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Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python by [Peter Bruce, Andrew Bruce, Peter Gedeck]

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

4.5 out of 5 stars 772 ratings

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About the Author

Peter Bruce is the Founder and Chief Academic Officer of the Institute for Statistics Education at Statistics.com, which offers about 80 courses in statistics and analytics, roughly half of which are aimed at data scientists. He has authored or co-authored several books in statistics and analytics, and he earned his Bachelor’s degree at Princeton, and Masters degrees at Harvard and the University of Maryland.



Andrew Bruce, Principal Research Scientist at Amazon, has over 30 years of experience in statistics and data science in academia, government and business. The co-author of Applied Wavelet Analysis with S-PLUS, he earned his bachelor’s degree at Princeton, and PhD in statistics at the University of Washington



Peter Gedeck, Senior Data Scientist at Collaborative Drug Discovery, specializes in the development of machine learning algorithms to predict biological and physicochemical properties of drug candidates. Co-author of Data Mining for Business Analytics, he earned PhD’s in Chemistry from the University of Erlangen-Nürnberg in Germany and Mathematics from Fernuniversität Hagen, Germany

--This text refers to the paperback edition.

Product details

  • ASIN ‏ : ‎ B08712TT3F
  • Publisher ‏ : ‎ O'Reilly Media; 2 edition (April 10 2020)
  • Language ‏ : ‎ English
  • File size ‏ : ‎ 17693 KB
  • Simultaneous device usage ‏ : ‎ Unlimited
  • Text-to-Speech ‏ : ‎ Enabled
  • Enhanced typesetting ‏ : ‎ Enabled
  • X-Ray ‏ : ‎ Not Enabled
  • Word Wise ‏ : ‎ Not Enabled
  • Sticky notes ‏ : ‎ On Kindle Scribe
  • Print length ‏ : ‎ 631 pages
  • Customer Reviews:
    4.5 out of 5 stars 772 ratings

About the author

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Dr. Peter Gedeck holds a Ph.D. in chemistry. He worked for twenty years as a computational chemist in drug discovery at Novartis in the United Kingdom, Switzerland, and Singapore. His research interests include the application of statistical and machine learning methods to problems in drug discovery. He is a scientist in the research informatics team at Collaborative Drug Discovery, which offers the pharmaceutical industry cloud-based software to manage the huge amount of data involved in the drug discovery process.

Peter’s specialty is the development of machine learning algorithms to predict biological and physicochemical properties of drug candidates. His scientific work is published in more than 50 peer reviewed articles.

Peter also teaches at University of Virginia's School of Data Science and gives a series of courses on Predictive Analytics at Statistics.com.

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Chris
5.0 out of 5 stars Excellent book for aspiring data scientists
Reviewed in the United Kingdom 🇬🇧 on November 3, 2021
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Cabiria
5.0 out of 5 stars Making sense of statistics
Reviewed in the United Kingdom 🇬🇧 on May 5, 2022
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3.0 out of 5 stars Good
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Laszlo Molnar
5.0 out of 5 stars Good explanations of complicated issues
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Dr. Franco Arda
5.0 out of 5 stars Deep knowledge of data science
Reviewed in Germany 🇩🇪 on July 14, 2020
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Dr. Franco Arda
5.0 out of 5 stars Deep knowledge of data science
Reviewed in Germany 🇩🇪 on July 14, 2020
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.

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