Statistical Application Development with R and Python - Second Edition

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  • 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

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.

  • 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.
Page Count 432
Course Length 12 hours 57 minutes
ISBN 9781788621199
Date Of Publication 30 Aug 2017


Prabhanjan Narayanachar Tattar

Prabhanjan Narayanachar Tattar is a lead statistician and manager at the Global Data Insights & Analytics division of Ford Motor Company, Chennai. He received the IBS(IR)-GK Shukla Young Biometrician Award (2005) and Dr. U.S. Nair Award for Young Statistician (2007). He held SRF of CSIR-UGC during his PhD. He has authored books such as Statistical Application Development with R and Python, 2nd Edition, Packt; Practical Data Science Cookbook, 2nd Edition, Packt; and A Course in Statistics with R, Wiley. He has created many R packages.