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The Elements of Data Analytic Style
Data analysis is at least as much art as it is science. This book is focused on the details of data analysis that sometimes fall through the cracks in traditional statistics classes and textbooks. It is based in part on the authors blog posts, lecture materials, and tutorials. The author is one of the co-developers of the Johns Hopkins Specialization in Data Science the largest data science program in the world that has enrolled more than 1.76 million people. The book is useful as a companion to introductory courses in data science or data analysis. It is also a useful reference tool for people tasked with reading and critiquing data analyses. It is based on the authors popular open-source guides available through his Github account (
The paper is also available through Leanpub ( if the book is purchased on that platform you are entitled to lifetime free updates.
The paper is also available through Leanpub ( if the book is purchased on that platform you are entitled to lifetime free updates.
88 pages, Kindle Edition
First published March 1, 2015
31 people are currently reading
268 people want to read
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Displaying 1 - 29 of 29 reviews
December 14, 2018
Quick read, filled with best practises (do's and don't when analysing and presenting data). Very succinct and will likely refer to it time and time again.
August 9, 2018
A simple, comprehensive and basic statistical book, mainly on R.
January 19, 2019
Pleasant, readable, sensible. This bit's good, tells you exactly how most social science is limited (it stops at inferential, and sometimes manages to mess even that up):
November 6, 2017
Очень сжатое и лаконичное описание видов анализа данных с базовыми советами и указанием типовых ошибок. Подойдет новичкам для начального знакомства с темой. Бесплатно (или с пожертвованием) можно получить здесь:
July 7, 2016
Nice small book. Very well structured. But small.
January 29, 2020
I would certainly recommend this book to anyone who starts with data analytics for a readable, short, summary of the main ideas. What I found lacking was the presentation of these ideas.
The heading levels seemed inconsistent and combine subjects of different level on the same heading level (e.g. chapter 3 mixes the basic components of the processed dataset with minor errors).
Also, the book's insights and ideas could be summed up in a nice series of checklists. And it is great that the book actually provides a series of checklists for the data scientist.
The heading levels seemed inconsistent and combine subjects of different level on the same heading level (e.g. chapter 3 mixes the basic components of the processed dataset with minor errors).
Also, the book's insights and ideas could be summed up in a nice series of checklists. And it is great that the book actually provides a series of checklists for the data scientist.
June 26, 2017
Perhaps useful if this is your first course in data analysis, but not useful for anything but the most beginner starting out. Lack of good examples, strange unexplained discipline specific jargon, and overly broad advice that rarely goes beyond obvious. Was hoping this could be a useful resource to build team norms and goals around, but it is not
July 29, 2018
Taking off from Strunk and White, Leek develops here a handbook for the user of data.
It is a good introduction for someone who is just really diving into thinking about using and presenting data as a thing in itself. Good data and its presentation is a rhetorical tool that is often under-thought. I will surely be using this as a reference book in the future.
It is a good introduction for someone who is just really diving into thinking about using and presenting data as a thing in itself. Good data and its presentation is a rhetorical tool that is often under-thought. I will surely be using this as a reference book in the future.
September 2, 2019
A very lucid guide which act as a prerequisite for a deep driven analysis of a problem in hand and make yourself less vulnerable of some problems which you may fall into while analysis and can yield very of the line results.
June 23, 2017
A nice checklist of important points. Good as a source but could use some more detail.
February 14, 2018
It is a good book, but once you read it then you may not need it anymore.
November 26, 2018
I didn't really need this book, but I enjoyed the read.
March 6, 2019
Good summary of best practices and recommendations with links to more information. I will certainly keep this handy and revisit when working on data analysis.
March 6, 2019
Good Introduction
August 14, 2019
A quick read to give an overview of the best practices.
June 11, 2020
É bem curtinho mas me mostrou várias coisas novas. Li bem rápido, me prendeu bastante. Foi uma boa ver uma visão bem parcial de alguém de dentro da área.
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August 29, 2020PQHS 431 Biostatistics CWRU
October 12, 2020
A good enough read for budding data analysts / scientists on the basics of exploratory data analysis and data preprocessing basics.
February 2, 2016
This was a decent overview to data science, although in some places it assumes knowledge that the reader might not actually have. I think this could become an important reference for someone going further in data science, but I'm not so sure that it shouldn't be revised to be more self-explanatory for beginners.
May 5, 2016
A concise introdution and instructions about all stages of data analysis. Each topic can be expanded into a much more deep communication but the suggestions mentioned are very practical. I think it's a good starting point if you're a new-comer to data analysis. And it would be helpful to frequently look it up when you're doing the process to make sure you're on the right track.
August 22, 2016
Read this from a Data Viz/Tableau PoV, extremely interesting to see how statisticians/academics approach Visual Data Analysis. Small, concise, wish it went into a bit more detail with examples, but I guess there are plenty of other books that do that.
June 22, 2015
Very helpful for what it is, and what it is intended to be -- an extremely concise and pragmatic quick reference, with plenty of helpful pointers to other sources of information for more depth. A very useful companion to the JHU data science courses as well.
November 2, 2015
Helpful guide to know how to create a data analisys.
It just shows the main points to take care of, even a chapter on how to do presentations of data analitics.
It has lots of links to more specific reads.
It just shows the main points to take care of, even a chapter on how to do presentations of data analitics.
It has lots of links to more specific reads.
September 23, 2015
Excellent quick review of the basics of data analysis. No fluff - Almost a cheat sheet for you to browse through!
January 26, 2016
This is a neat introduction to the data analysis field, sadly, the scope is too broad to contribute meaningful information in just 100 pages.
March 15, 2016
Nice short list of good habits to have when analysing and reporting data.
March 21, 2016
Very short, shallow book, but useful as a comprehensive check-list for do's and don'ts in data analysis.
June 13, 2016
A very straightforward guide for beginner level data analysts.
April 6, 2017
Seguindo o estilo do livro de Roger Peng que li antes, o autor aqui faz praticamente uma receita de bolo, com passos detalhados de cada etapa da análise de dados e um conjunto de dicas acerca dos erros mais comuns e como evitá-los. Certamente retornarei a este livro em algum momento da minha pesquisa.
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