Buying Options

Kindle Price: $44.05
includes tax, if applicable

These promotions will be applied to this item:

Some promotions may be combined; others are not eligible to be combined with other offers. For details, please see the Terms & Conditions associated with these promotions.

Deliver to your Kindle or other device

Deliver to your Kindle or other device

<Embed>

Download the free Kindle app and start reading Kindle books instantly on your smartphone, tablet or computer – no Kindle device required. Learn more

Read instantly on your browser with Kindle Cloud Reader.

Using your mobile phone camera, scan the code below and download the Kindle app.

QR code to download the Kindle App

Enter your mobile phone or email address

Processing your request...

By pressing ‘Send link’, you agree to Amazon's Conditions of Use.

You consent to receive an automated text message from or on behalf of Amazon about the Kindle App at your mobile number above. Consent is not a condition of any purchase. Message and data rates may apply.

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

Follow the Author

Something went wrong. Please try your request again later.


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

4.5 out of 5 stars 244 ratings

Amazon Price
New from Used from
Kindle
$44.05
Due to its large file size, this book may take longer to download

Kindle Monthly Deals
Kindle Monthly Deals
New deals each month starting at $1.49. Learn more

Product description

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 (10 April 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
  • Print length ‏ : ‎ 631 pages
  • Customer Reviews:
    4.5 out of 5 stars 244 ratings

About the author

Follow authors to get new release updates, plus improved recommendations.
Brief content visible, double tap to read full content.
Full content visible, double tap to read brief content.

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.

Customer reviews

4.5 out of 5 stars
4.5 out of 5
244 global ratings

Review this product

Share your thoughts with other customers

Top review from Australia

Reviewed in Australia on 13 September 2020
Verified Purchase

Top reviews from other countries

Chris
5.0 out of 5 stars Excellent book for aspiring data scientists
Reviewed in the United Kingdom on 4 November 2021
Verified Purchase
Amazon Customer
3.0 out of 5 stars Good
Reviewed in the United Kingdom on 18 March 2021
Verified Purchase
Laszlo Molnar
5.0 out of 5 stars Good explanations of complicated issues
Reviewed in the United Kingdom on 25 February 2021
Verified Purchase
Franco Arda
5.0 out of 5 stars Deep knowledge of data science
Reviewed in Germany on 14 July 2020
Verified Purchase
Customer image
5.0 out of 5 stars Deep knowledge of data science
Reviewed in Germany on 14 July 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.

Kudos
Images in this review
Customer image
Customer image
4 people found this helpful
Report abuse
Komal Diwe
3.0 out of 5 stars Blank & White print..!
Reviewed in India on 26 February 2021
Verified Purchase
Customer image
3.0 out of 5 stars Blank & White print..!
Reviewed in India on 26 February 2021
At this huge price was expecting color print but got greyscale edition this disappointed me.
Book content is awesome but color print was expected.😢😢
Images in this review
Customer image
Customer image
8 people found this helpful
Report abuse