laptak2003/AmazonDataScience huggingface dataset:
The book is an awesome refresher for basic statistics. I particularly like the way that the book outlines the connections between data science and statistics.
Great review while finishing my master's. Focuses on the statistics you actually need.
This book gets right to the point, and explains relevant statistical concepts in straightforward, easy to follow language. It is exactly what I was looking for. I was glad that the authors started at the literal beginning, but the pace and efficiency of writing allow the reader to come up to speed quickly. Well done.
Reads super easily, and doesn't dumb it down to the point that I don't feel like I'm not getting anything.
Content is well organized and easy to digest.  Good prob/stats refresher prior to starting ML/Data Science studies.
Excellently written book on those looking for modern statistical data applications. The book provides open source codes so it is accessible to all.
The book explains all the topics in good detail and also details the implementation in R or Python. But, I suggest that People who has basic knowledge of statistics to buy this book or few concepts might be confusing.
While this book seems to cover the bulk of important topics for data science, there is a lot of hand-waving and relegation of explanation to external documents for readings in order to comprehend topics.  I would recommend the book if you are generally interested in learning about data science, but I would strongly recommend pursuing the deeper dives necessary to actually learn the subject matter.
I think this book covers a great and useful set of topics for a data scientist to know. The problem is that the author does a poor job explaining many of the topics clearly. So, I often feel the need to read about every explained concept online or watch a youtube clip to understand it better. The definitions, explanations, and examples are sometimes good but often rushed. You need this book to know what you need to learn, but then you end up learning those things elsewhere, and not from this book.
The book is great as supplementary reading. It does not teach how to do this and that, and is not an exercise in math, and does not teach basic stuff. But it provides a great alternative look at statistics in general.
The text provides a good foundation for exploratory data analysis(estimates of variability, univariate, bivariate and multivariate analysis), data distributions, sampling methods, statistical experiments(A/B testing, chi-square, ANOVA,..) and statistical machine learning models.Anyone with exposure to Machine learning without explicit exposure to statistical machine learning may find a significant portion of content in last few chapters as familiar.The only drawback I felt was the text could have gone deeper in few concepts and may be included distributions such as Dirichlet distribution which is of high importance for topic modeling.Nonetheless, I feel this is one of the must-have for an Aspiring/Working Data Scientist since it can work both as a reference and an interview prep. Thanks to the Authors for making learning easier.
Concepts are not explained fully and actually leaves you to hunt answers elsewhere. It tried to be the bridge in between data science applications and a classic statistic book, but actually fails at both. It's not terrible, but would not recommend.
Concepts in statistics are very well organized in relationship with machine learning context. All the computer scripts are in R which may be losing its popularity in data science community over python, but that is not the point.Highly recommend especially for people who are preparing DS interviews. Any question in statistics (+ a little bit of ML) interview session can be covered by this one single book.
This book is good as a reference and is easily readable even for those who are not expert data scientists or statisticians, but it leaves a lot of information out. The book directs you to other books written by the authors if you want more in-depth information on a given topic.
Easy to read book to bring your stats up to speed with current best practices, without the weight of all the theory or massive code dumps.  Sufficient references are provided for each topic if you want to dig deeper.  You don't have to know, use, or love R to benefit from this book!
Misleading title.  The book is more about applying Python to statistics than a book on statistics for data scientist.None of the procedures described require more than a good usage of Excel..Change the title...The content is not bad but basic. Let's say badsic....;-)
I really like this book.  It covers a lot of ground without being a lot of pages.  Also, they remember to mention the practical things, e.g. some differences in terminology across statistics vs. data mining fields.  Excellent review book, for example.
Nowhere on the front cover is R mentioned; however, in the preface it is stated that "this book is aimed at the data scientist with some familiarity with the R programming language."
Made the concepts easy to digest
Great book! Wish it was using Python
clear and easy reading
Topics are very briefly explained. Too brief to give a reasonable understanding.
Students learning statistics with data science can have basic knowledge and techniques about statistics in data science field.
My friend even bought it after me
Very well written and approachable.  Highly recommend if interested in using statistics in a practical work setting.
I like how concise this book is.Meanwhile ,it  gives you enough knowledge to be familiarized with a lot of concepts  data scientists  must be aware of.
I bought this book for $13 an it has been a great read. Numerous major concepts required for a data scientist interview have been covered in this book. If you ask me, it's worth every cent spent on it. I gifted a second one to my friend who is in a Data Science program.
The Book is good for people how to use R to analyze data.
Really enjoy this book for my first data analytics class. The author explains the statistical concepts and how they may not be relevant to data science applications. I also like the key concepts summary after each chapter section.
