Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
Bruce, Peter/ Bruce, Andrew/ Gedeck, Peter
Language: English
Published by Oreilly & Associates Inc, 2020
- Softcover
- New
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
AbeBooks seller since January 6, 2003
Condition: New
£ 87.48
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Add to basketItem description from seller
2nd edition. 342 pages. 9.00x7.00x0.75 inches. In Stock.
Seller Inventory # zk149207294X
- Title
- Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
- Author
- Bruce, Peter/ Bruce, Andrew/ Gedeck, Peter
- Publisher
- Oreilly & Associates Inc
- Publication year
- 2020
- Condition
- Brand New
- Binding
- Paperback
- Language
- English
- ISBN 10
- 149207294X
- ISBN 13
- 9781492072942
- Edition
- 2nd Edition
- Item weight
- 0.62 kilograms
Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying 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 or Python programming languages 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
"Synopsis" may belong to another edition of this title.
About the Author
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
"About the title" may belong to another edition of this title.
Revaluation Books
Exeter, United Kingdom
AbeBooks seller since January 6, 2003
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