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· 245 ratings · 34 reviews
Start your review of The Art of Information Science: A Guide for Anyone Who Works with Data
I'm somewhat ambivalent regarding this volume -- I very much appreciate the pragmatic writing way, and at that place are some genuinely useful pieces of communication contained inside. Nonetheless, the target audience seems cryptic. The best fit seems to be folks who are intending to have the JHU information science classes, and retrospectively this looks like information technology would be a very handy companion guide to the course. Having taken them, however, along with a number of other statistics/data assay type classes, much of t I'm somewhat ambivalent regarding this book -- I very much capeesh the pragmatic writing style, and there are some genuinely useful pieces of advice independent within. However, the target audience seems ambiguous. The all-time fit seems to be folks who are intending to have the JHU data scientific discipline classes, and retrospectively this looks similar it would exist a very handy companion guide to the course. Having taken them, however, along with a number of other statistics/information assay type classes, much of the content seems likewise cursory in review. ...more
The authors share their feel 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 information analysis projects.
three.5. A skilful overview for someone with some practical experience in analytics who wants to meliorate understand the data analysis workflow.
I wouldn't recommend this to someone who isn't an advanced or works equally a information scientist.
Although I am pretty sure it would be likewise practiced 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 information scientist's listen.
It discusses the major steps taken to complete a task and how to judge every footstep you lot take.
It intro
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 likewise expert for some one who Is/Does.The book explains the untaught process, or lets say the unspoken process of a data scientist'southward job.
It explains the things you wouldn't read in a book of statistics, information technology explains the thought procedure taking place in a data scientist's listen.
It discusses the major steps taken to consummate a chore and how to judge every step you lot take.
Information technology introduces the so called Epicycle and how it works.
But I will take to say this, If you don't work as a data scientist I think part of this book will confuse you or even scare you away. Some parts are easily understood through the first one-half of the book at to the lowest degree, just there are lots of unclear stuff and Jargons that volition leave you really confused. Some examples where legit others again need manner more avant-garde knowledge not because they are difficult to understand simply i felt they were a scrap unclear or directed to allow'due south say a "data scientist"
Again I am only maxim that If you think this book is an introduction to data science in whatever kind then you might get a little disappointed.
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3.five stars rounded upwards. in essence a more polished version of executive data science, and a lot my review of executive data scientific discipline as well applies to this volume, although 'the fine art of data science' is more than gear up for public consumption. still is based on a procedural frame without much caption for why you'd want to have the steps in the procedure largely. i think this is the kind of matter that lost undergrads volition capeesh a lot, but information technology'southward pretty sloppy about estimation formalisms and this will le 3.v stars rounded up. in essence a more polished version of executive data scientific discipline, and a lot my review of executive information science as well applies to this volume, although 'the fine art of data science' is more set for public consumption. still is based on a procedural frame without much explanation for why you lot'd want to take the steps in the procedure largely. i think this is the kind of thing that lost undergrads volition appreciate a lot, but information technology'due south pretty sloppy near estimation formalisms and this will leave those with advanced preparation somewhat disappointed imo. additionally the framing alternated betwixt "the audience/context/system receiving your analytic production is constantly nowadays and influencing your choices" and "you touch base with the audience/context/system receiving your analytic production intermittently merely basically are on your own and merely need to produce correct piece of work," and i feel that at that place is a lot more than meaningful exploration that could exist done in this vein of thinking ...more
This excellent volume takes you lot through each step of a typical data scientific discipline project giving general advice, warning about common mistakes and giving many applied 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 projection stages. Definitely a book to read multiple times.
This volume is very expert to obtain a big motion-picture show about what is data analysis. The nigh important lesson that I learnt from this book was that a data analysis starts with a question, not with the information and at the finish of the twenty-four hour period it leads to another (better) question. The challenges in data assay processes are described very well. Various types of question and types of data assay are explained.The book is very good for beginners, also it can be used by sophisticated data scientists.
I work with information, but I guess this book isn't for me.I got the feeling that is was addressed to the experienced data scientist and non someone that wants to understand a fiddling bit more nearly information technology. 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 reply my questions. For sure I didn't learn the "art" of information science.
I work with data, simply I guess this book isn't for me.I got the feeling that is was addressed to the experienced information scientist and not someone that wants to understand a little bit more well-nigh information technology. 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 scientific discipline.
The book was interesting and well written just didn't really answer my questions. For sure I didn't learn the "art" of data science.
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This book equipped me to respond all the questions I have in my data analyst life, specifically the "why am I here?" and "what is my purpose?" blazon ones. In my work, there are a lot of technical resources for information analysis tools but not a great deal of guidance on method. This book is exactly what I was looking for to make full that gap. This book equipped me to answer all the questions I have in my data annotator 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. ...more than
It'due south a practiced volume for anyone who wants to know more than nearly information science and data science analysis
In this book, Roger D.peng showed the entire procedure of data science analysis :
one-stating a question
2-EDA (Exploratory data analysis)
3-Using Models & Expectations
4-inference and prediction
5- Interpreting Your Results
half dozen-compunctions
It's a expert book for anyone who wants to know more about data science and information science analysis
In this book, Roger D.peng showed the unabridged procedure of data science analysis :
1-stating a question
two-EDA (Exploratory information analysis)
iii-Using Models & Expectations
4-inference and prediction
5- Interpreting Your Results
vi-compunctions
...more
This is an introductory volume on how to recollect analytically and some of the terminology that goes forth with it. It's practiced for learning how to speak data science and information analysis, but it won't go much further than that. It's helpful for touching up on your power to recall analytically, refresh on terminology, and hone presentation skills. This is an introductory volume 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 information technology won't become much farther than that. It'due south helpful for touching up on your ability to think analytically, refresh on terminology, and hone presentation skills. ...more
Basically a brusk enquiry methodology volume utilizing data science. If you're already familiar with the former topic, there'due south quite a lot of filler and it's a short volume in the offset place. If you're not, it'd be a decent intro to the topic.