Excellent book. I use it daily in my data science activities!!!
It is great one !  It is practical !I like it
I wish I returned it. It did not have anything useful.
Still reading it. There are some typos and references to data sets in the figure that do not exist. But overall the book seems very well structured. Still, definitely only for beginners.
Quick summary of useful statistical concepts for my real world job. Examples in R are easy to understand without prior R coding experience.
That book is good for a newbie Data Scientists because it teaches you the statistics in the human words, and mainly covers the parts of statistics which are needed in Data Science.
Its a great book to start data science because they introduce you on statistics, R (program), libraries and concepts (statistics applied).I totally recommend this book.
Very helpful book
This might be a good read if you have taken a stats 101 course a long time ago and need a refresher, but donâ€™t expect to be an expert by the end. A lot of concepts arenâ€™t explained in detail, and itâ€™s very light on the math.
[Note] please note that this review is about the quality of the book printing, NOT the content.I have bought books from O'Reilly before and am generally familiar with the good quality of their book printing. After opening this book I immediately noticed the overall appearance of low-quality, photocopy-like print. Such appearance is especially observable in the plots throughout the book. As examples shown in the photos attached to this review, one can clearly see the "checker board"-like printing artifact. Such printing artifact reminds me of what happens when one tries to photocopy photocopies too many times.
Not very serious.
I have an MS in statistics, and work in bioinformatics, so I already have a reasonable background in many of the important concepts in statistics and data science; nonetheless this was an enjoyable and worthwhile read.  This book takes the reader through a very wide range of concepts, at a nice depth - shallow enough that the book is quite readable, but deep enough to impart understanding and a fresh perspective on areas already familiar to me, with suggested further reading for each section.The 50 concepts are split across seven sections: 1) Exploratory Data Analysis; 2) Data and Sampling Distributions; 3) Statistical Experiments and Significance Testing; 4) Regression and Prediction; 5) Classification; 6) Statistical Machine Learning; and 7) Unsupervised Learning.  I was surprised how well the contents of the book fit in with what aspects of statistics I actually require for work (as opposed to what my MS taught me) since it really does cover most areas I use.Code included is generally quite short and succinct, aiding clarity and making the Github repository (scripts were committed in June 2017) largely unnecessary. Personally I like to type out code to try as I think I learn better that way, rather than copying and pasting.There are a few subject areas, such as neural networks, that I am aware of but don't yet understand and would like to have been discussed, but it was nice to see a text that actually does cover 'modern' concepts like boosting and bagging as well as the more traditional; on the whole I was very happy with the selection of topics covered.  It is clear from their approach that the authors have insights from experience into which areas are practically useful.  I particularly liked that the book includes a sizable discussion of diagnostics and has frequent illustration of making real inferences from data.
This convenient book provides a helpful summary of many of the common statistical and analytic methods commonly used in data science.  While not mathematically deep, the book does an excellent job providing a background on a wide range of methods, and of providing a big-picture overview of when and why it would be appropriate to choose various methods.Given the surface level overview of the topics, the book is well-suited to reading a section a day on a kindle, so as to gradually enhance one's awareness of a wide range of analytic techniques.  In addition to its approachability, the book provides many references for those who want to dig deeper, and hence serves as an excellent introduction to the statistical techniques commonly used in data science.
Awaiting the GitHub link associated with the book to be updated. Currently the link returns empty page. Thanks !
This is the kind of book that every aspiring data scientist should have on their book shelf. It is also a good review book for data scientists with formal statistics education.Very clearly written and authors knew what they were writing that data scientist beginners should know. For example, inclusion of concepts like A/B Testing, Resampling etc.The practical examples and R code makes it rich. github repo is available now. This book also contains the "Further Reading" sections which are very important.  The book title says "... 50 essential concepts" but I clearly see lot more than that, which is bonus and fun to read :-).I love this book and recommend it.
Just Read the first chapter. Looks to me as a compilation of articles from various resource on the internet. Even worst is the fact that when i checked the Github for source code, it is empty. Just one of those books that wants to capitalize on  Data scientist. As i read the rest of the chapters i will see if my thoughts change.
Book website does not have sample code/data.
I find my book, my roommate stole it. I reported this thing to the house owner sorry to bother you so much.
Really love this book!! It makes some statistical cocepts so easy to understand!! Definitely a must read if you lack background of statistics.
I like the collection of definitions and formulae. This will definitely be my go-to book for them.
Great book! I highly recommend it
Nice easy reading and lots of information
very useful book! If you wanna be a data scientist, this is ur start
Best source that contains most statistical information in one single book.
