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[null,null,"Autore",null,"Mailund Thomas"]
[null,null,"Titolo",null,"Beginning data science in R 4 : data analysis, visualization, and modelling for the data scientist / / Thomas Mailund"]
[null,null,"Pubbl/distr/stampa",null,"New York, NY : , : Apress Media, LLC, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"1-4842-8155-1"]
[null,null,"Edizione",null,"[Second edition.]"]
[null,null,"Descrizione fisica",null,"1 online resource (528 pages) : illustrations"]
[null,null,"Disciplina",null,"519.502855133"]
[null,null,"Soggetti",null,"R (Computer program language)"]
[{"$nulls":4},"Statistics - Data processing"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,null,"Nota di bibliografia","Includes index"]
[null,null,"Nota di contenuto",null,"1: Introduction 2: Introduction to R Programming 3: Reproducible Analysis 4: Data Manipulation 5: Visualizing Data 6: Working with Large Data Sets 7: Supervised Learning 8: Unsupervised Learning 9: Project 1: Hitting the Bottle 10: Deeper into R Programming 11: Working with Vectors and Lists 12: Functional Programming 13: Object-Oriented  Programming 14: Building an R Package 15: Testing and Package Checking 16: Version Control 17: Profiling and Optimizing 18: Project 2: Bayesian Linear Progression 19: Conclusions"]
[null,null,null,"Sommario/riassunto","Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. Updated for the R 4.0 release, this book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R 4, Second Edition details how data science is a combination of statistics, computational science, and machine learning. You'll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. Modern data analysis requires computational skills and usually a minimum of programming. After reading and using this book, you'll have what you need to get started with R programming with data science applications. Source code will be available to support your next projects as well."]