Basically a brusk enquiry methodology book utilizing data scientific discipline. If you're already familiar with the former topic, there's quite a lot of filler and it's a short book in the get-go identify. If you lot're not, it'd exist a decent intro to the topic.
...more 
In that location is a lot of helpful information pertaining to all steps of performing a data analysis. The information is well written and clear with lots of examples to help with understanding. Will be a skillful source for reference.
Great volume for those new to information science. The illustrations and examples were apt.
Gentle introduction for those who are searching for more knowledge on data science in general and information mining in item.
Not recommended for experienced data scientists, information technology is besides basic.
An informative book on the process of information analysis from start to terminate.
Really clear with helpful examples. Good introduction for budgeted analyses from an cocked perspective in elementary language
Elementary and curtailed volume for anyone who is simply starting their journey into Information scientific discipline field.
Interesting and a great first approach to data science
Bang-up read on basic data analytics. Walk through of thought process. very useful
Quite interesting framework, although too simple for experienced data scientists
This is an splendid volume that breaks downwardly 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. Equally a information annotator, I enjoyed this book. I retrieve it would be a smashing read for people who piece of work with analysts but don't have a clear view of what the piece of work entails; I believe it would ameliorate their appreciation of the creativity and circuitous processes carried out by the analysts wit This is an splendid book that breaks downward the steps of data analysis. Many of these steps are carried out intuitively by information analysts, and information technology was enlightening to have them identified and put into context of the process. As a data annotator, I enjoyed this book. I think it would exist a great read for people who work with analysts but don't have a clear view of what the work entails; I believe information technology would improve their appreciation of the creativity and circuitous processes carried out past the analysts with whom they collaborate. ...more
I read this in South Africa when information technology was recommended reading during the beginning week of a data scientific discipline course. While it'south hard to know for certain, I believe that the perspective I gained from information technology contributed towards my success in the course. I learned the most most all the non-science parts of a data scientist's job, and this was valuable to me afterwards as I began to accumulate some of my earliest work experience in a information-science-similar role. I read this in Due south Africa when it was recommended reading during the first calendar week of a data scientific discipline course. While it'due south hard to know for certain, I believe that the perspective I gained from information technology contributed towards my success in the class. I learned the almost about all the non-science parts of a data scientist's task, and this was valuable to me after as I began to accrue some of my earliest work experience in a data-science-like role. ...more
Groovy book with awesome examples. There are quite a few errors in grammar and spelling, but they practice not subtract from the value of the cognition. Very useful frameworks to understand and utilise to data analyses.
I did feel the last 3rd part was a little rushed, and like it is somewhat incomplete, but overall the content was really expert and useful.
This is a light information science volume. I have mixed feelings towards it. Sometimes, Prof. Roger writes assuming some prior cognition and feel (e.g. he wrote some parts freely assuming some knowledge about distributions, significance, ..) and those are the parts I actually liked in the book. On the other mitt, prof. Roger sometimes writes as if the audience are total newbies with no previous experience.Leaving the above annotation aside, the abstraction Prof. Roger introduces about the procedure of data
This is a calorie-free data science volume. I accept mixed feelings towards it. Sometimes, Prof. Roger writes assuming some prior noesis 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 near the process of data science / data assay will probably exist agreed upon by everyone. It probably won't add much to experienced people (every bit information technology is probably a second-nature) except when they start introducing data scientific discipline and data analysis to others, and for this reason, i recommend this book. The abstractions and the examples will brand introducing someone to the field a much organized process.
The book is highly accessible, enjoyable, and short. You won't invest much in reading it (time-wise) and you will get decent output from it.
Disclaimer: I read a pre-released version from leanpub
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As the volume mentions, "the volume continues to serve as a useful resource later on 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 fabric and this book does a good job at explaining the start and ends of it, without going into great detail. I too requite this volume nifty props for writing in the most layman'due south terms possible. This make
As the volume mentions, "the volume continues to serve as a useful resource subsequently you're washed reading it when you hit the stumbling blocks that occur in every analysis". The book is what it sought out to be, a adept reference material to a very broad topic. Information science covers a large latitude of fabric and this book does a good job at explaining the commencement and ends of information technology, without going into keen detail. I too give this book great props for writing in the most layman's terms possible. This makes it piece of cake to understand for newcomers of the field.
...more 
This volume was a mixed purse for me. About of the time, I felt he was explaining bones research approaches (developing a hypothesis, exploratory data assay, and especially the last affiliate in presenting your findings). Other times I would get lost in his examples particularly those without visualization. Overall, the volume focused heavily on data analysis, and I think information science as a field as well encompasses data extraction or collection, direction, and virtualization. I would have liked to learn m This book was a mixed bag for me. About of the fourth dimension, 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 assay, and I think data scientific discipline as a field also encompasses information extraction or collection, direction, and virtualization. I would accept liked to learn more than nearly those topics. ...more
Roger Peng and Elizabeth Matsui have attempted to summarize the art of data analysis, an art that is non well documented. They did this so effectively that they made it look easy. Their organization of the topics is perfect merely at times they were likewise repetitive for my liking. I hope they improve on this in future editions.
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