Book Image

Clean Data

By : Megan Squire
Book Image

Clean Data

By: Megan Squire

Overview of this book

<p>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.</p> <p>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.</p> <p>At the end of the book, you will be given a chance to tackle a couple of real-world projects.</p>
Table of Contents (17 chapters)
Clean Data
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

About the Reviewers

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).

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.