Free PDF Clean Data - Data Science Strategies for Tackling Dirty Data, by Megan Squire
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Clean Data - Data Science Strategies for Tackling Dirty Data, by Megan Squire
Free PDF Clean Data - Data Science Strategies for Tackling Dirty Data, by Megan Squire
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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
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
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.
Table of Contents- Sales Rank: #1022729 in Books
- Published on: 2015-05-29
- Released on: 2015-05-25
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .62" w x 7.50" l, 1.04 pounds
- Binding: Paperback
- 267 pages
About the Author
Megan Squire
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.
Most helpful customer reviews
6 of 6 people found the following review helpful.
Must read for both aspiring data scientists and professionals
By Robert Menke
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.
4 of 4 people found the following review helpful.
Useful to learn how to deal with data using a large variety of tools and data formats
By gabriele.lanaro
The book clean data is for someone who wants to learn effective strategies on how to prepare your datasets for data analysis.
The book is structured in 10 chapters, where the author explores how to handle data in several data formats and tools (Excel, JSON, CSV, SQL ...)
The strong points of the book are:
- Excellent writing style. Explanations are very clear and interesting.
- Chapter on sharing and documenting data
- Twitter and Stackoverflow Projects
However, I believe some choices have been questionable
- Use of PHP for many scripts and d3.js for plotting, while omitting R, a very popular language among data scientists.
- I would have liked an emphasis on larger datasets, as demand is growing for those.
2 of 2 people found the following review helpful.
Useful for beginners, as intended
By D. Pentecost
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.
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