[Epub] Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python Full
                      
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Book Description
 
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 scienceHow random sampling can
reduce bias and yield a higher-quality dataset, even with big dataHow the principles of
experimental design yield definitive answers to questionsHow to use regression to estimate
outcomes and detect anomaliesKey classification techniques for predicting which categories a
record belongs toStatistical machine learning methods that "learn" from dataUnsupervised
learning methods for extracting meaning from unlabeled data
 
 
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