Comparative Approaches to Using R and Python for Statistical Data Analysis

Business & Finance, Economics, Statistics, Nonfiction, Science & Nature, Mathematics
Cover of the book Comparative Approaches to Using R and Python for Statistical Data Analysis by Rui Sarmento, Vera Costa, IGI Global
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Author: Rui Sarmento, Vera Costa ISBN: 9781522519904
Publisher: IGI Global Publication: January 6, 2017
Imprint: Information Science Reference Language: English
Author: Rui Sarmento, Vera Costa
ISBN: 9781522519904
Publisher: IGI Global
Publication: January 6, 2017
Imprint: Information Science Reference
Language: English
The application of statistics has proliferated in recent years and has become increasingly relevant across numerous fields of study. With the advent of new technologies, its availability has opened into a wider range of users. Comparative Approaches to using R and Python for Statistical Data Analysis is a comprehensive source of emerging research and perspectives on the latest computer software and available languages for the visualization of statistical data. By providing insights on relevant topics, such as inference, factor analysis, and linear regression, this publication is ideally designed for professionals, researchers, academics, graduate students, and practitioners interested in the optimization of statistical data analysis.
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
The application of statistics has proliferated in recent years and has become increasingly relevant across numerous fields of study. With the advent of new technologies, its availability has opened into a wider range of users. Comparative Approaches to using R and Python for Statistical Data Analysis is a comprehensive source of emerging research and perspectives on the latest computer software and available languages for the visualization of statistical data. By providing insights on relevant topics, such as inference, factor analysis, and linear regression, this publication is ideally designed for professionals, researchers, academics, graduate students, and practitioners interested in the optimization of statistical data analysis.

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