Buy new:
$45.00
FREE delivery: Tuesday, June 13
Ships from: Amazon
Sold by: AV02 Store
List Price: $106.95
Save: $61.95 (58%)
FREE delivery Tuesday, June 13. Details
Or fastest delivery Sunday, June 11. Order within 10 hrs 46 mins. Details
In Stock
[{"displayPrice":"$45.00","priceAmount":45.00,"currencySymbol":"$","integerValue":"45","decimalSeparator":".","fractionalValue":"00","symbolPosition":"left","hasSpace":false,"showFractionalPartIfEmpty":true,"offerListingId":"5Ri7Z7MUihyvNcGGXrYTJH1MuV12xOAJVa1MyRx7rMlHH2yKMVJ1hKzlKAJ4%2FAkwo%2B46%2ByIQLrU3Vr32mzA6CaiHmY%2F2uFrugtFaaOTtkR6maSLGAHme%2BlBaafTiMgX%2FRcsQJakKcuanege9uo3g53AEKcxrwh74ktAIOCfBJIcq3gAoDnA20gS%2BH8xkgYO9","locale":"en-CA","buyingOptionType":"NEW"},{"displayPrice":"$30.99","priceAmount":30.99,"currencySymbol":"$","integerValue":"30","decimalSeparator":".","fractionalValue":"99","symbolPosition":"left","hasSpace":false,"showFractionalPartIfEmpty":true,"offerListingId":"5Ri7Z7MUihyvNcGGXrYTJH1MuV12xOAJGnOpDjxDL9jATA88yhYLhXLqRnbcF0ue879Rc3DtSRXaPbEXN8%2BNy80p05%2FOltJsz3PzmPdrsv6DgiEJTxWH4P5IG6SOhx35j%2F7pkhh0RSbbzpiLp2yp11%2BWrHW5Cqg0jEP%2BuP8iQz6RJR94PpxMNzvFj%2FT5TF3d","locale":"en-CA","buyingOptionType":"USED"}]
$$45.00 () Includes selected options. Includes initial monthly payment and selected options. Details
Price
Subtotal
$$45.00
Subtotal
Initial payment breakdown
Shipping cost, delivery date and order total (including tax) shown at checkout.
Your transaction is secure
We work hard to protect your security and privacy. Our payment security system encrypts your information during transmission. We don’t share your credit card details with third-party sellers, and we don’t sell your information to others. Learn more
Return policy: Eligible for Return, Refund or Replacement within 30 days of receipt
This item can be returned in its original condition for a full refund or replacement within 30 days of receipt.
Practical+Statistics+for+... has been added to your Cart
FREE delivery Tuesday, June 13 on your first order. Details
Or fastest delivery Monday, June 12. Order within 18 hrs 1 min. Details
Used: Good | Details
Sold by KW Choice
Condition: Used: Good
Have one to sell?
Kindle app logo image

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 for Web.

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

QR code to download the Kindle app

Flip to back Flip to front
Listen Playing... Paused   You're listening to a sample of the Audible audio edition.
Learn more

Follow the Author

Something went wrong. Please try your request again later.

Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python Paperback – June 16 2020

4.5 4.5 out of 5 stars 854 ratings

Amazon Price
New from Used from
Kindle Edition
Paperback
$45.00
$44.98 $30.99

Purchase options and add-ons

Frequently bought together

$45.00
Get it by Tuesday, Jun 13
In Stock.
Sold by AV02 Store and ships from Amazon Fulfillment.
+
$61.44
Get it by Tuesday, Jun 13
In Stock.
Ships from and sold by Amazon.ca.
Total price:
To see our price, add these items to your cart.
Details
Added to Cart
These items are shipped from and sold by different sellers.
Choose items to buy together.

From the Publisher

Statistics, Data Scientists, python, r, data science

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.

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

Product details

  • Publisher ‏ : ‎ O'Reilly Media; 2 edition (June 16 2020)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 360 pages
  • ISBN-10 ‏ : ‎ 149207294X
  • ISBN-13 ‏ : ‎ 978-1492072942
  • Item weight ‏ : ‎ 590 g
  • Dimensions ‏ : ‎ 17.78 x 2.29 x 23.11 cm
  • Customer Reviews:
    4.5 4.5 out of 5 stars 854 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
854 global ratings

Top reviews from Canada

Reviewed in Canada 🇨🇦 on February 9, 2023
Verified Purchase
Reviewed in Canada 🇨🇦 on October 25, 2021
Verified Purchase
7 people found this helpful
Report
Reviewed in Canada 🇨🇦 on May 22, 2023
Verified Purchase
Reviewed in Canada 🇨🇦 on December 1, 2022
Verified Purchase
Reviewed in Canada 🇨🇦 on March 9, 2022
Verified Purchase
Customer image
4.0 out of 5 stars Black & white print
Reviewed in Canada 🇨🇦 on March 9, 2022
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.
Images in this review
Customer image Customer image
Customer imageCustomer image
One person found this helpful
Report
Reviewed in Canada 🇨🇦 on September 27, 2022
Verified Purchase
Reviewed in Canada 🇨🇦 on June 23, 2021
Verified Purchase
Reviewed in Canada 🇨🇦 on January 27, 2021
Verified Purchase
One person found this helpful
Report

Top reviews from other countries

Chris
5.0 out of 5 stars Excellent book for aspiring data scientists
Reviewed in the United Kingdom 🇬🇧 on November 3, 2021
Verified Purchase
One person found this helpful
Report
Cabiria
5.0 out of 5 stars Making sense of statistics
Reviewed in the United Kingdom 🇬🇧 on May 5, 2022
Verified Purchase
One person found this helpful
Report
Amazon Customer
3.0 out of 5 stars Good
Reviewed in the United Kingdom 🇬🇧 on March 18, 2021
Verified Purchase
One person found this helpful
Report
Laszlo Molnar
5.0 out of 5 stars Good explanations of complicated issues
Reviewed in the United Kingdom 🇬🇧 on February 25, 2021
Verified Purchase
One person found this helpful
Report
Dr. Franco Arda
5.0 out of 5 stars Deep knowledge of data science
Reviewed in Germany 🇩🇪 on July 14, 2020
Verified Purchase
Customer image
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.

Kudos
Images in this review
Customer image
Customer image
9 people found this helpful
Report