[Pdf/ePub] Advanced R Statistical Programming

Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Visualization by Matt Wiley, Joshua F. Wiley

• Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Visualization
• Matt Wiley, Joshua F. Wiley
• Page: 638
• Format: pdf, ePub, mobi, fb2
• ISBN: 9781484228715
• Publisher: Apress

Download Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Visualization

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Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about the techniques, includes multiple examples using R to achieve results, and concludes with a case study. Written by Matt and Joshua F. Wiley, Advanced R Statistical Programming and Data Models shows you how to conduct data analysis using the popular R language. You’ll delve into the preconditions or hypothesis for various statistical tests and techniques and work through concrete examples using R for a variety of these next-level analytics. This is a must-have guide and reference on using and programming with the R language. What You’ll Learn Conduct advanced analyses in R including: generalized linear models, generalized additive models, mixed effects models, machine learning, and parallel processing Carry out regression modeling using R data visualization, linear and advanced regression, additive models, survival / time to event analysis Handle machine learning using R including parallel processing, dimension reduction, and feature selection and classification Address missing data using multiple imputation in R Work on factor analysis, generalized linear mixed models, and modeling intraindividual variability Who This Book Is For Working professionals, researchers, or students who are familiar with R and basic statistical techniques such as linear regression and who want to learn how to use R to perform more advanced analytics. Particularly, researchers and data analysts in the social sciences may benefit from these techniques. Additionally, analysts who need parallel processing to speed up analytics are given proven code to reduce time to result(s).

1 Introduction - Introduction to Data Science
The book is divided into eight parts: R Basics, Data Visualization, Probability, Statistical Inference and Modeling, Data Wrangling, Regression, Machine Learning  Course Descriptions | Data Matters
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Discover best practices for data analysis and software development in R and start on the in R: Data Analysis, Visualization, and Modelling for the Data Scientist [ Book] is a combination of statistics, computational science, and machine learning. This requires computational methods and programming, and R is an ideal  Advanced R Statistical Programming and Data Models: Analysis
Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Visualization è un libro di Matt Wiley , Joshua F. Wiley pubblicato da  18 must read books for data scientists on R and Python
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What You'll Learn Conduct advanced analyses in R including: generalized out regression modeling using R data visualization, linear and advanced regression, additive models, survival / time to event analysis Handle machine learning using R Advanced R Statistical Programming and Data Models: Analysis, Machine  Is Python preferred over R for Data Analysis? – Mindfire Solutions
Many data analysts prefer Python and R to other programming languages. Likewise, they can use Seaborn to visualize statistical models. The advanced Python packages like TensorFlow, Theano and Keras further optimizes data analysis by leveraging machine learning and deep learning. On the other 

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