Megan Squire - Clean Data
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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
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 DetailIs 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.
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Copyright 2015 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: May 2015
Production reference: 1190515
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-78528-401-4
www.packtpub.com
Author
Megan Squire
Reviewers
J. Benjamin Cook
Richard A. Denman, Jr.
Oskar Jarczyk
Commissioning Editor
Akram Hussain
Acquisition Editor
Harsha Bharwani
Content Development Editor
Mamata Walkar
Technical Editor
Nilesh Mangnakar
Copy Editors
Puja Lalwani
Aditya Nair
Stuti Srivastava
Project Coordinator
Shipra Chawhan
Proofreaders
Stephen Copestake
Safis Editing
Indexer
Priya Sane
Production Coordinator
Shantanu N. Zagade
Cover Work
Shantanu N. Zagade
Megan Squire is a professor of computing sciences at Elon University. She has been collecting and cleaning dirty data for two decades. She is also the leader of FLOSSmole.org, a research project to collect data and analyze it in order to learn how free, libre, and open source software is made.
J. Benjamin Cook , after studying sociology at the University of Nebraska-Lincoln, earned his master's in computational science and engineering from the Institute of Applied Computational Science at Harvard University. Currently, he is helping build the data science team at Hudl, a sports software company whose mission is to capture and bring value to every moment in sports. When he's not learning about all things data, Ben spends time with his daughters and his beautiful wife, Renee.
Richard A. Denman, Jr. is a senior consultant with Numb3rs and has over 30 years of experience providing services to major companies in the areas of data analytics, data science, optimization, process improvement, and information technology. He has been a member of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE) for over 25 years. He is also a member of the Institute for Operations Research and the Management Sciences (INFORMS) and the American Society for Quality (ASQ).
I would like to thank my wife, Barbara, my son, Ashford, and my daughter, Addie, for their support in producing this book.
Oskar Jarczyk graduated from Polish-Japanese Academy of Information Technology with an MSc Eng. degree in computer science (major databases). After three years of commercial work, he returned to academia to become a PhD student in the field of social informatics.
His academic work is connected with problems in the category of web intelligence, especially free/libre open-source software (FLOSS) and collaborative innovation networks (COINs). He specializes in analyzing the quality of work in open source software teams of developers that are on the GitHub portal. Together with colleagues from the WikiTeams research team, he coped with the problem of "clean data" on a daily basis while creating datasets in MongoDB and MySQL. They were later used with success for FLOSS scientific analyses in the R and Python language.
In his spare time, Oskar reads books about big data and practices kendo.
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"Pray, Mr. Babbage, if you put into the machine the wrong figures, will the right answer come out?" |
| -- Charles Babbage (1864) |
"Garbage in, garbage out" |
| -- The United States Internal Revenue Service (1963) |
"There are no clean datasets." |
| -- Josh Sullivan, Booz Allen VP in Fortune (2015) |
In his 1864 collection of essays, Charles Babbage, the inventor of the first calculating machine, recollects being dumbfounded at the "confusion of ideas" that would prompt someone to assume that a computer could calculate the correct answer despite being given the wrong input. Fast-forward another 100 years, and the tax bureaucracy started patiently explaining "garbage in, garbage out" to express the idea that even for the all-powerful tax collector, computer processing is still dependent on the quality of its input. Fast-forward another 50 years to 2015: a seemingly magical age of machine learning, autocorrect, anticipatory interfaces, and recommendation systems that know me better than I know myself. Yet, all of these helpful algorithms still require high-quality data in order to learn properly in the first place, and we lament "there are no clean datasets".
This book is for anyone who works with data on a regular basis, whether as a data scientist, data journalist, software developer, or something else. The goal is to teach practical strategies to quickly and easily bridge the gap between the data we want and the data we have. We want high-quality, perfect data, but the reality is that most often, our data falls far short. Whether we are plagued with missing data, data in the wrong format, data in the wrong location, or anomalies in the data, the result is often, to paraphrase rapper Notorious B.I.G., "more data, more problems".
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