Contain the information that I needed
This book is superficial. For full disclosure I'm only on page 90 which is in chapter 3, but I don't think it will get better.Basically it summarizes concepts at a very general level to the point it's not useful except to show you what you need to go buy another book to learn.So far it's style has been to give a couple paragraph overview of each concept, followed by giving us several references to check out in order to learn more. If you intended to use any of the concepts you would definitely have to read about them in more detail elsewhere.If someone is talking about stats to you and you need to be familiar with the terms  this books would be helpful,  or it could be good if you want a high level look at how stats concepts fit together before diving in to learn concepts at a usuable depth of knowledge. That could be pretty useful.In it's defense, after more carefully reading the back cover I  think this is what the book intended to be. it doesnt intend to teach stats or intend to show how to use stats in datascience. It seems to want to be a glossary of stats concepts from a data science perspective. It definitely could have made more clearer.
SUPER DANK!
Information seems plainly written and relevant. No link to datasets makes the "practical" code portion of the book unusable. Will happily update my review when the datasets are released.EDIT:Ok the datasets are up. There is a short R script to run to download the data, it will require some small modifications to get it working correctly.You need to create a folder named "data".and I changed the second line in the script from:PSDS_PATH <- file.path('~', 'statistics-for-data-scientists')to this:PSDS_PATH <- file.path('.')This will download the data into a folder named "data" in whatever directory you run the script. The script runs with no real feedback and some of the data sets are large, so just be patient. Once these were downloaded the examples in the book run great.
I like the book and I like the authors' writing styles. They make dull and dry statistical concepts easy to understand and remember. The only thing (and also most important thing) I don't like about this book is the misleading instruction to download examples and codes mentioned in the book. In the very beginning, the book directs you to a GitHub page to download "codes and examples" but when you go there, the only thing you will find is a readme.md file with only one line of text. I feel cheated and will wait for the authors to update their GitHub depository to update my reviews.
very useful and practical..
I've done coursework and worked professionally in machine learning for a few years now, but my background and focus has been in the engineering side of things.  This book gave me a perspective on the statistical approach to machine learning, including the difference in terminology between the two fields.  It was great for filling in gaps, and for presenting different approaches to stuff I already knew.  My only requests for the next edition are:* Include a discussion of neural networks* Discuss the curse of dimensionality* Include Python code
Perfect for seeing the difference in the way of thinking between practical data scientists and (old time) statisticians!  The best sections are the ones showing tools which are basically â€œone size fits allâ€. This book showed me that i don't need to know all the tiny details of many topics in statistic books as long as you know the basics.
Good for beginners on R applications
It is a great help for a data science programmer. The code is in R, and I hope there will be a Python version.
Covers the most important topics in data science with easily understandable explanations. Great book!
This book completes my code-is-there ML trilogy with the other two be 'an introduction to statistical learning' by James et al. and 'the elements of statistical learning' by Hastie et al. They share my favorite style: theory is first presented and then code for your to execute the theory.The book did not follow a classical receipt for introducing data science. Instead, it groups topics considering their impact on real business environment. It is nice that the author with rich experience frankly share what are the impactful knowledge in business data science!While this book can well be a textbook, it definitely can also be a good reference or as a very readable book for outsiders to know about business data science (potentially have somehow coding!).
Reading this book makes me re-evaluate my knowledge about statistical and machine learning models. I have taken dozens of courses in statistics and machine learning at the graduate level. However, this book makes me realize that applying these models in practice is way more difficult in some sense, than figuring out the technical stuff on paper. Models, in theory, make all kinds of assumptions, and when they are applied in practice, one may see surprising results than those presented on paper.Matt's background in both academia and industry makes him the perfect author to write such a book, that bridges the gap between theoretical models and implementations in practice. There are so many tricks and notes that I appreciate, which can save me tons of time if I were in a position to implement those methods. I also like the in-line code a lot.
The approach is practical and effective.  It weaves together concepts in business, economics and statistical analysis (via R language) to arrive at a multi-view perspective in business decision making using data.
2 chapters in about Experiemts and Controls are worth buing the book. It's first so accesible treat on Orthogonal/Double Machine Learning, e.g. Causal Tree, Generalized Random Forrest. Highly recommended, for everyone willing to use data science to support decisions, not merely forecasting.
Strikes the right balance between being practical for a (relatively) broad audience, and having enough depth to teach the reader techniques beyond the basics (and pointing the way to further reading).There is a focus on the subset of data science that overlaps with (or is adjacent to) Econometrics, in case that is/isn't what you're looking for
This book is definitely one of must-have in your bookshelf if you want to keep a board horizon about the application of machine learning in economics question or want to find a decent solution for the business problem you ran into.
