Practical statistics for data scientists : 50 essential concepts Using R and Python

By:

Bruce, Peter C

Contributor(s):

Material type: Text

TextPublication details: Mumbai O'Reilly Shroff Publishers & distributors Pvt.Ltd. 2020Edition: 2nd edDescription: xvi, 342pISBN:

9788194435006 (pbk)

Subject(s):

DDC classification:

001.422 BRU

Summary: "All Indian Reprints of O'Reilly are printed in Grayscale 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 More"

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Book	Plaksha University Library	General Book	001.422 BRU (Browse shelf(Opens below))	Checked out	17/09/2024	003960

https://www.shroffpublishers.com/books/9788194435006/

"All Indian Reprints of O'Reilly are printed in Grayscale

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
More"

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