Clean Data

by Megan Squire (Author)

9 ratings

ISBN-13: 978-1785284014

ISBN-10: 1785284010

Why is ISBN important?

Share <Embed>

Other Sellers

from

Other Sellers

See all 3 versions

In Stock.

Ships from and sold by Amazon.com.

FREE delivery Friday, May 27

Or fastest delivery Wednesday, May 25. Order within 45 mins

Select delivery location

Key Features

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

Book Description

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.

What you will learn

Understand the role of data cleaning in the overall data science process
Learn the basics of file formats, data types, and character encodings to clean data properly
Master critical features of the spreadsheet and text editor for organizing and manipulating data
Convert data from one common format to another, including JSON, CSV, and some special-purpose formats
Implement three different strategies for parsing and cleaning data found in HTML files on the Web
Reveal the mysteries of PDF documents and learn how to pull out just the data you want
Develop a range of solutions for detecting and cleaning bad data stored in an RDBMS
Create your own clean data sets that can be packaged, licensed, and shared with others
Use the tools from this book to complete two real-world projects using data from Twitter and Stack Overflow

About the Author

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.

Why Do You Need Clean Data?
Fundamentals – Formats, Types, and Encodings
Workhorses of Clean Data – Spreadsheets and Text Editors
Speaking the Lingua Franca – Data Conversions
Collecting and Cleaning Data from the Web
Cleaning Data in Pdf Files
RDBMS Cleaning Techniques
Best Practices for Sharing Your Clean Data
Stack Overflow Project
Twitter Project

About the Author

Megan Squire

Start reading Clean Data - Data Science Strategies for Tackling Dirty Data on your Kindle in under a minute.

Don't have a Kindle? Get your Kindle here, or download a FREE Kindle Reading App.

Product details

Publisher ‏ : ‎ Packt Publishing (May 25, 2015)
Language ‏ : ‎ English
Paperback ‏ : ‎ 272 pages
ISBN-10 ‏ : ‎ 1785284010
ISBN-13 ‏ : ‎ 978-1785284014
Item Weight ‏ : ‎ 1.04 pounds
Dimensions ‏ : ‎ 7.5 x 0.62 x 9.25 inches

Customer Reviews:
9 ratings

About the author

Follow authors to get new release updates, plus improved recommendations.

Megan Squire

Brief content visible, double tap to read full content.

Full content visible, double tap to read brief content.

I am a professor of Computing Sciences at Elon University (NC, USA). I teach mostly database systems, web development, data mining, and data science courses. My first language was Perl, but I am learning to love Python. Some colleagues and I started the FLOSSmole project in 2004 as a way to collect data and analyses about free, libre, and open source software (FLOSS) projects. We provide lots of historical data about the way open source has grown and changed over the past decade-plus.

Customer reviews

5 star		17%
4 star 0% (0%)		0%
3 star		45%
2 star 0% (0%)		0%
1 star		38%

How customer reviews and ratings work

Top reviews from the United States

There was a problem filtering reviews right now. Please try again later.

Dimitri Shvorob

Poor

Reviewed in the United States on June 16, 2016

You cannot blame an academic for trying to get extra mileage out of research project where she did some PHP/Python/MySQL coding and get a "book" on one's CV, but this particular outcome leaves a bad taste.

Most obviously, the title is a lie, as the book is not about data cleaning, but about data extraction, primarily web scraping. Invocations of data science are similarly for the gullible - you might as well write a book about, say, project management, and call it "data science", because hey, data scientists manage projects too. Second, the author advertises the book to beginners, but can't be bothered to provide beginner-adequate hand-holding, and just gives them code dumps - off you go. This isn't helping beginners, it's exploiting them.

Beginners, you don't have to settle for this. If it's actual data cleaning that you are looking for, I recommend "Data Quality Assessment" by Arkady Maidanchik. For what this book was actually going for, see "Web Scraping with Python" by Ryan Mitchell, or "Automate the Boring Stuff with Python" by Al Sweigart. Once in a blue moon, Packt does publish a good book, but "Clean Data" is part of the 99%, a low-quality quickie by a non-expert and a non-writer.

15 people found this helpful

Helpful

D. Pentecost

Useful for beginners, as intended

Reviewed in the United States on June 28, 2016

This book is useful for new computer science students and those who are returning to the field after a good bit of time away. The content is clearly laid out and offered as part of the Packt "Learning" series. The material is aimed at people who are new to the subject, such as first or second year undergraduate students or people who are very new to Data Science. Experienced or previously educated data science students will find the material generic and more like a review course. If you're a freshman or sophomore data science student, buy this book. If you're a senior or masters student, skip it unless you weren't paying attention in class those first 2 years.