...if what you have been waiting for is an approachable volume on the tools raising the bar for business decision-making.
I am a current student at Booth who didn't get a chance to take Prof. Taddy's course, so I was very excited to dig into this book.  It was mostly what I was hoping for, with a few omissions.First, this is essentially a book on applied statistics from a business perspective, so exactly what you'd be hoping for if you're looking to utilize data science principles in real-world scenarios.  The complexity level of the book is absolutely beyond that of a complete beginner, however.  Familiarity with certain basic mathematical concepts is assumed, as well as a degree of proficiency with R.  I have definitely seen more approachable texts,so if you're a complete novice you may want to start elsewhere.  On the other hand, I don't think this is a book for 'experts' (at least based on some of the physics texts I've worked with in the past).  As the intro says, this is based on a curriculum for MBA students (I'd argue its really for more quantitatively-inclined MBA students), and I think it does a good job balancing on that fine line between novice and expert.However, once you get into it the book is really very detailed, with a lot of good examples.  I've only started my journey through the book, but so far I haven't noticed any glaring issues (nor would I expect to see any, to be honest).The reason I'm deducting a star is the machine learning section - I was really hoping for some useful practical examples of machine learning, convolutional neural nets, and similar.  However the chapter is more of a summarization of some of the research out there and gives a high-level overview of the terminology and actual applictions of AI.  Given that Coursera offers a really approachable course on this whole topic, I was expecting more out of what is essentially a textbook (especially one that advertises machine learning on the cover).If you're looking for a good mid-level work on big data concepts and applied statistics I think this is a great book to pick up (especially as its not priced like a textbook).  On the other hand, if you're looking for depth on ML, CNNs, AI, and similar then you'll want to look elsewhere.  The concepts this book covers are important theoretical background for ML/CNNs/AI, however if you're not a researcher/expert you can certainly get into that space without the depth presented in this book.
Overall, this book is really well done. I definitely recommend it, and certainly recommend it over other, lighter-weight intro to data science books.The author includes the (important) mathematics behind a lot of the statistical intuition and machine learning that's vital to data science, and does so without producing a dry textbook.I also liked how the author included caveats about the shortcomings of the various approaches as well as suggestions and solutions for how to best accommodate the issues that they might introduce.My one (minor) complaint is that the sample code in the book is in the R language. That's fine -- R is still a very common language in data science, and if you work in this field you'll certainly encounter it -- but I would have preferred Python, both for ease of reading and because Python is fast emerging as the dominant language.
He has many examples of code at work so you can catch a glimpse of what it's capable of.  Leafing through the book, it looked overwhelming and unusable.  Putting time into it, it starts to make sense.  It helps if you've meddled in R or Python.He goes into great detail about how information can be deceiving and making a solution that can accommodate changing and unknown inputs is rewarding.This book took me a long time to get through, but I'm sure it pales in comparison to the amount of time it took to write.  Nearly every page has algorithms that all would take time to be constructed and tested for accuracy, then explained for their uses.  It's worth every penny.Highly recomennded
Back in my days as an Analyst at a huge corporation in the 90's and early 2000's, I spent my 9 hour days making endless spreadsheets and doing pivot tables a billion different ways to deliver accurate reports for our Senior Executives on a daily, weekly, monthly, etc. basis. One report took so long to filter through the data, I began calling it my "8 hour report" because it literally took me all day to create that particular one each week, between pulling and analyzing all the data in that manual manner. At the time, that was the best we could do. Those days are long gone, and a LOT has changed since then - and most things like that are automated by software these days. I was really curious what businesses are up to these days in terms of data science, and was familiar with the concepts of this book - even though vaguely in some areas - and this really filled in a lot of blanks for me.My oldest son is a computer whiz who is ahead of his time, so I mainly got this book for him - even though it is over both of our heads in many parts. That said, this has invaluable information for anyone looking into Business Data Science, as it combines the best of science and engineering concepts to merge into a business context to take it to the next level - and THIS is what is in demand these days, and on the cutting edge of successful businesses.I like how it outlines the primary parts that make ML work, and how it shows where AI can also come in to help your business. I also like that it shows real world examples on how these things can be applied, especially with A/B experiments and applications of text analysis.There's a lot in here regarding scripting in R, frameworks for cloud computing, and a lot more to help you improve how you use your business data effectively for the best success. It's like a great mash-up of economics, computer science, statistics, etc. that balances well, and it gives you a leg up in your business decisions. While I don't pretend to know everything in this book, it really has a wealth of info and direction for me to dive more thoroughly into, and I think my son will greatly benefit from this book, as well. It certainly gets two thumbs up from us!
