Statistical Application Development with R and Python - Second Edition

Software Implementation Illustrated with R and Python
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Statistical Application Development with R and Python - Second Edition

Prabhanjan Narayanachar Tattar

Software Implementation Illustrated with R and Python
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Book Details

ISBN 139781788621199
Paperback432 pages

Book Description

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 end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python.

The data analysis journey begins with exploratory analysis, which is more than simple, descriptive, data summaries. You will then apply linear regression modeling, and end with logistic regression, CART, and spatial statistics.

By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.

Table of Contents

Chapter 1: Data Characteristics
Questionnaire and its components
Experiments with uncertainty in computer science
Installing and setting up R
Using R packages
Python installation and setup
IDEs for R and Python
The companion code bundle
Discrete distributions
Continuous distributions
Summary
Chapter 2: Import/Export Data
Packages and settings – R and Python
Understanding data.frame and other formats
Using utils and the foreign packages
Exporting data/graphs
Pop quiz
Summary
Chapter 3: Data Visualization
Packages and settings – R and Python
Visualization techniques for categorical data
Visualization techniques for continuous variable data
Pareto chart
A brief peek at ggplot2
Summary
Chapter 4: Exploratory Analysis
Packages and settings – R and Python
Essential summary statistics
Techniques for exploratory analysis
Summary
Chapter 5: Statistical Inference
Packages and settings – R and Python
Maximum likelihood estimator
Confidence intervals
Hypothesis testing
Summary
Chapter 6: Linear Regression Analysis
Packages and settings - R and Python
The essence of regression
The simple linear regression model
Multiple linear regression model
Regression diagnostics
Model selection
Summary
Chapter 7: Logistic Regression Model
Packages and settings – R and Python
Model validation and diagnostics
Logistic regression for the German credit screening dataset
Summary
Chapter 8: Regression Models with Regularization
Packages and settings – R and Python
Regression spline
Ridge regression for linear models
Summary
Chapter 9: Classification and Regression Trees
Packages and settings – R and Python
Splitting the data
Summary
Chapter 10: CART and Beyond
Packages and settings – R and Python
Understanding bagging
Summary

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

Authors

Table of Contents

Chapter 1: Data Characteristics
Questionnaire and its components
Experiments with uncertainty in computer science
Installing and setting up R
Using R packages
Python installation and setup
IDEs for R and Python
The companion code bundle
Discrete distributions
Continuous distributions
Summary
Chapter 2: Import/Export Data
Packages and settings – R and Python
Understanding data.frame and other formats
Using utils and the foreign packages
Exporting data/graphs
Pop quiz
Summary
Chapter 3: Data Visualization
Packages and settings – R and Python
Visualization techniques for categorical data
Visualization techniques for continuous variable data
Pareto chart
A brief peek at ggplot2
Summary
Chapter 4: Exploratory Analysis
Packages and settings – R and Python
Essential summary statistics
Techniques for exploratory analysis
Summary
Chapter 5: Statistical Inference
Packages and settings – R and Python
Maximum likelihood estimator
Confidence intervals
Hypothesis testing
Summary
Chapter 6: Linear Regression Analysis
Packages and settings - R and Python
The essence of regression
The simple linear regression model
Multiple linear regression model
Regression diagnostics
Model selection
Summary
Chapter 7: Logistic Regression Model
Packages and settings – R and Python
Model validation and diagnostics
Logistic regression for the German credit screening dataset
Summary
Chapter 8: Regression Models with Regularization
Packages and settings – R and Python
Regression spline
Ridge regression for linear models
Summary
Chapter 9: Classification and Regression Trees
Packages and settings – R and Python
Splitting the data
Summary
Chapter 10: CART and Beyond
Packages and settings – R and Python
Understanding bagging
Summary

Book Details

ISBN 139781788621199
Paperback432 pages
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