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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

Machine Learning for Time-Series with Python

$57.99

Cleaning Data for Effective Data Science

$45.99

$50.99

Total $ 154.97

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Stefan Mar 25, 2024

I'm only in the first chapter and I can already tell. This one is special. I've already learned so much! Looking forward to absorbing the rest.

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Steve Karmeinsky May 12, 2021

Data Science (which includes machine learning etc) is all about working with data, sometimes a lot of data. As the saying goes, garbage in, garbage out and not all data sets are as clean as they should be, that may be due to data entry, formatting or other simple errors, however some data could be an outlier or statistical error and even spelling errors.This book shows the errors that can occur and methods to clean that data so it's useful for being processed so the messy data won't cause problems down the line. There are clear examples both of the types of data, how to manipulate it (code examples mainly python, but shell scripts, R and various data bases like SQQLite and others).If you're new (or even an old hand) at using data, then this book should be near you as a reference.

Amazon Verified review

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.

Nate Apr 27, 2021

I think this is a good reference book for cleaning and handling numeric data, and includes some feature engineering as well. For images or text, one will probably want to consult another book or reference for more complete information. For example, the feature engineering and data preparation for NLP can get complex and there are many ways to do it (same for images). I think chapters 1, 2, 4, 5, and 6 would be most helpful for a DS beginner or intermediate practitioner. My favorite part was early in the book on the discussion of how spreadsheets can cause major problems.

Carl Trachte Oct 25, 2021

Even if all you learn is some cool quotes (like the one in the headline), the UNIX tr(1) command, and what the word "chimera" is and how it relates to SQLite's data type enforcement, you never get cheated buying or reading one of this guy's books.Dr. Mertz is an intelligent, conscientious author who makes for a good read. His books always emphasize the mindset and thought process over rote memorization of code or cut and paste examples. But if you want to use the book that way, there are plenty of code examples you can use as templates. I am weak on Pandas and UNIX shell scripting, and totally ignorant of R. Just seeing how much you can do in a few lines of code throughout the book is encouraging.I'm about 2/3 of the way through the book. For me this is a great introduction to data science. Most of what I do is cleaning and transforming data. I think someone who is a data scientist by trade could still benefit a lot from this book - if only for review of the tools and, more importantly, methods available for cleaning and preparing data for the heavy lifting of data science.As a non-data scientist, I have still found plenty that is useful, even in the later data science focused chapters I'm reading now.Recommend.

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

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