As a grad student in business analytics, I'm always on the lookout for a great reference book to keep handy. This is a very comprehensive guide - I would imagine it could be used as a textbook in a broad survey course. If you do not have a background in statistics, linear algebra, and/or computer programming, it would be better to start with an introductory text. This book provides excellent practical examples of how to use data science tools in the real-world. It is easy to get bogged down in theory and miss the (random) forest for the (decision) trees (...that was an attempt a joke, ha...). The examples in the book are in R.
I was pleasantly surprised that despite the textbook amount of examples and details the book was easy to follow and had plenty of depth to the sections on business economics and the statistic behind it. My only complaint is the section for machine learning, AI and neural networks is a little higher level than I was hoping. It does a good job with some examples as well as a mid/high-level approach. If you are looking for a deep dive into these topics as I was you will be a little disappointed and will need some supplemental material for that.
This is an extraordinarily good text that is utterly comprehensive in giving you everything you need to know about the science of Big Data.  What is also amazing is that such a comprehensive text is priced so inexpensively!  As far as I am concerned this is the greatest value for your hard-earned dollar of any book published in the past 12 months.  My headline says it all:  Seminal.
Covering a lot of statistics, with snippets of R code and a touch of Python, you will get a good overview of business oriented data science in this book. A caution - if you are not reasonably strong on your stats before you dive into this book, you will quickly become befuddled. I've got 12+ credit hours of stats and found the book a challenge. Having said that, it covers the basics well and touches on pretty much everything from simple linear regression to machine learning and AI. If this is your discipline, it's a good intro.
"Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions" by Matt Taddy is well presented. The ideas and concepts are easy to follow with good flow. I'm giving an extra star for easy to read text and graphics. I wouldn't say this is an accessible read. It is clearly meant for those who actually use data in a scientific manner. That said, the author does a good job of presenting the topic in a clear and concise manner.
Very useful for managers or others responsible for implementation oversight. This provides a solid introduction and overview with in depth examples and explanations. Not exactly the most exciting topic but well organized and relatively reader friendly.
Matt does a great job at balancing practical applications of data science and theory.He gives the practitioner cutting edge tools and places a strong focus on advances in casual inference where a lot of other accessible resources choose to ignore.  I really found the section on orthogonal machine learning to be worth the value of the book alone.There's less focus on hype here (i.e. deep learning) and more focus on applications that can drive immediate value to businesses today.  Highly recommend!
Many books like this usually miss the target of being practice and useful for 'business', but this one does a very good job of weaving the various concepts and ideas together.
Just ordered my copy express Amazon prme.Can't wait to get my eyes reading the much anticipated book. Super excited
Edit:I now have the book and obviously the equation problem below is absent. To give full credit to the scientific content over the Kindle issue - this is an extremely interesting book that presents a bigger technical context around ML, as well as presenting main themes in ML. I don't know any other text that frames the science/math in this way and it's  clear and well-explained and gives plenty of food-for-thought. There are accompanying Youtube videos, so one can switch between the book and videos. Highly recommended.---This book has superb and interesting content, but the equations are tiny both on Kindle For Mac and on Kindle on an iPad. I estimate that the equation font is half the size of the main text font / the equation number at the end of the line, which I doubt is intentional. I persevered with it for a day but, given there are many equations with subscripted variables, and summations with indices at top and bottom etc, it was a painful reading experience and I returned it.---
This book is an absolutely spectacular introduction to the state of the art in modern data driven engineering. This field is rapidly taking center stage with the ubiquity of data-gathering devices and the exponential increase of computation in everyday workstations.The aspiring engineer and the experienced professional have something to learn here. This book is unique in that it provides easily adaptable code and conceptually accessible explanations for the beginner, while also depicting advanced methods to tantalize the more advanced researcher.Definitely requires a working knowledge of linear algebra and some real and complex analysis to fully benefit, but the corresponding YouTube lectures make this easily accessible for the advanced engineering undergraduate, or graduate in other fields.
The book is really full of neat explanations and uses a clear language. The mathematical foundations are very well tackled and the code is well commented and explained. It will be much better if the codes were written in Python but most of them can be easily implemented in Python.
The book is written in a good arrangement. It gives the clear mathematical background and the practical Matlab code for some important topic in machine learning, dynamical systems and control .The book is very useful for the students who want to learn the data science. I suggest it to everybody.
