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Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python Paperback – June 2 2020

4.5 out of 5 stars 711 ratings

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From the Publisher

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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

Product details

  • Publisher ‏ : ‎ O'Reilly Media; 2 edition (June 2 2020)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 368 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 out of 5 stars 711 ratings

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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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4.5 out of 5 stars
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711 global ratings

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4.0 out of 5 stars Black & white print
By Berlin 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.
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Reviewed in Canada 🇨🇦 on June 23, 2021
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2.0 out of 5 stars Good.. but looks used!
By BATOOL on December 29, 2021
Well definitely the content is the same but it looks used and I paid for a new one!
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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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5.0 out of 5 stars Making sense of statistics
Reviewed in the United Kingdom 🇬🇧 on May 5, 2022
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Laszlo Molnar
5.0 out of 5 stars Good explanations of complicated issues
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3.0 out of 5 stars Good
Reviewed in the United Kingdom 🇬🇧 on March 18, 2021
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Komal Diwe
3.0 out of 5 stars Blank & White print..!
Reviewed in India 🇮🇳 on February 26, 2021
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3.0 out of 5 stars Blank & White print..!
Reviewed in India 🇮🇳 on February 26, 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.😢😢
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