laptak2003/AmazonDataScience huggingface dataset:
I found this book to be a very odd bird indeed. It is one book you can read from back cover to front cover and not be at a disadvantage. This is because the book is really just a collection of presentations made by various people to a class taught by the primary author Rachel Schutt at Columbia University in the Fall of 2012 â€“ Introduction to Data Science. It wasnâ€™t entirely clear what content Schutt was directly responsible for since only some of the chapters indicate who the contributors were (one of the chapters was contributed by a group of her students!). The co-author, Cathy Oâ€™Neil, Iâ€™ve encountered before as an outspoken blogger going by the name â€œmathbabeâ€ but it wasnâ€™t specifically stated how she became part of the book project, other than to say she was one of the students in Schuttâ€™s class. Chapter 6 was partly written by Oâ€™Neil.Both Schutt and Oâ€™Neil are Ph.D.s data science appropriate fields, but the book was not â€œwrittenâ€ by the two, rather they seemed to have performed some kind of editing function with the materials submitted by each contributor and added commentaries of their own. As a result, the book is a hodgepodge of anecdotes, factoids, R code snippets, plots, and mathematics, all from the in-class presentations. I enjoy seeing math in data science books, but the equations in this book were sort of just floating there requiring the reader to explore further at another time.Although I have issues with the book as it is not any sort of text for the field, I did enjoy reading it with a number of â€œAh, I didnâ€™t know that!â€ moments. Schuttâ€™s credentials in data science are considerable, having worked at Google for a few years around the same time that â€œdata scienceâ€ was growing up in Silicon Valley. As a result the book has many memorable anecdotes about the early days of the data science industry, and observations about what makes big data tick. I enjoyed the story about the Google software engineer who accidentally deleted 10 petabytes of data, and I think my favorite quote from the book is from the studentâ€™s chapter 15:  Kaggle competitions could be described as the dick-measuring contests of data science.With contributorâ€™s chapters on statistical inference, machine learning algorithms, logistic regression, financial modeling, recommendation engines, data visualization, Hadoop, MapReduce, and more, Iâ€™d say the book is worth a read, but not necessarily as a source of learning data science but more as a high-level guide and short historical account of this young industry. You get to learn about the people, companies, technologies that have collectively built the data science arena and youâ€™ll be better for it especially if you are working to become a data scientist yourself.
Highly to recommend for any data scientists or knowledge engineer who wants to be successful in real life applications. Great book!
The Kindle version has very poor rendition of the maths formulae and symbols - some being huge, some so small they couldn't be read, and some just plain wrong. If this is typical of Kindle versions of such text, then don't buy Kindle versions.  I would not have bought this had the sample contained any formulaeThe authors use a chatty style which some people like, but I found somewhat condescending.
For a survey book it's fine.I've been doing statistical computing on financial data since about 1997. I used R for the first time in 2001 and I've worked as quant for proprietary trading groups. And I gotta say I tried to like this book. Here's what stops me: there are references to, for example, Tufte's work on data visualization. But then there's no consistency to follow up with Tufte's principles. Likewise with the discussion about coding. There's scant to no mention of good programming practices. There's R code examples but no one bothered to use a style guide (or mention one). And one issue with managing scripts for exploratory data analysis is version control. It's a big deal to organize scripts you're likely never going to look at again. And this topic isn't even mentioned. There's also no mention as to the quality of tools people use. (I've never used Excel, for example, and wouldn't even consider it an option.)The biggest thing that bothers me about the book is that there's no coverage of how you might be fooling yourself with the analysis you've just done. That's really the biggest deal of all in doing data science. (Given that one of the authors worked as a quant at D.E. Shaw this topic should get top billing, and you'd expect the financial data analysis section to be a lot stronger.)Save space on your book shelf...the book is worth renting from a library or browsing through in a bookstore.Ok, one last thing that keeps me from liking it more: There's a lot of words used to talk about the contributors. Going on and on about so-and-so's brand name university credentials isn't appealing or relevant. It's sort of gossipy. There could've been a lot more substance given the page count.
I didn't read the whole book. The first few chapters were very boring and I gave up. I did not see any specific practical pieces of advise on how to do data science. Very little code and specific algorithms. This book feels more like a lunch conversation with a data scientist rather than a real guide, or a cookbook of data science.
