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.
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From the Publisher
From the Preface
This book is aimed at the data scientist with some familiarity with the R and/or Python programming languages, and with some prior (perhaps spotty or ephemeral) exposure to statistics. Two of the authors came to the world of data science from the world of statistics, and have some appreciation of the contribution that statistics can make to the art of data science. At the same time, we are well aware of the limitations of traditional statistics instruction: statistics as a discipline is a century and a half old, and most statistics textbooks and courses are laden with the momentum and inertia of an ocean liner. All the methods in this book have some connection—historical or methodological—to the discipline of statistics. Methods that evolved mainly out of computer science, such as neural nets, are not included.
In all cases, this book gives code examples first in R and then in Python. In order to avoid unnecessary repetition, we generally show only output and plots created by the R code. We also skip the code required to load the required packages and data sets. You can find the complete code as well as the data sets for download at GitHub.
Two goals underlie this book:
- To lay out, in digestible, navigable, and easily referenced form, key concepts from statistics that are relevant to data science.
- To explain which concepts are important and useful from a data science perspective, which are less so, and why.
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Amazon seuls the book to me like new. but it is used book. I'm disappointed.The value of the book is correct.4* because the lack of color in the book.
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Amazon seuls the book to me like new. but it is used book. I'm disappointed.
The value of the book is correct.
4* because the lack of color in the book.
The value of the book is correct.
4* because the lack of color in the book.
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Chris
Excellent book for aspiring data scientists
Reviewed in the United Kingdom 🇬🇧 on November 3, 2021Verified Purchase
Cabiria
Making sense of statistics
Reviewed in the United Kingdom 🇬🇧 on May 5, 2022Verified Purchase
Amazon Customer
Good
Reviewed in the United Kingdom 🇬🇧 on March 18, 2021Verified Purchase
Laszlo Molnar
Good explanations of complicated issues
Reviewed in the United Kingdom 🇬🇧 on February 25, 2021Verified Purchase
Dr. Franco Arda
Deep knowledge of data science
Reviewed in Germany 🇩🇪 on July 14, 2020Verified Purchase
Dr. Franco Arda
Reviewed in Germany 🇩🇪 on July 14, 2020
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.
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