The authors have a background different from other ML books I've read that I find enlightening. Great book.
This book has a lot of great examples of data science applied to engineering modeling and control.  The code in the book is in matlab, but the authors have python code on their book website.
The book is intended for research purpose. I believe it will meet my expectations.
This book complements very well the author's Youtube channel.Each concept is clearly explained and usually followed by examples or implementation techniques. I knew some of the concepts before from my classes, however, reviewing them using the book brought new insights into the topics, as well as pointed to some interesting bibliographic references. Definitely worth buying.
Pros:*Very accessible reviews of machine learning methods*Summarizes authors own work related to applying these to physics*The accompanying code is very helpful*Focus on handling real world cases versus the math.Cons:*The most serious issue with this book is that it is filled with typos which are very serious. These are not just missing factors of 2. These are fundamentally incorrect statements of theory. The statement of the PBH theorem on page 289 (and the online youtube lectures) cannot be correct. You can see this by trying 8.30, since it will tell you the system is not controllable when it in fact is. These types of serious issues result from the authors unwillingness to prove most of their statements besides with a handful of matlab examples per subject.*Very focused on the authors (admittedly very impressive work) rather than the whole field. For this reason I consider it a review of their work, rather than an introduction to these methods in general.*Minimally discusses it in the context of model selection/Bayesian learning.In the end I recommend this book, but ultimately would recommend the classics Numerical Recipes for growing scientists.
Hands-down one of the best books on Machine Learning. Very clear, specially when you watch the video lectures next to it.
This book teaches you the best practices of data science and machine learning based on real world case studies. I found this highly valuable because you are able to actually work on real data sets. This is also a quick way to learn industry recognized tools and mathematical concepts that are actually being used by data scientist. Another advantage of this book in my opinion is the author's approach for coding. Author writes and explains each code and outcome separately rather than giving you several paragraphs of code and explain them all at once. I strongly recommend this book if you want to learn data science and machine learning on a practical level applying code and assessing the outcome
If you already have some (minimal) python experience under your belt and want to have a detailed walk-through of a Data Science project, this is the book for you. Steve goes in depth on the what, why and how when it comes to exploring, cleaning, modeling and presenting a project in a business setting. He does a very good job of explaining what exactly is happening in the code used throughout the book. This really made the book great for a beginner/intermediate data scientist like myself. These explanations really helped me get a better understanding of pandas and scikit-learn. The best part about the book is that he goes into detail on the Machine Learning process and explains how the models work without getting too "mathy." There is some math at some parts, but hey, its ML and this was a great intro to the math and intuition of some important ML algorithms. One final reason to buy the book: I had trouble with some of the code in the book due to unknowingly having outdated versions of the necessary packages, so I emailed Steve directly, half expecting to never get a response. However, Steve responded with a very detailed explanation and code to help me get my code running. It was great. Buy this book if you want to learn how to work through Data Science Projects in Python.
Written in a very understandable fashion. Takes the reader through real life data problems in useful ways.
I liked this book better because it broke through some other books' lectures and abstracts and dove into the kind of data and scenarios that  I am more likely to actually encounter in my job, rather than just memorize them. Plus I didn't have to fix or workaround outdated or outversioned python code as I have had to do with some online teaching forums. This book will move your career or business forward.
This book is great because it explains concepts well and uses real-world examples. However, the only machine learning free part was the first chapter.I would recommend this book to someone who would like learn about machine learning, not data analysis or visualization.
The book is very well written and author did a good job explaining every line of codes and concepts. Worth every penny! Thank you!
As a professor at DePaul University who teaches data science and machine learning, I can say that this is a great book for introducing the fundamental concepts that lie behind using Python for data science projects. Readers will learn useful coding skills in Python, and its various packages for data manipulation and visualization such as Pandas, Numpy, Matplotlib. Furthermore, they will learn how to use Scikit-Learn, one of the major data science toolkits in Python, to construct machine learning models based on the same data.  The book is well laid out, with each section building on the last, and reflects what actual data scientist do in the field day-to-day.The book provides a great platform for anyone who is interested in learning practical "how-to" skills, and creates the foundation for those who want to move on to more advanced concepts.
