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

by Mertz

Think about your data intelligently and ask the right questions 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. 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.

498 pages, Paperback

Published March 31, 2021

Reviews

[John · Rating 1 out of 5]

Another book that misses the mark by David who takes techniques he's read in other books and found in tutorials vs. actually used in practice. David has a reputation for focusing on teaching and giving conference talks rather than doing what he talks about in practice, and most importantly, in production. This is another money grab from a so-called "expert" that really just makes his money selling educational material rather than actually doing the work and really understanding the code himself.