Not for the general reader.This is a somewhat difficult book about an emerging discipline of Data Science by a pre-eminent practitioner. But it is a necessary book to explain what this discipline encompasses. It is still too early to be absorbed into popular culture.This is really a text book which requires the reader to do the exercises so that the reader can grasp the topics. This is what makes the book difficult for the general reader. One has to be sufficiently competent enough with computers, programming, and statistics in order to appreciate the evolution of Data Science.Despite its difficulty, this book is a necessary introduction for the general public to understand the threat to democratic society from the misuse of Data Science. O'Neil explains this further in her call-to-arms (Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy).Unfortunately, public culture still regards computers as magic. Say the right words, and the answer magically appears. Data Science is far more than just using computers -- Data Science involves the collection, analysis, cleaning, and presentation of enormous amounts of data.
I work closely with some great data engineers and statistical programmers and am interested in practical applications of these skills. Generally, textbooks on statistics and machine learning are purely academic, but here the techniques are treated in the context of solving problems with data. The reality of working with real-world data is that it is rarely ready for analysis in its original state â€“ it's a challenge to clean and organize. Applying the right analysis and gleaning insight is only possible with that groundwork.The style of the book is informal and friendly and different than most technical books. This makes it accessible to beginners or more experienced professionals who want a new angle on a topic with which they're familiar â€“ it may be a turn-off to those who are looking for a more traditional approach. The book is not traditional â€“ it has a style of its own and asks fundamental questions about the role data plays in our society.
I liked this book so much that I feel compelled to write a short review about it , which I have never done before and I have bought many books through Amazon. Anyway, I think this book is a great introduction to Data Science for someone just trying to get a very strong feel for what the field is all about.  As the book title implies, it is not text book.  It explains and shows in a very casual way (classroom style?) how data science is done in the real world, without getting too deep into the technical details that you will and should find in text books.  It also helped figure out what areas of my skill set I need to work on should I pursue work in this field.  In summary, great buy!
Absolutely great book for anyone interested in Data Science and the field - for novices as well as advanced students. Yet to read every chapter but quite complete book thus far.
The content is now a little dated, but the other reviews are accurate.
I make my living working with businesses to help them build their analytic capabilities in sales and marketing, by working on real opportunities and generalizing lessons from specific experiences and results.  Business sort of demands that over time you narrow problem-defining frameworks, problem-solving techniques, and problem-processing operations to what you can scale and consistently deliver.  But what I have learned is that if you push these past use as points of departure, and force them as points of arrival, results and learning suffer.  (As they say, if you have a hammer, then everything looks like, etc. etc.) This is especially true as things get more complex on all these fronts.  What I appreciated about this book was that even as it is an excellent survey of specific approaches and techniques at the coal-face of their application (there are code samples here, people!), it's also a thoughtful exposition of the limits of these and of the pitfalls they present.  You might be tempted to skip Chapters 15 and 16 if you're just looking to RTFM, but you shouldn't; for me they were the real meat of the book. Maybe it's confirmation bias at work, but it was reassuring to hear such a humble tone coming at the end of such a competent treatment of different data science challenges.  Doing this work very occasionally brings triumph and much more often despair, so it helps to have reminders from seasoned pros that what really matters is just trying, steadily, to get better at your craft.
The meaning of the term "data science" is unclear to many and this book is an excellent introduction to the subject.
All or most of the key issues of data science are covered. That is really good for reading. However, the presentation of material makes it difficult for a student to quickly follow.
We are in 2013, no one knows what the heck is â€œdata scienceâ€ but there are plenty of jobs out there. Here is a course for you, future data rockstar.  Rachel and Cathy invited a bunch of people from industry to talk about a wide range of topics: from statistical inference to data visualization with plenty of algorithms, R code and data sets. This is therefore a hands on course with good theoretical depth. And the take away message is: if the world is a bunch of data pipes, don't just be a plumber. Rather behave like the freaking Mario Bros!
It's an OK book. It has some nice parts, although you don't learn data science from it. It's like a compendium of blog posts about where data science is used. There are some great articles on it, from interviewees, however, it lacks something.
It is a must-read book w/ proper expectations  and after learned data science concepts in another books/videos!- For people looking for things about data science, it is just OK book (3 star). Learn then come again :)- If you good at python & pandas, solid understanding statistic, and somehow has experience w/ data science, It is VERY GOOD (4.5) book.Data is really mass and massive. So cases are very close to real scenarios. That's why takes too much time and effort ,+ You have to do exercises in python and pandas.+ You have to handle performance issues in pandas elegantly+ Solutions in the book is just to give a hint . Nothing more
The books is fairly dated, a lot of the exercises have broken links/outdated code.
