Goodreads helps you keep track of books you want to read.
Start by marking “Thinking with Data” as Want to Read:
Thinking with Data
Enlarge cover
Rate this book
Clear rating
Open Preview

Thinking with Data

3.44  ·  Rating details ·  149 ratings  ·  20 reviews
Many analysts are too concerned with tools and techniques for cleansing, modeling, and visualizing datasets and not concerned enough with asking the right questions. In this practical guide, data strategy consultant Max Shron shows you how to put the why before the how, through an often-overlooked set of analytical skills.

Thinking with Data helps you learn techniques for t
Paperback, 94 pages
Published February 11th 2014 by O'Reilly Media (first published January 20th 2014)
More Details... Edit Details

Friend Reviews

To see what your friends thought of this book, please sign up.

Reader Q&A

To ask other readers questions about Thinking with Data, please sign up.

Be the first to ask a question about Thinking with Data

This book is not yet featured on Listopia. Add this book to your favorite list »

Community Reviews

Showing 1-30
Average rating 3.44  · 
Rating details
 ·  149 ratings  ·  20 reviews

More filters
Sort order
Start your review of Thinking with Data
Nov 08, 2014 rated it really liked it
Shelves: computer, math-stats
Thinking with data focuses, not on how to do data analysis, but on the questions that one should be asking. It does so in two ways, first through providing an overall framework to looking at situations, then working through a series of topics using examples to serve as plausible paths of decision making. In a fairly short book, it covers the framework, determining purpose, threats to validity, experimental design, and a few extended examples that illustrates both concepts and deviations. It is a ...more
Daniel Wright
May 07, 2014 rated it did not like it  ·  review of another edition
Shelves: technology
'How to turn information into insights? Well that's easy! You just give me and my consultancy firm unfeasible amounts of cash and we'll tell you a whole load of things you probably could have guessed using just a dash of common sense anyway! It's great! If you don't believe me, I've written this insipid little book about it! Come on, some of it's not even about the relevant subject and shows an utterly superficial understanding of the humanities - you must be convinced now!'

I am rather cruel. Th
Ernestasia Siahaan
Data professionals (data analysts/scientists) are storytellers - we work out meaningful stories from data. I think this book captures that definition/purpose of data professionals. It describes the framework of thinking to work out a (business) meaningful story from data.

It starts out with describing a famework for nailing the “WHY” of a project before diving into the “HOW”. It then continues with examples of building arguments, and from there defining relationships between the points in our ar
Faye Zheng
Aug 25, 2017 rated it really liked it
I use and teach these principles every day on my job. Essential reading for anyone who does analytic work. This book is not about data science, it is about the 95% of data science that should be spent on problem formulation, critical thinking, evidence-based arguments, and deep examinations of value and outcome. Read it in 4 hours and it contains no more information than necessary while providing a thorough, well-organized framework.
Sep 05, 2019 rated it really liked it
Excellent book to get structure with working on data science projects. Convo and scaffolding, techniques I would like to apply regularly.
Nov 08, 2020 rated it really liked it
Как ставить правильные вопросы при работе с данными, различные техники анализа + case studies
ezequiel orbe
Aug 17, 2017 rated it it was ok
It's a "meeh" book.. ...more
Shibaprasad Bhattacharya
Mar 04, 2021 rated it really liked it
A good book for novice Data Scientists/Analysts. It will guide you in asking the right questions that are pertinent to Data Science.
Donn Lee
Apr 26, 2016 rated it liked it
It's an "ok" book that needs a more coherent storyline. There's no theme that ties the elements together, and sometimes it feels as if you're reading a stream of consciousness (or encyclopedia). The sort of thing somebody might tell you over a lunch or in the hallway while walking to the bathroom.

