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Practical Statistics for Data Scientists: 50 Essential Concepts by [Bruce, Peter, Bruce, Andrew]
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Practical Statistics for Data Scientists: 50 Essential Concepts 1st Edition, Kindle Edition

4.0 out of 5 stars 1 customer review

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Length: 320 pages Enhanced Typesetting: Enabled Page Flip: Enabled
Language: English

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Product description

Product Description

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.

Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.

With this book, you’ll learn:

  • Why exploratory data analysis is a key preliminary step in data science
  • How random sampling can reduce bias and yield a higher quality dataset, even with big data
  • How the principles of experimental design yield definitive answers to questions
  • How to use regression to estimate outcomes and detect anomalies
  • Key classification techniques for predicting which categories a record belongs to
  • Statistical machine learning methods that “learn” from data
  • Unsupervised learning methods for extracting meaning from unlabeled data

About the Author

Peter Bruce founded and grew the Institute for Statistics Education at Statistics.com, which now offers about 100 courses in statistics, roughly a third of which are aimed at the data scientist. In recruiting top authors as instructors and forging a marketing strategy to reach professional data scientists, Peter has developed both a broad view of the target market, and his own expertise to reach it.

Andrew Bruce has over 30 years of experience in statistics and data science in academia, government and business. He has a Ph.D. in statistics from the University of Washington and published numerous papers in refereed journals. He has developed statistical-based solutions to a wide range of problems faced by a variety of industries, from established financial firms to internet startups, and offers a deep understanding the practice of data science.


Product details

  • Format: Kindle Edition
  • File Size: 21119 KB
  • Print Length: 318 pages
  • Simultaneous Device Usage: Unlimited
  • Publisher: O'Reilly Media; 1 edition (10 May 2017)
  • Sold by: Amazon Australia Services, Inc.
  • Language: English
  • ASIN: B071NVDFD6
  • Text-to-Speech: Enabled
  • X-Ray:
  • Word Wise: Not Enabled
  • Enhanced Typesetting: Enabled
  • Average Customer Review: 4.0 out of 5 stars 1 customer review
  • Amazon Bestsellers Rank: #104,029 Paid in Kindle Store (See Top 100 Paid in Kindle Store)

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7 March 2019
Format: Kindle EditionVerified Purchase

Most helpful customer reviews on Amazon.com

Amazon.com: 4.1 out of 5 stars 55 reviews
Aaron Lipeles
3.0 out of 5 stars* Decent review of core concepts * Good coverage of importance of distinguishing between sample and population ...
19 April 2018 - Published on Amazon.com
Verified Purchase
57 people found this helpful.
Math Person
5.0 out of 5 starsExcellent introductory textbook for data scientists (and students)
14 July 2017 - Published on Amazon.com
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15 people found this helpful.
Chris H
5.0 out of 5 starsI love this book as a reference
6 January 2018 - Published on Amazon.com
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13 people found this helpful.
Olga V
4.0 out of 5 starsA modern and very readable book that nicely explains high-level concepts.
14 November 2018 - Published on Amazon.com
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6 people found this helpful.
Terran
4.0 out of 5 starsVery well-written survey. Does not go into enough depth in any area to create proficiency.
14 June 2017 - Published on Amazon.com
Verified Purchase
30 people found this helpful.