced facts, and getting ready facts for use by way of others? if this is the case, then having the ideal instruments makes a serious distinction, and may be a good funding as you develop your facts technological know-how expertise.

The e-book starts off by means of highlighting the significance of knowledge cleansing in information technological know-how, and may aid you acquire rewards from reforming your cleansing procedure. subsequent, you are going to cement your wisdom of the elemental techniques that the remainder of the publication is dependent upon: dossier codecs, information forms, and personality encodings. additionally, you will find out how to extract and fresh facts kept in RDBMS, internet records, and PDF files, via sensible examples.

At the tip of the booklet, you can be given an opportunity to take on a few real-world projects.

What you'll learn

  • Understand the position of knowledge cleansing within the total facts technology process
  • Learn the fundamentals of dossier codecs, info kinds, and personality encodings to wash facts properly
  • Master serious good points of the spreadsheet and textual content editor for organizing and manipulating data
  • Convert facts from one universal layout to a different, together with JSON, CSV, and a few special-purpose formats
  • Implement 3 diversified options for parsing and cleansing information present in HTML documents at the Web
  • Reveal the mysteries of PDF records and easy methods to pull out simply the knowledge you want
  • Develop more than a few options for detecting and cleansing undesirable facts kept in an RDBMS
  • Create your individual fresh information units that may be packaged, approved, and shared with others
  • Use the instruments from this booklet to accomplish real-world initiatives utilizing information from Twitter and Stack Overflow

About the Author

Megan Squire is a professor of computing sciences at Elon college. She has been amassing and cleansing soiled facts for 2 a long time. She is additionally the chief of FLOSSmole.org, a learn undertaking to assemble facts and research it on the way to learn the way loose, libre, and open resource software program is made.

Table of Contents

  1. Why do you want fresh Data?
  2. Fundamentals codecs, varieties, and Encodings
  3. Workhorses of unpolluted info Spreadsheets and textual content Editors
  4. Speaking the Lingua Franca facts Conversions
  5. Collecting and cleansing facts from the Web
  6. Cleaning information in Pdf Files
  7. RDBMS cleansing Techniques
  8. Best Practices for Sharing Your fresh Data
  9. Stack Overflow Project
  10. Twitter Project

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Additional info for Clean Data - Data Science Strategies for Tackling Dirty Data

Sample text

Next, we need to consider whether, by allowing zero values for dates, we are also making our x axis substantially longer (my count query yielded 1211 rows, but there are a total of 1822 days in the range specified, which is 1998-2002). Maybe showing zero count days might not work; if the graph is so crowded, we cannot see the gaps anyway. To compare, we can quickly run the same data into Google Spreadsheets (you can do this in Excel or D3 too), but this time, we will only select our count column to build the graph, thereby forcing Google Spreadsheets to not show dates on the x axis.

Yet, there are still some options that can differentiate one delimited format from another. The first differentiator is how each instance of the data (each row) is separated. Usually at the end of a line, a new line, or a carriage return, or both are used depending on the operating environment in use during the creation of the file. Seeing invisible characters In the preceding example, the new line or carriage return is invisible. How do you see the invisible characters? We will read the same file in Text Wrangler on a Mac (similar full-featured editors such as Notepad++ are available for Windows), where we can use the Show invisibles option (located by navigating to View | Text Display).

Type conversion at the SQL level Here, we will walk through a few common cases when SQL can be used to manipulate data types. Example one – parsing MySQL date into a formatted string For this example, we will return to the Enron e-mail dataset we used in Chapter 1, Why Do You Need Clean Data?. As with the previous example, we are going to look in the message table, where we have been using the date column, which is stored as a datetime MySQL data type. Suppose we want to print a full date with spelled-out months (as opposed to numbers) and even the day of the week and time of day.

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Clean Data - Data Science Strategies for Tackling Dirty Data by Megan Squire


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