Reading this book, you feel like you are talking to the authors. With 10 years of experiences in data analytics, I can still learn so much from the authors' way of explaining Big data to novice. I learnt from it and used it successfully in my work. As the expert within the organization, you need someone like the author who can "coach" you. Recommend to any Lead Data Scientists in Real World
Great text that provides a very informative overview of topics in Data Science. Ideal for someone like me: Generally curious but cautiously skeptical. Description of K-NN/Bayesian classification algorithm encouraged me to study these and related topics further. Obviously not a one-stop shop as the title suggests.My only complain is that the author sometimes digress too much on industry background/providing context. This seemed like a good thing in the beginning, but eventually just kind of became a distraction to the main text. Individual preference may differ on this
The book is well written and provides good insights into how to form a foundational core to further one's education and experience in data analysis and visualization. To truly accomplish the end results that significantly impact informed decisions, one must bring to the table a well rounded background and experience. The book serves as an excellent source to focus on a positive approach to learning and executing data science. Not all will agree, but in my opinion, the secret to success in this area is never to evolve your skill isolated from the works of those already successful and willing to share their knowledge.
This is a beautiful, thoughtful survey with excellent references.  I am an academic data scientist with nearly 20 years experience and I wanted a book to offer my students who are starting in the field.  This is it.The "difficulty" with data science is in the breadth of skills that are needed. Because data scientists need training in art, communication, statistics, and programming nobody is prepared to handle all the tasks and the neophyte (and expert) will need to fill in around their weaknesses.  This book does a brilliant job of working around that issue.  The writing is superb for a beginning to intermediate reader and the graphics and aside boxes are engaging.  More importantly. the references are plentiful and spot on.  In the areas I know well the authors suggest the things I recommend and where I am weak the recommendations have proven interesting.While this is a broad survey, there is some depth here.  There are formulas throughout but the book does not get bogged down in proofs and derivations.  There are programs written in R code scattered throughout.  The code is nicely commented but there is not a deep dive into how it words.  So, the reader who knows some R will learn a few new tricks but it does not interrupt the flow of the book.A reader who types the R code will run into problems.  Clearly the authors/editors did not attempt to run the code after the type setter mangled it.  For example, on page 39 there is a line which begins with a + and that character needed to be on the previous line.  In other places, (like page 49) functions are invoked (count) but the authors have not included the commands to make the functions available (in this case library(plyr)).  Sadly there does not seem to be an errata for the book and these will be major headaches or show stoppers for novices.While this book could be improved with a code supplement on the web (including the code to make all the graphics, complete solutions to the example/problems and an errata), this is a wonderful buy for readers of all levels.
Arrived as promised.
Have you ever been in a class in which the teacher says "OK, now each student can present a lecture on their favorite topic"?While reading this book I was reminded of such classes, during which I often felt I wasn't making good use of my time.Although several of the topics being presented are interesting, it's difficult to get a "big picture" of the whole subject covered by the class, and there often are things being repeated between one lecture and the next.Personally I prefer a more coherent approach, where all the lectures are prepared by the professor, who can guide you through understanding the important concepts first, and then dive into the details of one specific problem at the time.In short, I felt like with this book I got exposure to several real word data science problems (and their solutions), but without much depth, and without getting a coherent picture of the field. I felt like I made a better use of my time reading "The Python Data Science Handbook" and "Data Science for Business"
Doing Data Science is about the practice of data science, not its implementation. It is based on a course on data science that featured a guest lecturer on each topic. This leads to the guest lecturers (and chapters) focusing more on important concepts rather then the methodology. So, this is not a textbook or a how-to-do-this type of book, rather it is a how-to-think-when-doing book.A problem with books like this where each chapter is written by someone different is the need for coherence. A second is that each author typically has something to day, and she has to say it in her chapter. So, compared to other data science books, it suffers from the chapters not building on each other in a systematic way and having multiple messages that appear as you go through the book.One benefit from this is that each author has something to say. While I find the book thin on how to do things, this is a good source of wisdom in why things are done and issues that come up along the way in real life. I am teaching data science for the first time and I find myself turning here for topics of discussion which my chosen textbooks don't cover (as they have more focus on how to do things).I don't think this is the book to use to learn how to do data science, and I suspect the students at Columbia learned how to find other sources to help them figure things out. But it provides wisdom, which is harder to find and worth quite a bit.Note: I received a free electronic copy of this book from the publisher as par of the OReilly Bloggers program.
