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.
Price: 41 USD
Location: Delcevo
End Time: 2024-11-05T01:59:12.000Z
Shipping Cost: 13 USD
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Item Specifics
Restocking Fee: No
Return shipping will be paid by: Buyer
All returns accepted: Returns Accepted
Item must be returned within: 30 Days
Refund will be given as: Money Back
Subject: Statistics
Item Length: 9.2in
Item Height: 0.9in
Item Width: 7in
Author: Peter Bruce, Peter Gedeck, Andrew Bruce
Publication Name: Practical Statistics for Data Scientists : 50+ Essential concepts Using Rand Python
Format: Trade Paperback
Language: English
Publisher: O'reilly Media, Incorporated
Publication Year: 2020
Type: Textbook
Item Weight: 22.3 Oz
Number of Pages: 350 Pages
