Elements of Data Science, Machine Learning, and Artificial Intelligence Using R

Frank Emmert-Streib; Salissou Moutari; Matthias Dehmer

doi:10.1007/978-3-031-13339-8

Elements of Data Science, Machine Learning, and Artificial Intelligence Using R

Frank Emmert-Streib
, Salissou Moutari
, Matthias Dehmer

Tampere University
Queen's University Belfast
Swiss Distance University of Applied Sciences

Research output: Book/Report › Book › peer-review

Abstract

The textbook provides students with tools they need to analyze complex data using methods from data science, machine learning and artificial intelligence. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. The authors cover all three main components of data science: computer science; mathematics and statistics; and domain knowledge. The book presents methods and implementations in R side-by-side, allowing the immediate practical application of the learning concepts. Furthermore, this teaches computational thinking in a natural way. The book includes exercises, case studies, Q&A and examples.

Original language	English
Publisher	Springer International Publishing AG
Number of pages	575
ISBN (Electronic)	9783031133398
ISBN (Print)	9783031133381
DOIs	https://doi.org/10.1007/978-3-031-13339-8
Publication status	Published - 1 Jan 2023
Externally published	Yes

Keywords

Algorithms
Bayesian analysis
data driven sciences
Data science
Learning from data
Prediction models

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10.1007/978-3-031-13339-8

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abstract = "The textbook provides students with tools they need to analyze complex data using methods from data science, machine learning and artificial intelligence. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. The authors cover all three main components of data science: computer science; mathematics and statistics; and domain knowledge. The book presents methods and implementations in R side-by-side, allowing the immediate practical application of the learning concepts. Furthermore, this teaches computational thinking in a natural way. The book includes exercises, case studies, Q\&A and examples.",

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author = "Frank Emmert-Streib and Salissou Moutari and Matthias Dehmer",

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AB - The textbook provides students with tools they need to analyze complex data using methods from data science, machine learning and artificial intelligence. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. The authors cover all three main components of data science: computer science; mathematics and statistics; and domain knowledge. The book presents methods and implementations in R side-by-side, allowing the immediate practical application of the learning concepts. Furthermore, this teaches computational thinking in a natural way. The book includes exercises, case studies, Q&A and examples.

KW - Algorithms

KW - Bayesian analysis

KW - data driven sciences

KW - Data science

KW - Learning from data

KW - Prediction models

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DO - 10.1007/978-3-031-13339-8

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SN - 9783031133381

BT - Elements of Data Science, Machine Learning, and Artificial Intelligence Using R

PB - Springer International Publishing AG

ER -

Elements of Data Science, Machine Learning, and Artificial Intelligence Using R

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Keywords

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