Doing Data Science is actually a bit of an oddity; an easy read in a deeply technical field. The book is based on a series of lectures and aims to inform the reader how data science works rather than simply providing a cookbook of recipes to carry out processes. For me this approach worked very well; after trawling through endless examples and documents of the data science flavour of the year map reduce I can finally say that I understand the process. Of course if you are labouring under a pointy haired boss who has heard the term data science in a meeting and is pressing you to use all its latest and greatest techniques this approach may seem a bit frivolous, but itâ€™s well worth the investment. Understanding the process will speed up your implementations and will also inform you as to what approaches are valid with what you want to do. Iâ€™ve played with the big data hero du jour map-reduce a few times and never really knew what I was doing; having read Doing Data Science I have a reasonable clue as to what is supposed to happen so I can at least sanity check my next attempt rather than just try and hack example scripts and read impenetrable howtos.Not only is Doing Data Science informative itâ€™s also a light and engaging read which is no mean feat in a domain that tends towards the dry and dusty. The authors have certainly done an excellent job of bringing together a thorough grounding in the data science domain and pull together a lot of data from different areas into a book that is coherent and eminently readable. If I do have any grouch itâ€™s the title; I would have been inclined to call it Understanding Data Science rather than Doing Data Science which I think covers the content a little more accurately. Overall though I would thoroughly recommend Doing Data Science to anyone interested in understanding the field rather than simply implementing it.
Doing data science: Straight talk from the frontline is a book based on a course Introduction to Data Science thought at Columbia University in 2012 which is reflected in its structure and content. The book is organized into several topics/lectures, describing various problems when extracting information form data in connection with used methods for various types of data.They describe Data science profile as a mix of several skills such as computer science, math/statistics and machine learning but also domain expertise and data visualization.The book is intended for those who want to gain overview in the field but also for those who want to deepen their understanding of reasons why and how various methods are working and what are their limitations. The book is organised into several chapters, each for different problem or different type of data such as: time series analysis, spam detection, recommendation, data visualization or social network analysis. Every chapter contains a case study with concrete applications of described methods written by an expert who practice described methods in real project.Every topics is written as separate section of the book and you don't have to follow the structure of the book. You can rather choose the topic most relevant for you at the time and study them separately.The book tries to create general and the broadest possible overview of methods commonly used in the field, but at the same time it tries to closely explain selected methods and provide concrete application and code examples in Python or R language. The author is aware that every topic they describe is a topic for separate book and they include many links for further resources such as articles and books but also online courses.Described topics are written for those who want to practice, not for academics even though it is sometimes impossible to completely avoid formulas. For full understanding of the book, the basic knowledge of programming and at least some basics of algebra and statistics. But also without them, one could expect valuable info about methods used in the field and can be used to gain insight in various problems one could face.Summary: No need to be discouraged by repeated use of the buzzword Big data. If you can overlook it, you will have a book that provide comprehensive overview of the data science for novices in the field and valuable insights about various commonly used methods for those with some level of expertise in the field.
Data science has been around for decades.  Itâ€™s not â€œnewâ€.  You have to be a statistician, as well as a programmer.  Itâ€™s not a â€œhot newâ€ field to get into for high pay.  Stop selling over generalized books like this, making people think they can just walk into â€œdata scienceâ€....which by the way is a stupid term, created by douche bags in Silicon Valley.
"Doing Data Science: Straight Talk from the Frontline" by Cathy Oâ€™Neil and Rachel Schutt; O'Reilly MediaWith so many books being published on Data, from Big Data to Machine Learning to Data Analysis, we must ask what yet another book is going to offer us.  And the answer is not that much.  Oâ€™Neil and Schutt have seemingly written this to give us a view of how the ins and outs of the actual world of Data Science are practiced.  But we get little more than an overview of what Data Analysis is in general.Many topics a reader might find interesting, whether for background knowledge or otherwise, are given little emphasis, and even then, without much depth.  As an example, a discussion on the Exponential Distribution merely states that â€œbecause we are familiar with the fact that â€˜waiting timeâ€™ is a common enough real-world phenomenon that a distribution called the exponential distribution has been invented to describe it.â€  Any mathematician realizes that inventing probability distributions are a little more involved than is implied.The main part of the book starts with a look at algorithms.  The authors use R as their primary language.  There is little in terms of explanation of the underlying processes and examples are pretty direct.  NaÃ¯ve Bayes, for example, is explained in a page and a half, so we can clearly say that this is an overview of many of the ideas going into Data Science.  In fact it is a very wide overview.  Financial Modelling, Spam Filtering, Epidemiology, the list goes on.  This is definitely a plus; we see the wide applications that Data Science has.The book ends with a discussion on Competitions, but we come back to the question of what we have gained more than we might get by a simple perusal of the web?  Maybe I was hoping for more out of the book than I got; expectations can be a killer.  But in the end we must make a choice.  If we would like to know about Data Science â€“ how itâ€™s done â€“ then we would probably like to look elsewhere.  If we would like to have some book that we can show someone what areas exist within this field and what topics are touched upon in it (perhaps for an advisor), then this book should do fine.(FTC disclosure (16 CFR Part 255): The reviewer has accepted a reviewer's copy of this book which is his to keep. He intends to provide an honest, independent, and fair evaluation of the book in all circumstances.)[...]
