Statistical Application Development with R and Python - Second Edition.

Statistical Application Development with R and Python - Second Edition.

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Bibliographic Details
Author / Creator:Tattar, Prabhanjan Narayanachar.
Edition:2nd ed.
Imprint:Birmingham : Packt Publishing, 2017.
Description:1 online resource (432 pages)
Language:English
Subject:
Application software -- Development.
Application software -- Development.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/12398963
Hidden Bibliographic Details
ISBN:9781788622264
178862226X
Notes:Print version record.
Summary:Software Implementation Illustrated with R and Python About This Book Learn the nature of data through software which takes the preliminary concepts right away using R and Python. Understand data modeling and visualization to perform efficient statistical analysis with this guide. Get well versed with techniques such as regression, clustering, classification, support vector machines and much more to learn the fundamentals of modern statistics. Who This Book Is For If you want to have a brief understanding of the nature of data and perform advanced statistical analysis using both R and Python, then this book is what you need. No prior knowledge is required. Aspiring data scientist, R users trying to learn Python and vice versa What You Will Learn Learn the nature of data through software with preliminary concepts right away in R Read data from various sources and export the R output to other software Perform effective data visualization with the nature of variables and rich alternative options Do exploratory data analysis for useful first sight understanding building up to the right attitude towards effective inference Learn statistical inference through simulation combining the classical inference and modern computational power Delve deep into regression models such as linear and logistic for continuous and discrete regressands for forming the fundamentals of modern statistics Introduce yourself to CART - a machine learning tool which is very useful when the data has an intrinsic nonlinearity In Detail Statistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions. This book explores statistical concepts along with R and Python, which are well integrated from the word go. Almost every concept has an R code going with it which exemplifies the strength of R and applications. The R code and programs have been further strengthened with equivalent Python programs. Thus, you will first understand the data characteristics, descriptive statistics and the exploratory attitude, which will give you firm footing of data analysis. Statistical inference will complete the technical footing of statistical methods. Regression, linear, logistic modeling, and CART, builds the essential toolkit. This will help you complete complex problems in the real world. You will begin with a brief understanding of the nature of data and e ...
Other form:Print version: Tattar, Prabhanjan Narayanachar. Statistical Application Development with R and Python - Second Edition. Birmingham : Packt Publishing, ©2017
Table of Contents:
  • Cover; Copyright; Credits; About the Author; Acknowledgment; About the Reviewers; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Data Characteristics; Questionnaire and its components; Understanding the data characteristics in an R environment; Experiments with uncertainty in computer science; Installing and setting up R; Using R packages; RSADBE
  • the books R package; Python installation and setup; Using pip for packages; IDEs for R and Python; The companion code bundle; Discrete distributions; Discrete uniform distribution; Binomial distribution.
  • Hypergeometric distributionNegative binomial distribution; Poisson distribution; Continuous distributions; Uniform distribution; Exponential distribution; Normal distribution; Summary; Chapter 2: Import/Export Data; Packages and settings
  • R and Python; Understanding data.frame and other formats; Constants, vectors, and matrices; Time for action
  • understanding constants, vectors, and basic arithmetic; What just happened?; Doing it in Python; Time for action
  • matrix computations; What just happened?; Doing it in Python; The list object; Time for action
  • creating a list object.
  • What just happened?The data.frame object; Time for action
  • creating a data.frame object; What just happened?; Have a go hero; The table object; Time for action
  • creating the Titanic dataset as a table object; What just happened?; Have a go hero; Using utils and the foreign packages; Time for action
  • importing data from external files; What just happened?; Doing it in Python; Importing data from MySQL; Doing it in Python; Exporting data/graphs; Exporting R objects; Exporting graphs; Time for action
  • exporting a graph; What just happened?; Managing R sessions.
  • Time for action
  • session managementWhat just happened?; Doing it in Python; Pop quiz; Summary; Chapter 3: Data Visualization; Packages and settings
  • R and Python; Visualization techniques for categorical data; Bar chart; Going through the built-in examples of R; Time for action
  • bar charts in R; What just happened?; Doing it in Python; Have a go hero; Dot chart; Time for action
  • dot charts in R; What just happened?; Doing it in Python; Spine and mosaic plots; Time for action
  • spine plot for the shift and operator data; What just happened?
  • Time for action
  • mosaic plot for the Titanic datasetWhat just happened?; Pie chart and the fourfold plot; Visualization techniques for continuous variable data; Boxplot; Time for action
  • using the boxplot; What just happened?; Doing it in Python; Histogram; Time for action
  • understanding the effectiveness of histograms; What just happened?; Doing it in Python; Have a go hero; Scatter plot; Time for action
  • plot and pairs R functions; What just happened?; Doing it in Python; Have a go hero; Pareto chart; A brief peek at ggplot2; Time for action
  • qplot; What just happened?
  • Time for action
  • ggplot.

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