As someone who has managed multiple data science projects in academia and the business world, I found this book to be a much-needed introduction to practical data science in the real-world. Some books thoroughly cover the mathematical complexity of machine learning models while others focus on implementing the models through coding (e.g. Scikit-Learn, Tensor flow, etc.). However, it is rare to find a book that ties the math and coding together to provide a comprehensive take on the data science process, which includes much under-appreciated topics such as data munging, exploratory data analysis, model evaluation, etc. Nevertheless, the author also does not skip out on explanation of the mathematics of the machine learning models and treat them as â€œblack-boxes,â€ which can be frustrating for many readers who need more depth.This book is ideal for individuals with some familiarity with Python and limited mathematical background. It does not include the latest, cutting-edge deep learning models. However, having a robust process of understanding the data and evaluating models is more critical to the success of a data science project than applying the latest, most sophisticated models coming out of academic research. In this regards, the author does an excellent job of walking through its reader step-by-step in building a robust pipeline process using real-world data science projects as examples.Chapter 6: Imputation of Missing Data, Financial Analysis, and Delivery to Client offers a good overview of the most important step in data science in the business world. You would be hard-pressed to find information in this chapter anywhere else.For experienced data scientists, this book may be too introductory, but it can serve as a textbook or a training manual for your team if you lead a team with entry-level data scientists/analysts who recently graduated from school and still need help applying what they learned from school in the real-world.My only suggestion to the author would have been to include more materials on the next steps and provide a brief survey of the latest models in data science and resources to learn about them.All in all, it is a great book for new entrants or those hoping to join the field. It also seems ideal as a textbook for short 6-8 week data science courses.
I am using advanced analytics for work, and want to horn my machine learning skills in python.  Have been shopping on Coursera and Udemy for courses but there isnâ€™t one designed for my background.  This book is a MUST Have for a experienced sql analyst who want to learn a new language.
This book is great for the data scientist looking to improve their Python skills. It covers the standard methods used in data science (as well as when to consider them) through an applied programming lens. Recommended!
As a college senior who studies data science, I found this book is super useful. This book provides a variety of real-world use of data analysis in marketing, where you can apply your knowledge in theory, something that you canâ€™t learn from class. With applications of basic simple regression analysis to advanced machine learning, this book will get you engaged to practice many techniques that are applied to key researches and analyses in marketing.Another huge thing about this book is that it is very engaging and comprehensible. With well-organized order in each chapter, the author guides you challenging materials with nice step by step explanation to almost effortlessly help you learn key functions in both Python and R languages. If you want to get your feet wet or brush up your data analytic expertise in marketing, I highly recommend this book!!
I am a data scientist at MBB and have done numerous marketing related projects. However, most of my team's approach was rather ad-hoc so I was looking for a structured way of looking at those problems. This book walked me through from start to end and covered most of the things that I needed to perform data science for marketing. I think this book will help data scientists in all experience levels; if you are new to data science, this book has a step-by-step setup and coding guide with examples. If you are an experienced data scientist, this book will help you to understand key concepts in marketing analytics as well as a general marketing strategy and know-how. All in all, I recommend this book to any data scientist who wants to learn more about both data science and marketing.
Good introduction to data science and some of the techniques to use in the real world.  I recommend reading this book.
It brings back sanity to the hype of data science. If you are in it you will appreciate the way it's written and how it approaches methods and tools.
I'm loving this book. It's been a great intro to the field
A very solid book on the subject if you are serious about the subject. Not for the casual grazer.
*Doing Data Science* is a great introduction to practical data science.  It's ideal for someone with a strong statistics background who wants to know how to apply the theory to common tasks in industry like spam detection and A/B testing.The book assumes the reader has a strong statistics background and good knowledge of R.  Those with a computer science background will find much useful information too.
It seems there are some misses in the book \emph{Doing Data Science} by Cathy O'Neil and Rachel  Schutt page 61-62 when linear regression was discussed.  First of all, in page 61, in equation $y=x \cdot \beta$, it's perhaps better to indicate  that vector $x$ is different than the $x$ mentioned before. More specifically, $x = [x, 1]$.  More importantly, the target function in page 62:  \begin{equation}  RSS(\beta) = (y - \beta x)^{t}(y - \beta x)  \end{equation}  should be:  \begin{equation}  RSS(\beta) = (y - x \beta)^{t}(y - x \beta)  \end{equation}  Then we have:  \begin{eqnarray*}  \frac{\partial R}{ \partial \beta} &=& \frac{\partial (y - x \beta)^{t}(y - x \beta)}{\partial \beta}\\  &=& 2(y - x \beta)^{t}\frac{\partial(y - x \beta)}{\partial \beta} \\  &=& -2(y - x \beta)^{t}x = 0\\  &\Rightarrow& y^{t}x - \beta^{t}x^{t}x = 0 \\  &\Rightarrow& y^{t}x = \beta^{t}x^{t}x \\  &\Rightarrow& x^{t}y = x^{t}x \beta \\  &\Rightarrow& \beta = (x^{t}x)^{-1}x^{t}y  \end{eqnarray*}
After reading few of chapters and skimming through the book, on the plus side it covers a lot of ground and has many useful tips and insights, on the not so good side, as many reviewers mentioned, it is poorly edited and sometimes the authors go on and on beating a point to death,the biggest problem for me was the missing code, otherwise I would rate it 5 stars, it is unfortunate that the code is missing from the download, I don't really understand the genius who decide these things at O'Reilly...I bought many books already from this publisher with missing code, truly frustrating.