The quarter has just started. I will report more, and adjust my rating, later.One concern: the data files referenced in the text (chapter 2) no longer exist at the stated location. After some fussing, I found them on the O'Reilly site - that's the publisher.  I'm not sure Amazon will allow the URL, but I will try:[...]On github, oreillymedia has a list of many books, with code. The relevant page for this book is doing_data_science .Be sure to click on the button on the bottom right, Download ZIP. (The more obvious file link is the wrong file.)
I have an advanced degree in Decision Science albeit 20 years ago.  I am now boning back up on this topic after already reading through several other books on Data Science and R Programming, including both "Datasmart" and "The Art of R Programming".  I had hoped that this book would help me put all the puzzle pieces together but after the first several chapters I found that it made way too many assumptions about my level of both programming knowledge and my understanding of advanced math including calculus.  In addition several examples of the code used R Packages that either had already been archived or just plain did not work as they had been portrayed.  Those same code examples came along with little step by step explanatory support.  Again, the first couple chapters held promise, but then it turned into a book I found more geared for Academics or hard core programmers.  I'm moving forward with "An Introduction to Statistical Learning", and a few other selections.  I would pass on this one.
A friend was recently at an IT conference and reported a great quote, "Data science is like sex in high school: everyone says they are having it, but almost no one actually is."That the field itself is emerging now is one reason that Doing Data Science is so valuable: it's far more than a technical cookbook (though the authors are practicing professionals in the field). Make no mistake, this book is intended for a technical audience and very much involves writing and running code, but it seems to offer many entry points for the reader, regardless of their particular proficiency or specialty.Drawing upon a course they instructed at Columbia, the authors move through the different pieces of what this emerging discipline is thought to comprise at the moment. I found these sections (based on lectures from leading industry folks- not academics) to paint a picture of what the data science toolbox is rather well.While no one quite knows (or agrees) on what data science is, Schutt and O'Neill give a thoughtful and nuanced guide to what is likely going to be the most in-demand technical skill of the century.
Just okay. The explanations of statistical topics weren't very good and there are quite a few errors in the code. This book shoots for broad overview descriptions of algorithms and they just miss the mark. This book reminds me of lecture slides prepared by a professor that didn't have time to proofread.
It's a great book. There isn't a whole lot of technical details about some of the algorithms, but they don't claim to provide it all. You're expected to read up the sources (they provide) if you want to know the nitty gritty and use that algorithm frequently. They do, however, provide the (more valuable) code in R for most algorithms. It's also a much much more interesting read than a textbook. The authors actually work with this stuff in industry, not just academia. The language is often informal, which I appreciate after reading many formal and dry papers. All in all, if you want a book that will teach you a thing or two about data science, this is a great start.If I could give this 4.5 start I would though. The visualization section, in my opinion, is a bit weak. They do reference classic books like Tufte, but they didn't present InfoVis IMO. The art exhibits are super cool and all, but those are one-off examples. Almost all vis, in practice, is in software and on the web. I would have like to have seen mention of parallel coordinates, scatter plot matrices and other concepts that are actually useful for data science. They could have had some D3 examples, instead of what they had. Still cool to see the exhibits.
I'm used to high quality of o'reilly books. This one is not the case. The whole books is 330 pages of information. First 60 of them are pure fiction. I have to admit it was somewhat useful, but it could have been covered in 15 page. Then when you encounter real math in the book(linear regression) it contains errors and symbols that come from nowhere without any explanation. Only after reading wikipedia page on this topic I could understand where this misterious t comes from. In fact it should have been written as T. Code examples come without any explanation whatsoever. Very disappointed. If you look for a data science book - buy something else.
