Practical Statistics for Data Scientists: 50 Essential Concepts - No Cost Library

Practical Statistics for Data Scientists: 50 Essential Concepts 

   Author(s): Peter Bruce, Andrew Bruce  

Publisher: O’Reilly Media, Year: 2017

                                        

 Description: 

Statistical methods are a key component of data science, but very few data scientists have any formal training in statistics. The subject is seldom covered from a data science perspective by courses and books on basic statistics. This practical guide discusses how to apply different statistical methods to data science, shows you how to prevent their misuse, and provides you with guidance on what is important and what is not.
Many data science tools include statistical approaches but lack a deeper statistical perspective. This simple guide bridges the gap in an open, understandable format if you are familiar with the R programming language and have any exposure to statistics.

You'll understand, with this book:

• Why is an exploratory analysis of data a crucial preliminary phase in data science?
• How random sampling, even with big data, can reduce bias and produce a higher quality dataset
• How experimental design concepts produce conclusive answers to questions
• How to use regression to predict effects and discover anomalies
• Main typing techniques to predict which categories a record belongs to
• Methods of learning machines that ìlearnî from data
• Unsupervised methods of learning how to derive value from unlabeled data

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