Mathematical Foundations of Data Science Using R / Frank Emmert-Streib, Salissou Moutari, Matthias Dehmer.
By: Emmert-Streib, Frank [author.].
Contributor(s): Dehmer, Matthias [author.] | Moutari, Salissou [author.].
Material type: BookSeries: De Gruyter STEM.Publisher: München ; Wien : De Gruyter Oldenbourg, [2022]Copyright date: ©2022Description: 1 online resource (XVI, 408 p.).Content type: text Media type: computer Carrier type: online resourceISBN: 9783110796063.Subject(s): Computer science -- Mathematics | Computational Statistics | Data Analytics | Data Science | R for Genomics | COMPUTERS / Database Management / Data Mining | Computational Statistics | Data Analysis | Data Analytics | Data ScienceAdditional physical formats: No title; No titleDDC classification: 004.0151 Online resources: Click here to access online | Click here to access online | Cover Issued also in print.Frontmatter -- Preface to the second edition -- Contents -- 1 Introduction -- Part I: Introduction to R -- 2 Overview of programming paradigms -- 3 Setting up and installing the R program -- 4 Installation of R packages -- 5 Introduction to programming in R -- 6 Creating R packages -- Part II: Graphics in R -- 7 Basic plotting functions -- 8 Advanced plotting functions: ggplot2 -- 9 Visualization of networks -- Part III: Mathematical basics of data science -- 10 Mathematics as a language for science -- 11 Computability and complexity -- 12 Linear algebra -- 13 Analysis -- 14 Differential equations -- 15 Dynamical systems -- 16 Graph theory and network analysis -- 17 Probability theory -- 18 Optimization -- Bibliography -- Index
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The aim of the book is to help students become data scientists. Since this requires a series of courses over a considerable period of time, the book intends to accompany students from the beginning to an advanced understanding of the knowledge and skills that define a modern data scientist. The book presents a comprehensive overview of the mathematical foundations of the programming language R and of its applications to data science.
Issued also in print.
Mode of access: Internet via World Wide Web.
In English.
Description based on online resource; title from PDF title page (publisher's Web site, viewed 29. Mai 2023)
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