It is the most difficult to digest and comprehend book to date out of what I have recently read. It is even fun though at the same time. I guess I need to blame myself because this book unexpectedly turned out to be more from the Academia world where my skills in Algebra and Statistics faded out over time than from the practical world. At the same time it was pleasant to feel a student again.Nevertheless, the book offers a ton of insight, and how-toâ€™s for the in â€œthe trenchesâ€ practitioners. This book is full of external reference and facts, it sure took a while for the authors to assemble it.From my observations, the knowledge of the R language is necessary before starting reading, sadly, even if a program code is provided in the book there is no sample output.The book is written so it has chapters by guest authors, this makes sense as a data project is rarely comprised of one kind of a professional, this nuance is also covered in the book by the way.These guest authors are top notch professionals that would write a complete book on their own subject matter of expertise. But because they are the â€œtop gunsâ€ in their corresponding field each managed to cover a lot of grounds just within a dedicated single chapter.So, in short, the best thing about this book is that in one single investment you get a comprehensive coverage for life on what approach or algorithm to use against a given data science task at hand. You must feel more secure after reading this book and as a result be more eager and ready to embark on any data science project.Five out of five stars.Disclaimer: I received this book for free as part of Oâ€™Reilly Blogger Review program.
Data sets are not provided for the majority of the examples.  Not sure why.
A nice overview of the tools that make up the current state of the data science paradigm. In bringing their practical perspective to an academic environment, the authors have provided an excellent and readable overview to the process and raise a number of meaningful questions on the role of data science across academia and industry.The text is an ideal place to begin students with real-world expertise but a lack of technical practice. I plan on using it as a first reader in a data science course when paired with my own exercises.
I can only say good things about the book and the authors' presentation style. In real-life applications, making sure data is used in a cost-efficient way while generating useful insights is challenging. The experience of this applied work surely comes through in this book with both authors bringing this kind of insight, something that authors coming from academia may have a limited exposure to. A great resource to get more into "doing" than "conceptualizing" when it comes to Data Science.
... helps the medicine go down, as Mary Poppins used to say. An IT-focused publisher, O'Reilly has twice before used the "book as collection of chapters by different contributors" formula in its foray into the attractive "data" niche, with such titles as "Beautiful data" and "Bad data". "Doing data science" - by the way, I prefer Hastie and Tibshirani's "statistical learning" to the fuzzy and grandiose "data science" - follows the same approach, but, with its subject matter being closer to the academe, the company enlisted two young PhDs to steer the collaborative effort. Rachel Schutt took the lead as author and editor, and, assisted by Cathy O'Neil, produced an engaging, informal - you don't often see "science" in the title and "huge-ass" in the text - yet sufficiently technical to be hands-on, sequence-of-vignettes-styled book. Imagine a mash-up of a magazine article and a textbook. Neither part may be best-in-class, but their combination makes for a "unique selling proposition".Well, maybe not a textbook. Most textbooks are carefully written and carefully checked. In contrast, when I see "Doing data science" introduce  the ROC curve in three places, one of which translates the "O" as "operator", I can guess that this is a copy-paste of papers by three contributors. When Dr. O'Neil casually redefines an English word ("causal") to avoid rewriting a couple of sentences, or pronounces, on page 159, that "priors reduce degrees of freedom" - this is painfully meaningless, and neither term is defined, only name-checked - I suspect that she knows better, but just did not feel like spending more time on her half-chapter. Neither author speaks of their own projects - if this is the "frontline", then it's other soldiers' "trenches" that we are visiting. The occasional code listings are borrowed as well, thrown in without editing or comments. In this last regard, "Doing data science" lags far behind the book that seems to have informed its choice of topics, Peter Harrington's "Machine learning in action". (That's one suggestion - and if you want a good, accessible textbook, "Introduction to statistical learning" by James et al. is another).None of it is going to matter to the book's target audience. "Doing data science" is aimed at beginners - and is bound to be interesting and useful to thousands of keen undergrads and adult learners.
Great book!!!! It is a good introduction for beginners to the field, and I particularly liked its humanist approach.For me, it was useful to hear from people who work at different companies to see how techniques are used in the "real world".
Begrudgingly typing in the R code, and it is an absolute clusterf---.
The book is based on course taught at Columbia University. A great composition of invited lectures by leading practitioners that is well put together. It provides a nice overview of industry practices.
