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The Art of Data Science: A Guide for Anyone Who Works with Data
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The Art of Data Science: A Guide for Anyone Who Works with Data

3.78  ·  Rating details ·  192 ratings  ·  26 reviews
This book describes, simply and in general terms, the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of their experience in a format that is applicab ...more
ebook, 154 pages
Published September 5th 2015 by Leanpub
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Sep 10, 2015 rated it liked it
I'm somewhat ambivalent regarding this book -- I very much appreciate the pragmatic writing style, and there are some genuinely useful pieces of advice contained within. However, the target audience seems ambiguous. The best fit seems to be folks who are intending to take the JHU data science classes, and retrospectively this looks like it would be a very handy companion guide to the course. Having taken them, however, along with a number of other statistics/data analysis type classes, much of t ...more
Aug 12, 2018 rated it it was amazing
The authors share their experience in data analysis and the steps they propose seems necessary for a neat data analysis. I think I should re-read this book throughout my future data analysis projects.
Ayas Shaqour
Oct 03, 2017 rated it liked it
I wouldn't recommend this to someone who isn't an advanced or works as a data scientist.
Although I am pretty sure it would be too good for some one who Is/Does.

The book explains the untaught process, or lets say the unspoken process of a data scientist's job.
It explains the things you wouldn't read in a book of statistics, it explains the thought process taking place in a data scientist's mind.
It discusses the major steps taken to complete a task and how to judge every step you take.
It intro
Feb 04, 2018 rated it really liked it
This excellent book takes you through each step of a typical data science project giving general advice, warning about common mistakes and giving many practical examples (including real industrial data science projects) to illustrate each of the points. It helps to build a mind map of options available at a data scientist’s disposal during each of the project stages. Definitely a book to read multiple times.
Javad Ebadi
Feb 14, 2018 rated it really liked it
This book is very good to obtain a big picture about what is data analysis. The most important lesson that I learnt from this book was that a data analysis starts with a question, not with the data and at the end of the day it leads to another (better) question. The challenges in data analysis processes are described very well. Various types of question and types of data analysis are explained.

The book is very good for beginners, also it can be used by sophisticated data scientists.
Mar 10, 2018 rated it it was ok
I work with data, but I guess this book isn't for me.

I got the feeling that is was addressed to the experienced data scientist and not someone that wants to understand a little bit more about it. It seems that the author focuses more on the process and the logistics of the day-to-day tasks of a data scientist rather than the field of data science.

