Result: Elements of data science, machine learning, and artificial intelligence using R

Title:
Elements of data science, machine learning, and artificial intelligence using R
Authors:
Emmert-Streib, Frank, Moutari, Salissou, Dehmer, Matthias
Source:
Emmert-Streib, F, Moutari, S & Dehmer, M 2023, Elements of data science, machine learning, and artificial intelligence using R. Springer Cham. https://doi.org/10.1007/978-3-031-13339-8
Publisher Information:
Springer Cham
Publication Year:
2023
Collection:
Queen's University Belfast: Research Portal
Subject Terms:
/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being, name=SDG 3 - Good Health and Well-being, /dk/atira/pure/sustainabledevelopmentgoals/quality_education, name=SDG 4 - Quality Education, /dk/atira/pure/sustainabledevelopmentgoals/industry_innovation_and_infrastructure, name=SDG 9 - Industry, Innovation, and Infrastructure, /dk/atira/pure/sustainabledevelopmentgoals/sustainable_cities_and_communities, name=SDG 11 - Sustainable Cities and Communities, /dk/atira/pure/sustainabledevelopmentgoals/responsible_consumption_and_production, name=SDG 12 - Responsible Consumption and Production, /dk/atira/pure/sustainabledevelopmentgoals/decent_work_and_economic_growth, name=SDG 8 - Decent Work and Economic Growth
Document Type:
Book book
Language:
English
ISBN:
978-3-031-13338-1
978-3-031-13341-1
3-031-13338-2
3-031-13341-2
Relation:
urn:ISBN:9783031133381; urn:ISBN:9783031133411
DOI:
10.1007/978-3-031-13339-8
Availability:
https://pure.qub.ac.uk/en/publications/a831a8e0-b958-4f0e-8be1-e592afe6fdc1
https://doi.org/10.1007/978-3-031-13339-8
Rights:
info:eu-repo/semantics/closedAccess
Accession Number:
edsbas.70283249
Database:
BASE

Further Information

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.