I buy plenty of books and regret some, but not this one.  I am keeping this one on my book shelf.
I thoroughly enjoyed this read. It's a very informative and well written book on data science. Kudos to the authors!
The authors are pretty condescending, and it feels like they're practically apologizing for the field of data science.  Yes, there's a lot of hype around data science, get over it and tell us something interesting.  If you want to be paranoid about the ethics surrounding big data, I guess you'll enjoy this.
can you please have the publisher proof read and fix the kindle edition? we should not have technical books without table of contents, and formulae size font is ridiculously large.
Loveeeeeee this book. The chapters are laid out so well. It starts with review of basic excel functions that will be used, and then you dive into real world problems! Additionally, you can download the workbooks from online and either try to recreate them as you go in the book, or just follow along. For me, being able to follow along and trying to recreate it makes it a much easier and enjoyable learning experience.
Printed book is in black & white, which makes the screenshots of spreadsheets and color-coded formulas so much more difficult to interpret. E-book is in color. -1 star for the low quality print job. Otherwise a great read of highly technical subject matter, made bearable by the author's joking writing style.
This review is based on product arrival. Bought this as a birthday gift for my husband, which is tomorrow, and was very disappointed how it arrived. The whole front page was damaged. I plan on gifting it to him and then returning to purchase another with the front page in better condition. Such an inconvenience
I am really glad that John Foreman wrote this book.  There are so many tools here that I can use in practical situations!  I do have two issues.  First, since the book was written, a free tool has been created, which is an add in for Excel that does logistic regression.  It is called XLminer and there is a video showing how to download it into Excel here:[...]My second issue is that I wish he would write a sequel to this first book covering more new data analysis tools!  I really like the step by step instructions in this book!
I bought this book to learn how to do some basic analytics with Excel.  I got that.  But what I also got was the background and an understanding of how some of the principles worked.  The information was presented in a very easy to understand and enjoyable manner.
This book is transformative! Seriously, as a CPA this book has made data science completely understandable and easily accessible. I recommend this to anyone looking to enter into the field. John provides sample spreadsheets and teaches you how to perform the analysis IN EXCEL. No other software is needed. Take the plunge and buy it already. You won't be disappointed.
One of the only books I brought on my last overseas deployment. I read the book then watched Sneakers. Disclaimer: I am often referred to as "nerdy" by other army guys.I think this book is best suited for someone who has intermediate to advanced Excel skills, Is the person in the office everyone looks to when they need to solve problems and likes some statistics (but I guess that is unnecessary because everyone likes statistics.)
Full 5 stars.There is no reason why you should not buy this book, if you even are remotely connected with things like 'Data Science', 'Analytics', 'Forecasting' etc.I enjoyed all chapters and especially Chapters 4 (Optimization), 6 (Regression), 8 (Forecasting).Seriously buy this book, now.It's very easy read, and yet the author does not merely skimp important concepts, so you get best of both worlds, a good solid foundation and practical implementation.One thing I like is for 90% of the time, the subject matter and the spreadsheet diagrams are on the same set of pages, so you don't have to go back and forth between pages to sync text and images.
This book is the great balance between theory and practical guidance. It's written in a fun an accessible style, and gets you started with the most well know data tools - the ubiquitous Excel. Highly recommended for folks who need to know where to start for building advanced data and analytics skills, and then pick a direction for specialization. A fine read!
Awesome book! It simplifies some very high end processes. The explanations of the concepts are excellent as well as the explanations of the methodology. The downloadable files are outstanding. I havenâ€™t gotten through the entire book, maybe half, so Iâ€™m giving 4 stars for now.
Instead of going through complex tools to start anaylzing data, this book explains all the essentials you need to be a Data Scientist using the master tool of all tools: Excel!I consider my self a black belt Excel user. I used it in finance, accounting, software engineering, proposal writing, you name it! but this book surprised me that there is still more under the hood when it comes to Data Science.
The explanation of the statistical and data-science approaches are easily understood and relevant.  He uses Excel for this, and does a good job applying the step-by-step methodologies to do this, but using Excel for these analyses require many steps.  If you are a fan of R (which he references at the end of the book), R would be a much more straightforward approach to use than Excel.  Loved the book, though.  It is a great reference.
