Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.

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.

## Book Details | |

Publisher: | O'Reilly Media |

By: | Peter Bruce, Andrew Bruce |

ISBN-13: | 9781491952962 |

ISBN-10: | 1491952962 |

Year: | 2017 |

Pages: | 318 |

Language: | English |

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Online | Practical Statistics for Data Scientists |

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Example Code | Practical Statistics for Data Scientists |

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Buy: | Practical Statistics for Data Scientists |

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