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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
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Paperback, 94 pages
Published February 11th 2014 by O'Reilly Media (first published January 20th 2014)
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Louis
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
...more
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
...more
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.
Yuriy
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.
Princessadivana
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
...more
Alex
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.
...more
Greg
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.
Sean
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.
Melissa
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 http://plusminusnmore.rapo.in/thinking-data-max-shron/
...more
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.
Amber
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
Andrew
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.
Tulio
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Jan 06, 2019
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Aug 09, 2015
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Feb 28, 2021
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