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Key benefits

Master data cleaning techniques necessary to perform real-world data science and machine learning tasks

Spot common problems with dirty data and develop flexible solutions from first principles

Test and refine your newly acquired skills through detailed exercises at the end of each chapter

Description

Data cleaning is the all-important first step to successful data science, data analysis, and machine learning. If you work with any kind of data, this book is your go-to resource, arming you with the insights and heuristics experienced data scientists had to learn the hard way. In a light-hearted and engaging exploration of different tools, techniques, and datasets real and fictitious, Python veteran David Mertz teaches you the ins and outs of data preparation and the essential questions you should be asking of every piece of data you work with. Using a mixture of Python, R, and common command-line tools, Cleaning Data for Effective Data Science follows the data cleaning pipeline from start to end, focusing on helping you understand the principles underlying each step of the process. You'll look at data ingestion of a vast range of tabular, hierarchical, and other data formats, impute missing values, detect unreliable data and statistical anomalies, and generate synthetic features. The long-form exercises at the end of each chapter let you get hands-on with the skills you've acquired along the way, also providing a valuable resource for academic courses.

Who is this book for?

This book is designed to benefit software developers, data scientists, aspiring data scientists, teachers, and students who work with data. If you want to improve your rigor in data hygiene or are looking for a refresher, this book is for you. Basic familiarity with statistics, general concepts in machine learning, knowledge of a programming language (Python or R), and some exposure to data science are helpful.

What you will learn

Ingest and work with common data formats like JSON, CSV, SQL and NoSQL databases, PDF, and binary serialized data structures

Understand how and why we use tools such as pandas, SciPy, scikit-learn, Tidyverse, and Bash

Apply useful rules and heuristics for assessing data quality and detecting bias, like Benford's law and the 68-95-99.7 rule

Identify and handle unreliable data and outliers, examining z-score and other statistical properties

Impute sensible values into missing data and use sampling to fix imbalances

Use dimensionality reduction, quantization, one-hot encoding, and other feature engineering techniques to draw out patterns in your data

Work carefully with time series data, performing de-trending and interpolation

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Cleaning Data for Effective Data Science

$43.99

Machine Learning for Time-Series with Python

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$48.99

Total $ 147.97

Alexander Afanasyev Apr 19, 2021

David did an incredible job covering the topics of data preparation and cleaning, I've learned quite a lot. Additionally, the outstanding choices of quotes, beautiful expressions, and anecdotes made this book an immensely enjoyable read. This goes directly to the list of book recommendations for my peers.

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chestnutj2000 Aug 26, 2021

I had the opportunity to take several training courses with David as he was authoring and editing the book and work through many of the book's projects. My entire career really started 20 years ago by being able to show companies how powerful their data was...but it had to be mined and transformed to be useful. That was in the heyday of the RDBMS and 3-tier applications...Oracle, SQLServer. To keep up in today's world, you definitely need up upskill with Python as it opens up a world of easy integrations. And of course, Big Data is huge and driving every aspect of our life. From wearables that track fitness trends with massive amounts of physiological data to iOt devices to the infinite amount of transactional data on the web...Do yourself a favor and get straight to having a well-seasoned guide show you how to be practically effective. In learning any domain you can waste massive amounts of time and energy trying to sift through complexity without really knowing which info is important and practical. Let Dave show you!

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Matthew Emerick Apr 23, 2021

About This BookThis is a crucial book thanks to the deluge of data we currently have and use in our software applications. It looks at both structure and content issues with data of various types and the pros and cons of methods to clean it enough to be useful.Who Is This For?This is a useful book for anyone who imports data into their application, which is a good number of us. Given the Python and R code in the book, it’s good to have some knowledge and experience of one of these languages, but that’s about all you need to know. I personally recommend anything earning a computer science degree to work through this book around the same time they learn about data structures and algorithms.OrganizationThe overall organization of this book follows a standard data pipeline that you might set up for your application and what cleansing issues you might need to resolve along the way. This is, in my opinion, a great way to set up the book. Within each chapter, you have your topics, the exercises, and then a summary for the chapter. All in all, this is a well-organized book.Did This Book Succeed?I believe that the author did a tremendous job on a difficult and large topic. This is one of the most time-consuming and least talked about portions of any data pipeline for data science and machine learning tasks. It is also one of the most important. Anyone working in data or AI needs to read through this book and learn how to implement its processes in order to have cleaner and therefore more useful data.Rating and Final ThoughtsI give this book a 5 out of 5.It is useful, timely, and well organized. While it may not be set up like a cookbook or reference, it can use used as such. It is well suited as a textbook either for a course or self-study. It should be in anyone’s personal library, ready to be pulled when there is data to be cleansed.

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N Satpall May 27, 2021

Certainly, some data is better, and some is worse. But almost all data is dirty and making it clean is a big challenge! This book goes a long way in helping meet this aspiration.It helps distinguish data quality from data utility - Data can be dirty but still be useful, and it can be clean but have little purpose. This book gives several techniques that can aid in evaluating the utility of data.The focus is not on learning to use tools, but to understand the purpose of data quality, and the concepts are applicable in any programming language used for data processing and machine learning.It covers data ingestion to look at structural matters, and anomalies, data quality, feature engineering, value imputation, and model-based cleaning to direct attention to content issues. There are plenty of illustrations that primarily use Python and associated tools, and R and its Tidyverse tools are mostly shown as code alternatives.This book is recommended for self-directed reader or in more structured academic, training, or certification courses. It avoids giving explicit solutions for mere copy-paste but presents a whole lot of exercises to enable readers in solving practical problems.

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dr t Apr 17, 2021

Anyone who works with data regularly will know the amount of effort required in cleaning the data in preparation for modelling. Hence, I was pleasantly surprised to see many new things in this book that I had not come across, nor considered, previously.The book discusses the tools and techniques needed for various data processing tasks and has exercises at the end of each chapter to allow the reader to practice the ideas presented in the chapter. Along the way the reader will also pick up useful knowledge on topics such as MongoDB and insights into data types and there are also sections in the book which warrant bookmarking for future rereading (e.g. the section on misspelled words and the section on weights). Furthermore, the author also introduces commands (e.g. melt) and new ideas (e.g. Benfords Law) that this reader at least had not previously come across.The book is well-written in a humorous style, but does require some level of knowledge and likely isn't for beginners, however, software engineers and data scientists would definitely benefit from this book.By the end of the book, I definitely felt that I had acquired a much firmer understanding and a different perspective on the data cleaning process that is necessary when working on real-world tasks.Highly recommended.

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Cleaning Data for Effective Data Science: Doing the other 80% of the work with Python, R, and command-line tools

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