Practical Statistics for Data Scientists Using R and Python

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5/5 - (1 vote)

Practical Statistics for Data Scientists Using R and Python. Statistical procedures are an essential aspect of data science, although only a small percentage of data scientists have had formal statistical training. Introductory statistics courses and books rarely tackle the issue from a data science perspective.

Sumamry Practical Statistics for Data Scientists Book

This popular guide’s second edition includes:

  • Detailed Python examples.
  • Practical help on applying statistical approaches to data science.
  • Advice on what’s important and what’s not.
  • Advice on what’s important and what’s not.

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Many data science tools include statistical approaches, but they do not provide a comprehensive statistical perspective. This fast reference bridges the gap in an accessible, readable way if you’re familiar with the R or Python computer languages and have some exposure to statistics.

Practical Statistics for Data Scientists Amazon

  • 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.

Release Date Practical Statistics for Data Scientists Ebook

Book Name:

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

Publisher:O’Reilly Media; 2nd edition?
Publishing Date(June 2, 2020)
Language :English
File Size: / Pages368

About Author: Practical Statistics for Data Scientists Ebook

Peter Bruce is the Founder and Chief Academic Officer of’s Institute for Statistics Education, which offers over 80 statistics and analytics courses, with about half of them geared toward data scientists. He received his Bachelor’s degree from Princeton and his Masters’s degrees from Harvard and the University of Maryland. He has produced or co-authored several works on statistics and analytics.

Andrew Bruce, Amazon’s Principal Research Scientist, has worked in academia, government, and business for over 30 years, specializing in statistics and data science. He got his Bachelor’s degree at Princeton and his Ph.D. in statistics at the University of Washington and co-author of Applied Wavelet Analysis with S-PLUS.

Conclusion: Practical Statistics for Data Scientists Review

The book is well-thought-out, and the principles are well-explained. The subtitle is a little deceptive, implying that the book equally covers R and Python. The reality is that it focuses mainly on the R programming language, with Python code being an afterthought.

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