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Book Review: Encyclopedia of Data Science and Machine Learning (5 Volumes)

Leigh Wang

Source Title: International Journal of Business Analytics (IJBAN) 10(1)

DOI: 10.4018/IJBAN.319321

Article PDF Download Open access articles are freely available for download

Abstract

The Encyclopedia of Data Science and Machine Learning (EDSML) examines current, state-of-the-art research in the areas of data science, ML, data mining (DM), optimization, artificial intelligence (AI), statistics, and the interactions, linkages, and applications of knowledge-based business with information systems. It provides an international forum for practitioners, educators, and researchers to advance the knowledge and practice of all facets of BDML, emphasizing emerging theories, principles, models, processes, and applications to inspire and circulate cutting-edge findings into research, business, and communities. This encyclopedia contains a collection of 187 high-quality chapters, which were written by an international team of more than 370 experts representing leading scientists and talented young scholars from more than 46 countries and regions.

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Volume 11: 1 Issue (2024)

Volume 10: 1 Issue (2023)

Volume 9: 6 Issues (2022): 4 Released, 2 Forthcoming

Volume 8: 4 Issues (2021)

Volume 7: 4 Issues (2020)

Volume 6: 4 Issues (2019)

Volume 5: 4 Issues (2018)

Volume 4: 4 Issues (2017)

Volume 3: 4 Issues (2016)

Volume 2: 4 Issues (2015)

Volume 1: 4 Issues (2014)

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Book Review: Encyclopedia of Data Science and Machine Learning (5 Volumes)

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