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2020-01-02 06:31:35

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Title: Clean Data Author: Megan Squire Length: 267 pages Edition: 1 Language: English Publisher: Packt Publishing Publication Date: 2015-05-29 ISBN-10: 1785284010 ISBN-13: 9781785284014 Save time by discovering effortless strategies for cleaning, organizing, and manipulating your data About This Book Grow your data science expertise by filling your toolbox with proven strategies for a wide variety of cleaning challenges Familiarize yourself with the crucial data cleaning processes, and share your own clean data sets with others Complete real-world projects using data from Twitter and Stack Overflow Who This Book Is For If you are a data scientist of any level, beginners included, and interested in cleaning up your data, this is the book for you! Experience with Python or PHP is assumed, but no previous knowledge of data cleaning is needed. In Detail Is much of your time spent doing tedious tasks such as cleaning dirty data, accounting for lost data, and preparing data to be used by others? If so, then having the right tools makes a critical difference, and will be a great investment as you grow your data science expertise. The book starts by highlighting the importance of data cleaning in data science, and will show you how to reap rewards from reforming your cleaning process. Next, you will cement your knowledge of the basic concepts that the rest of the book relies on: file formats, data types, and character encodings. You will also learn how to extract and clean data stored in RDBMS, web files, and PDF documents, through practical examples. At the end of the book, you will be given a chance to tackle a couple of real-world projects. Table of Contents Chapter 1. Why Do You Need Clean Data? Chapter 2. Fundamentals – Formats, Types, and Encodings Chapter 3. Workhorses of Clean Data – Spreadsheets and Text Editors Chapter 4. Speaking the Lingua Franca – Data Conversions Chapter 5. Collecting and Cleaning Data from the Web Chapter 6. Cleaning Data in PDF Files Chapter 7. RDBMS Cleaning Techniques Chapter 8. Best Practices for Sharing Your Clean Data Chapter 9. Stack Overflow Project Chapter 10. Twitter Project

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