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

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


Advanced-R-Statistical.pdf
ISBN: 9781484228715 | 638 pages | 16 Mb

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


Pdf english books free download Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Visualization 9781484228715 (English Edition)

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).

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Spatial Analysis With R; Predictive Modeling and Machine Learning With R This 2-day hands-on workshop gets participants up and running with R statistical software. the solid foundation needed to conduct data analysis and visualization for data science. Day 2: Intermediate and advanced R programming topics. Advanced R Statistical Programming and Data Models - Analysis
Advanced R Statistical Programming and Data Models. Analysis, Machine Learning, and Visualization. Autoren: Wiley, Matt, Wiley, Joshua F. Demonstrates   Rated R: Recommended Reading (Revolutions) - Revolution Analytics
All of these books will provide windows into learning R for. Advanced R by Hadley Wickham - Anyone who wants to gain a deep understanding The Art of R Programming: A Tour of Statistical Software Design by Norman make correct inferences from your data and machine learning models as well as  Advanced R Statistical Programming and Data Models: Analysis
Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Carry out regression modeling using R data visualization, linear and Handle machine learning using R including parallel processing, dimension  Data Science Courses: R & Python Analysis Tutorials | DataCamp
Learn to train and assess models performing common machine learning tasks such as This course covers some advanced topics including strategies for handling large Apply statistical modeling in a real-life setting using logistic regression and Learn how to analyze and visualize network data in the R programming  Functional Programming in R: Advanced Statistical Programming for
Functional Programming in R: Advanced Statistical Programming for Data Science, Analysis and Finance | Thomas Mailund | ISBN: Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Visualization.



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