The book was interesting and well written but didn't really answer my questions. For sure I didn't learn the "art" of data science.
Moahmmed Al-shami
Apr 21, 2018 rated it really liked it
It's a good book for anyone who wants to know more about data science and data science analysis
In this book, Roger D.peng showed the entire process of data science analysis :
1-stating a question
2-EDA (Exploratory data analysis)
3-Using Models & Expectations
4-inference and prediction
5- Interpreting Your Results
Aug 26, 2017 rated it it was amazing
This book equipped me to answer all the questions I have in my data analyst life, specifically the "why am I here?" and "what is my purpose?" type ones. In my work, there are a lot of technical resources for data analysis tools but not a great deal of guidance on method. This book is exactly what I was looking for to fill that gap.
Jan 07, 2020 rated it it was ok
This is an introductory book on how to think analytically and some of the terminology that goes along with it. It's good for learning how to speak data science and data analysis, but it won't get much further than that. It's helpful for touching up on your ability to think analytically, refresh on terminology, and hone presentation skills.
Nov 12, 2019 rated it really liked it
An informative book on the process of data analysis from start to end.
Feb 19, 2020 rated it really liked it
Really clear with helpful examples. Good introduction for approaching analyses from an artsy perspective in simple language
Sep 15, 2017 rated it it was amazing
Great book for those new to data science. The illustrations and examples were apt.
Sep 08, 2020 rated it liked it
3.5. A good overview for someone with some practical experience in analytics who wants to better understand the data analysis workflow.
Mustafa Hajmohammed
Nov 27, 2018 rated it it was amazing
Gentle introduction for those who are searching for more knowledge on data science in general and data mining in particular.
Not recommended for experienced data scientists, it is too basic.
Aug 04, 2019 rated it liked it
Shelves: 2019_read
High-level useful bits.
Mar 19, 2017 rated it it was amazing
Shelves: science-stuff
This is an excellent book that breaks down the steps of data analysis. Many of these steps are carried out intuitively by data analysts, and it was enlightening to have them identified and put into context of the process. As a data analyst, I enjoyed this book. I think it would be a great read for people who work with analysts but don't have a clear view of what the work entails; I believe it would improve their appreciation of the creativity and complex processes carried out by the analysts wit ...more
Mar 10, 2018 rated it really liked it
I read this in South Africa when it was recommended reading during the first week of a data science course. While it's hard to know for certain, I believe that the perspective I gained from it contributed towards my success in the course. I learned the most about all the non-science parts of a data scientist's job, and this was valuable to me later as I began to accumulate some of my earliest work experience in a data-science-like role.
Fermin Quant
Oct 16, 2016 rated it really liked it
Great book with awesome examples. There are quite a few errors in grammar and spelling, but they do not subtract from the value of the knowledge. Very useful frameworks to understand and apply to data analyses.
I did feel the last third part was a little rushed, and like it is somewhat incomplete, but overall the content was really good and useful.
Yahia El gamal
Dec 10, 2015 rated it liked it
This is a light data science book. I have mixed feelings towards it. Sometimes, Prof. Roger writes assuming some prior knowledge and experience (e.g. he wrote some parts freely assuming some knowledge about distributions, significance, ..) and those are the parts I really liked in the book. On the other hand, prof. Roger sometimes writes as if the audience are total newbies with no previous experience.

Leaving the above note aside, the abstraction Prof. Roger introduces about the process of data
Apr 10, 2016 rated it liked it
This book was a mixed bag for me. Most of the time, I felt he was explaining basic research approaches (developing a hypothesis, exploratory data analysis, and especially the last chapter in presenting your findings). Other times I would get lost in his examples especially those without visualization. Overall, the book focused heavily on data analysis, and I think data science as a field also encompasses data extraction or collection, management, and virtualization. I would have liked to learn m ...more
Oct 06, 2015 rated it really liked it
As the book mentions, "the book continues to serve as a useful resource after you're done reading it when you hit the stumbling blocks that occur in every analysis". The book is what it sought out to be, a good reference material to a very broad topic.

Data science covers a large breadth of material and this book does a good job at explaining the beginning and ends of it, without going into great detail. I also give this book great props for writing in the most layman's terms possible. This make
Mar 27, 2016 rated it liked it
Roger Peng and Elizabeth Matsui have attempted to summarize the art of data analysis, an art that is not well documented. They did this so effectively that they made it look easy. Their organization of the topics is perfect but at times they were too repetitive for my liking. I hope they improve on this in future editions.
Mar 16, 2016 rated it it was amazing
Shelves: data-science, science
This book gives a very good explanation of the process of doing data analysis. Not how to use tools or particular algorithms but which steps to carry out and why. Full of examples.
This book is full of information that is hard to find in other books. Highly recommended.
Jan 08, 2017 rated it really liked it
Shelves: nonfiction, data
Quick read, but good overview.

I liked the emphasis on how data analyses, while appearing linear in nature, are really the product of an iterative process.
Dec 25, 2015 rated it really liked it
Well written book, guiding you through the process of data analysis. Serves as a good review of the first few classes from the Data Science Specialization at Coursera (taught by Dr. Roger Peng).
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Jan 26, 2016
Pavel Klammert
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May 28, 2016
Carl Schroedl
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Sep 19, 2015
Bárbara Barbosa
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Jan 02, 2017
Eduardo Vázquez
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Jul 07, 2017
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