3 people found this helpful

Helpful

Steve Morse

Great book for beginners or researchers / students returning to the field

Reviewed in the United States on July 24, 2016

5 stars for the clear and conversational writing style. The author ties together the many topics of the book under the theme that data cleaning is not like expecting a researcher to do "janitor work," but more like expecting a chef to know how to keep a clean kitchen and sharpen a knife --- which I found to be a convincing premise. I also appreciated the context and background she provides in several chapters, which you cannot get from self-teaching this stuff from Stack Overflow forums.

4.5 stars for content. Pros: I recently began a graduate program at MIT in a data science-heavy field, and the sections on converting between formats in Python was especially helpful as a jump start to several projects. As another reviewer mentioned, this is not only an appropriate book for beginners, but anyone returning to the field after a hiatus. Cons: I agree with another reviewer that a section on R would have been helpful. Also the code snippets were written like a cookbook, where some may prefer a more line-by-line explanation.

2 people found this helpful

Helpful

Robert Menke

Must read for both aspiring data scientists and professionals

Reviewed in the United States on September 13, 2015

Dr. Squire, the author, was by far my favorite teacher at Elon University. She is extremely intelligent, hard-working, passionate, and has a wealth of corporate and academic experience. I've never met someone more excited about the power of data analysis, and her skills are universally respected and admired by her students and colleagues. This book is a result of a lifetime of dedication to the data science process and will teach the reader to use modern tools to increase the reader's efficacy as a professional, academic, or hobbyist. I will do my best in this review to provide an objective overview of the book, focusing on the skills the reader can expect to acquire.

Ask any data scientists, developer, or analyst and they'll tell you that they spend more time than they'd care to admit cleaning, parsing, and formatting data to suit their needs. This very process is the root of countless hours of lost productivity, frustrating bugs in code, and incomplete or sloppy analysis. This book attempts to arm the reader with a set of tools and a mindset by which the reader can successfully clean data and display it in compelling ways. For me it's been the most valuable technical literature I've dedicated time to in quite awhile.

But enough with the rhetoric, what should you know before thinking about purchasing this book? I'll focus on what I thought were the key skills to be gained from the book and also some things you should be aware of prior to investing your time and money.

Key Takeaways:

1 - You will learn to seamlessly convert common file types like CSV, TSV, JSON, and HTML into MySQL tables and vice versa. There are many subtleties I wasn't even aware of - for example, using the correct data types when cleaning data with tools like MS Excel. These subtleties, if not handled correctly can cause major headaches down the road.

2 - You'll learn to scrape and clean data using Python and PHP. Python is one of the go-to data science and visualization languages and is a personal favorite tool of mine. While PHP may not be a great choice for data science, it is refreshingly easy to use in conjunction with MySQL and doesn't require nearly as much boiler plate code as other languages. Those of you familiar with JDBC know just how annoying some languages make working with SQL.

3 - You'll learn to automate daily workflow items. The amount of data contained in PDFs, text files, and spreadsheets is enormous and can be difficult to parse. Often times, companies will resort to hiring more people or implementing increasingly complicated processes to store and communicate that data - this book will teach you how to automate those types of tasks and make life easier for yourself and your colleagues.

4 - This book will teach you to visualize the data you've cleaned using d3.js - a very powerful visualization library used by companies like the New York Times. Programmatic data visualization is a difficult task and it's very difficult to figure out all the nuts and bolts by yourself. It was enormously beneficial to have Dr. Squire's help in class working out the details of a tricky visualization problem, and she's done a great job of communicating that knowledge in this book.

Some things to consider before purchasing:

1 - If you're looking for a cookbook for a specific language this book may not be for you. While Dr. Squire includes numerous working code examples, it's my understanding that she's trying to impart knowledge of the fundamentals and thought process of cleaning data.

2 - If you've yet to learn fundamentals of programming, this book will not spend much time teaching you fundamentals of programming - after all, it's not intended to. If you are a beginner looking to become a data scientist, I would start with some books that go over programming fundamentals like data structures, objects, classes, function, etc.

5 people found this helpful

Helpful

Live Free

Better choices

Reviewed in the United States on January 18, 2018

It has the feel of a research paper that someone told her she should turn into a book to make a few bucks. I guess there is nothing wrong with trying to make money, capitalism and everything but there are far better choices on this topic such as Arkady Maidanchik's book "Data Quality Assessment." That is the book you want.

2 people found this helpful

Helpful

Clean Data

Key Features

Book Description

What you will learn

About the Author

Table of Contents

Editorial Reviews

About the Author

Product details

About the author

Megan Squire

Customer reviews

Top reviews from the United States

There was a problem filtering reviews right now. Please try again later.