Data Science and Knowledge Discovery Using Machine Learning Methods

Oded Maimon (), Lior Rokach () and Erez Shmueli ()
Additional contact information
Oded Maimon: Tel-Aviv University, Department of Industrial Engineering
Lior Rokach: Ben-Gurion University of the Negev, Department of Software and Information Systems Engineering
Erez Shmueli: Tel-Aviv University, Department of Industrial Engineering

A chapter in Machine Learning for Data Science Handbook, 2023, pp 1-19 from Springer

Abstract: Abstract This introductory chapter aims to explain the KDD process and position machine learning within this process. Research and development challenges for the next generation of data science are also defined. The rationale, reasoning, and organization of the handbook are presented in this chapter for helping the reader to navigate the extremely rich and detailed content provided in this handbook.

Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-24628-9_1

Ordering information: This item can be ordered from
http://www.springer.com/9783031246289

DOI: 10.1007/978-3-031-24628-9_1

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().