I am a statistician who has been working on the periphery of data science for a few years. I found this book to be highly useful to transition to thinking in terms of big data and to get out of the thinking of count-and-sort, which is so prevalent in heuristic modeling.  I've spent most of my time in the clustering and network graph theory chapters.  The sample data provided for Excel is useful to start with.  I got right into R, and it was fun to see it all come together in both Excel and R.  I will point out that the R chapter does not give sample code for network graphs - when you get there, look into Newman's fgreedy as a quick way to get up and running with modularity optimization.  All in all, I'm really glad that a friend mentioned this book to me, it's been a very enjoyable read.
I learned from this book. And I enjoyed the author's style. My only complaint is the use of Excel for all the examples. The descriptions become very cumbersome, with lots of one-off details to wedge an interesting example into a spreadsheet. I'd much prefer if he'd used Python or some other better analysis environment.
This is exactly what I wanted.Book is really technical to the point of creating machine learning/AI models in Excel (cell by cell, formula by formula), but still, methods and steps are clearly explained so it looks way too easy and at no point in time it felt over my head.There are plenty of examples which are crafted to stimulate you on how else can you use your data and what other treasures are burried within.Writing style is relaxed, informal and really engaging. You could read the book just for the fun of it.I would recommend this book to anyone who is willing to get his hands dirty with advanced analytics, but the book could be useful also to anyone making decisions in order to learn what and how to ask from his technical staff.Rarely useful, educational and applicable book at the same time.
A very well planned topics and examples that build on one another.  The beauty of it is that no special software is required to follow the examples - a spreadsheet program is all th]at is needed.  If decision trees were included, then the book would be a nice complete package of the relevant topics.[[ASIN:111866146X Data Smart: Using Data Science to Transform Information into Insight]
I have always been interested in extracting as much information from all of the data we generate in our business.  Data Smart Using Data Science to Transform Data into Insight is a practicable guide to do using the information generated from accounting, production and marketing to derive actionable plans and objectives on a corporate-wide level .  Using Excel and Solver will give a number of managers within our organization the tools to dig deep into basic concepts of Data Science and deeper into their own data.Foreman writes clearly and uses examples of common business problems to explain the concepts of Data Science.  He follows Excel equations and math with concise explanations to ensure their complete comprehension.  Many of the ideas addressed in this book can be complex, especially to someone new to Data Science, but Foreman elucidates them so clearly that they are easily understood.  I can't say enough about how well written is this book.  I will be recommending it to many of my colleagues.Charles ColeVP/GM-International DivisionAmerican Polywater Corporation
Book is helpful for learning how to use Excel to do data analysis.
If you need a gentle introduction to some data mining concepts, I found Foreman's work straight forward in its explanations and examples.  He makes a good case for using Excel as the teaching vehicle.  Certainly, there are more efficient tools to do tasks such as data cleansing and word counting, but his book is mostly about the concepts of data mining.  I plan on using this as a text to supplement an information systems course I teach.  Nice job!
This was a required textbook for one of my ITAM class and it is completely written for Mac users, with PC users as a side thought. The author leaves little witty comments all throughout the book, which sometimes makes the topic confusing. Some editing/continuity errors make somethings hard to understand because the pictures don't match up to the actual task at hand. One of the worst text books I have ever used.
Very easy to read and follow.  Exactly what I needed for class.
Excellent book for learning how these data science algorithms work.
This was my first â€œdata scienceâ€ book and it really showed me how much I need to learn, which was done gracefully,for what I am thankful with the approach the autor takes and his down to earth advice.
Got this for my BI certificate course. This guy is a genius and he does an excellent job walking you through very complex stuff.
I'm a semiconductor engineer who is interested in learning Big Data with my 16 year old daughter.  "Data Smart" is a great book with concise samples and easy to follow steps.  My daughter and I build solid understanding of cluster analysis by studying chapter 2 and 5.
Step-by-step easy to follow practical exploration  - finally I found a book to help connect dots from more theoretical readings. The book finalizes bridging the step-by-step learning with the real world robust data crunching platforms. Very well done.
This book is funny and amazing for all levels of program geeks. I found his sense of humor to be just what the doctor ordered. Teaching programming can wear you out depending on the day. I recommend this book.
A terrific book covering key data analytics concepts, and how to execute them in Excel.  What I liked the most was the author's ability to explain what each analytical technique was designed to do, and how it worked - in layman's terms.  This switched more than a few lights on for me...now I'm much more confident at choosing an analytical method, and understanding how it will produce the insights I'm looking for.
Instructive....concise....it gives you the confidence to try something new using Excel....great for trouble shooting as well
Hands down one of the handiest data books to own. Keep a copy on my shelf for reference!