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 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
General
Imprint: |
O'Reilly Media
|
Country of origin: |
United States |
Release date: |
June 2020 |
Authors: |
Peter Bruce
• Andrew Bruce
• Peter Gedeck
|
Dimensions: |
233 x 178 x 21mm (L x W x T) |
Format: |
Paperback
|
Pages: |
350 |
Edition: |
2nd New edition |
ISBN-13: |
978-1-4920-7294-2 |
Categories: |
Books
|
LSN: |
1-4920-7294-X |
Barcode: |
9781492072942 |
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