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.

## Book Details | |

Publisher: | O'Reilly Media |

By: | Peter Bruce, Andrew Bruce, Peter Gedeck |

ISBN-13: | 9781492072942 |

ISBN-10: | 149207294X |

Year: | 2020 |

Pages: | 368 |

Language: | English |

## Book Preview | |

Online | Practical Statistics for Data Scientists, 2nd Edition |

## Paper Book | |

Buy: | Practical Statistics for Data Scientists, 2nd Edition |

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