I bought this book as I was preparing for job interviews for data scientist positions. The book is a great overview of the general field of analyzing large datasets and building models. It strikes just the right balance between mathematical rigor and approachability. Specifically, every concept that is introduced is always accompanied by a real example involving real data, so its great to see what a particular method does to a set of numbers which, in my mind, is the clearest way to understand what an algorithm is actually doing. This book also served for me as a quick primer to R, as long as you are familiar with some other package for data handling & modeling (e.g. spss or matlab) the book got me comfortable with R very quickly. Highly recommend this book to those new to the field and those who have PhD's and want something that provides a quick overview with excellent example problems!
Alas, I was disappointed with this book.  I never got a cohesive feel for what a "data scientist" does, even though this book is entirely about that.  I was fairly certain that I was stupid, until I came across all of the same topic (Linear Regression, Machine Learning, training, et al.) in a masterfully written aritifical intelligence book, "Artificial Intelligence for Humans, Volume 1: Fundamental Algorithms" http://www.amazon.com/Artificial-Intelligence-Humans-Volume-Fundamental-ebook/dp/B00HAT0APE?ie=UTF8&redirect=true&ref_=cm_cr_ryp_prd_ttl_sol_6.
I highly recommend this book to anyone with a genuine interest in data science *as a career*. What I mean by that is this book is not written as a textbook, but rather, as suggested by the title, as a guide to the actual reality of the occupation. Different chapters are based on lectures given by different practitioners in the field, and so the book as a whole gives an excellent holistic perspective on the profession. It took some getting used to, as I did expect more of an academic, textbook treatment---and to be fair, there is quite a bit of statistics and data wrangling in the book. But given that data science is not really a subject, but a job title (one can see this in the history of the term), the book's approach makes total sense. As a handbook for the profession, I found the book to be clear, substantive, and astonishingly thoughtful, particularly in regards to the ethical dimensions of the job. I wish more books would take such an expansive view of their subject.
I am a small business owner and I read this book in order to get an introduction into the world of Big Data, and it was wonderful.  It gave a clear and comprehensive introduction into this world.  The book went out of its way to be friendly and inviting, and went out of its way to make things understandable.  (Note: I have attempted to read some other books on this subject and they were far more technical and harder to understand.)  This is an easy read and should be read as a primer by anyone interested in this area.
If you ever wondered what great data scientists from all over the data science spectrum do for their day job, what questions bother them and what's the easiest way to resolve them, this is the best book in town.Given that the book was based on a dozen of invited lectures, authors did a great job of making a coherent picture of snapshots of still very uncharted territory. Among the others, examples of how to build a recommendation system like one for Netflix or how to scrape the data and classify them with good old naive Bayes model are there. These examples will give enough juice for anyone who would like to try to implement some predictive models in any web app.
As many have already noted, the Kindle edition is a mess from the typesettiong point of view: Many formulas and greek symbols are 500% larger than normal for the font; e.g., see the half-page Sigma under Fig. 3.3 and too many others...This is clearly an amateurish automatic conversion from LaTex or whatever the original toolset to Amazon's proprietary Kindle e-format. Hardly acceptable in 2015, after all the prior feedback provided on this site. I paid only ca. $6 for it, so i may not return it - as i should :-)As per content it remains to be seen. However, the code is not fully functional, nor is WWW link provided for true code.
Really helpful in providing direction for those interested in the field.  Good, thorough survey of all the major aspects of data science.
I have decades of computer science and math experience but found this book largely incomprehensible due to the rambling writing style.  Throw in a lot of tangents, constant political correctness, jumping around, statistics deep dives, and you get the ingredients for a migraine.I'd recommend this book for someone with at least a bachelors in statistics and minor in computer science.
Lots of good information - if you can wade through significant comprehension interference caused by meandering trains of thought and poor, non-linear exposition. It has the prose of a novel, not a well written technical book. While trying to set an easy going tone, it is much too wordy - causing a reader to exert unnecessary effort to extract information.