There are some valuable nuggets in there, especially with how it relates to business understanding, so maybe this would be more geared toward those who are more technical and trying to get a grip on the
Concise book full of useful (if often "obvious") information. Interestingly, this book was more geared towards advice that I found to be useful from a general consulting perspective, rather than a "data science" perspective per se. Adds some color to areas that were not covered in such depth in Provost & Fawcett's "Data Science for Business," which overall remains the comprehensively best book on the subject I've yet read. Shron's book is much quicker to get through, though, and does point towar ...more
Carolina Bento
Oct 20, 2016 rated it it was amazing
This is a must-read book for anyone that works with data on a daily basis!
The framework presented in this book is very straightforward and focuses on the essential topics that you should think about before starting any project. It can definitely help you find clarity about what's important in your projects.
Apr 13, 2014 rated it it was ok
Too informal and not much new content here for anyone that has done basic project management. Feels like a short work meant to cash in on the data science/big data book frenzy. Data Science for Business, while much lengthier, is time better spent.
Feb 01, 2014 rated it really liked it
Focuses on asking the right questions more than how to ask them. A bit abstract in spots, but overall useful.
Mar 25, 2014 rated it really liked it
Shelves: non-fiction, data
Sound advice and great prose, but sometimes the organization is too opaque.
Menaka Sankaralingam
Dec 09, 2014 rated it it was ok
Not a very useful book in my opinion.
For a complete review check
Jim Razinha
Sep 12, 2014 rated it it was ok
Well...that was a wasted hour and a half. Little value in this. Nothing new and no paradigm shifting of the old. Even though it was quite short, it could have been distilled into a tri-fold tract.
May 10, 2014 rated it liked it
some good concepts to think about but not a ton of novel information
Azucena Coronel
Aug 06, 2015 rated it did not like it
Shelves: data-analytics
Very basic, it is like a compilation of definitons and an attempt to apply those definitions to small use cases. Didn't found value ...more
Sep 03, 2015 rated it liked it
For whatever reason, I had a tough time making it through this short book. I did enjoy the section on causality, which you don't see very often in books like this ...more
Ingus Rūķis
Mar 03, 2014 rated it really liked it
Loved the first chapter of the book, it applies not only to data science but to other types of projects as well.
rated it it was ok
Jan 06, 2019
rated it really liked it
Apr 26, 2014
rated it really liked it
Feb 10, 2019
Diana Parra
rated it it was amazing
Oct 01, 2018
rated it it was ok
Aug 09, 2015
rated it it was amazing
Feb 28, 2021
rated it really liked it
Sep 26, 2019
rated it it was ok
Jun 10, 2015
rated it really liked it
Jul 16, 2020
rated it really liked it
Sep 28, 2014
« previous 1 3 4 5 next »
There are no discussion topics on this book yet. Be the first to start one »

Readers also enjoyed

  • Introducing Mlops: How to Scale Machine Learning in the Enterprise
  • Daredevil: Back in Black, Volume 1: Chinatown
  • Captain America, Volume 1: Castaway In Dimension Z, Book One
  • Daredevil by Chip Zdarsky, Vol. 2: No Devils, Only God
  • Daredevil: Born Again
  • Daredevil: The Man Without Fear
  • Civil War: A Marvel Comics Event
  • Green Lantern: Rebirth
  • Daredevil: Yellow
  • Daredevil by Chip Zdarsky, Vol. 1: Know Fear
  • Legacy: The Last Will and Testament of Hal Jordan
  • Flash & Green Lantern: The Brave and the Bold
  • Building Intelligent Systems: A Guide to Machine Learning Engineering
  • A Children's Bible
  • Daredevil: Back in Black, Volume 2: Supersonic
  • Daredevil: Back in Black, Volume 3: Dark Art
  • 97 Things Every Engineering Manager Should Know: Collective Wisdom from the Experts
  • Facts and Fallacies of Software Engineering
See similar books…

Goodreads is hiring!

If you like books and love to build cool products, we may be looking for you.
Learn more »

Related Articles

What will you do when it's your turn to pick your book club's next read? Well, this is what you won't do: panic. Why not? Because we've dug...
76 likes · 15 comments
“The four parts are the context of the project; the needs that the project is trying to meet; the vision of what success might look like; and finally what the outcome will be, in terms of how the organization will adopt the results and how its effects will be measured down the line.” 0 likes
“Consider this incomplete list of things that can be made better with data: Answering a factual question Telling a story Exploring a relationship Discovering a pattern Making a case for a decision Automating a process Judging an experiment” 0 likes
More quotes…