Practical Statistics for Data Scientists

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“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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Readers' opinions about Practical Statistics for Data Scientists by Peter Bruce, Andrew Bruce & Peter Gedeck

The plot was incredibly well-crafted, with twists and turns that kept me hooked until the very last page. It was impossible to predict what would happen next.

The themes of resilience and hope resonated deeply with me. This book taught me valuable life lessons that I'll carry with me for years to come.

I loved the powerful messages hidden within the story. The book tackled important social issues and made me ponder the world we live in. It's a must-read for everyone.

More by Peter Bruce, Andrew Bruce & Peter Gedeck

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