Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python 2nd Edition, Kindle Edition

The media could not be loaded. Really a great book for stat. I am really glad I purchased this book. Easy explanation and great examples.

I had purchased a new physical copy of the book, and realized there were several pages that were blank and missing. I contacted O'Reilly about the problem and they were extremely quick with a resolution! They were able to give me a different copy so I could read it without the missing pages. The content of the book itself is good, except in all black and white, which doesn't bother me personally but may bother someone else when it comes to the graphs. I think the R and Python content are both great, and it keeps the code concise and quick to the point. Great for R beginners, but for python users I would recommend a little more experience. As for the math parts, its great for those who are new to statistics and gives easy to read explanations, and a great refresher for those who just want to review some of the concepts. I especially like the sections provided for further reading, which have been helpful.

I've taken many stats classes, most of them using R, at the undergraduate and graduate level, and I really wish I found this book before I did. I picked this book up as a refresher, and not only did it succinctly describe all and a bit more of what I learned in those courses, but it has excellent "further readings," great clarifying synonym lists when it defines "key terms," and is very readable. Literally blown away.

Good content/low quality print

Very good book- covers more than just implementing same old tactics.

This is a very good book to begin your DS stats journey with. I learned more from this book than I did in my DS grad school classes. It covers the basics you'll need everyday in a practical way.

No noticeable flaws or writings

This has been a common refrain in my review of O'Reilly books. Their technical content is, as usual, excellent and comprehensive. The quality of the printed version is horrendous. The illustrations which were created in color are printed in black and white. As a result, the illustrations are confusing and look faded and would be better off omitted entirely. The printing wanders in and out like it was done on some old dot matrix printer, and the paper seems to be of cheap quality. Quite frankly, it looks like an international version which is usually printed cheaply so that foreign students can afford it.I'd get a subscription to what was once called Safari Online and read these books online and in color. I'd stay away from printed O'Reilly books unless you can verify that the printing is in color and is a quality job overall. If O'Reilly wants to continue to have my business as far as purchasing print books they should raise the price so that they can deliver a well published product.

Buen libro con un excelente contenido temático

What a great book! The authors did a marvellous jobs in packing an incredible amount of information in very little pages AND doing it in a very pleasant style that is direct, informative, and extremely clear.I have read many books in statistics. I can tell you there are very very few written so well and so pleasant to read.And to top it all, it is one of the very few book of statistics for non-mathematician that *correctly* explain the p-value and t-test. Many statisticians *still* don't understand what that "significance test" really mean. But these authors do understand it very well and this is very important for anyone new to statistics to know this test correctly and in the hand of these authors they *will* learn it correctly.Thanks a lot to the authors. You did a fabulous job.

Entrega super rápida e excelente livro

I got this because I am taking a data analytics course that is not explained that well and I need to fill up my gaps in statistics. It is a good book

In my view, this book’s strength is the deep knowledge of the authors added by the ability to explain key points in a few sentences.I love the frequent question and answer to “Is it important for Data Scientists?” Data Science is such a wide and deep topic, that any pointers are extremely welcome.Who is this book for? I believe it’s for intermediate to advanced Data Scientists. There’s so much “wisdom” that any reader should find value in the book.The code snippets are in Python and R. Sometimes those snippets are enough (e.g. power analysis). Sometimes the reader needs different sources to dig deeper (e.g. bootstrapping where I highly recommend infer in R). I believe this “compressed” approach is smart. Data science is too wide and deep and we must be able to dig deeper on our own.In other words, for a beginner, the code is often not enough to learn a new concept. Experienced Data Scientists should be able to judge from the code snippet if it’s enough.+++ Personal highlights: +++One of the best explanations on effect size I’ve ever seen (page 135).Sometimes, the statistics community uses different terms than the machine learning community. The authors seem to understand both (page 143).For example, in the last 10 years or so, we’ve seen a trend in statistics that favors data and simulations over classical probability theory and complex tests. But why would we use permutations in a hypothesis test? On page 139, the authors explain in succinctly in two sentences.In fact, the authors have a deep knowledge of resampling and how to use simulations over classical tests.The authors don’t try to confuse you. I’ve seen new books which used two pages to explain recall and then two pages to explain sensitivity. In this book, they don’t do it. Recall is the same as sensitivity (page 223).Another example is “Power and Sample Size.” In only four pages, the reader probably gets a good idea of the four moving parts: sample size, effect size, significance level and power. This stuff is hard and explaining it well is even harder.When cluster algorithms tend to give the same results and when not.Funny: “…regression comes with a baggage that is more relevant to its traditional role …”(page 161).Why would a Data Scientist care about heteroskedasticity? Page 183.Kudos