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010   $a981-9790-60-3
024 7 $a10.1007/978-981-97-9060-9
035   $a(CKB)44890028500041
035   $a(MiAaPQ)EBC32474211
035   $a(Au-PeEL)EBL32474211
035   $a(DE-He213)978-981-97-9060-9
035   $a(EXLCZ)9944890028500041
100   $a20260106d2026      u|         0
101 0 $aeng
135   $aur|||||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aData Science for Modeling Managerial and Socioeconomic Problems $eConcepts, Techniques, and Applications /$fedited by Faiz Hamid, Deep Mukherjee
205   $a1st ed. 2026.
210  1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2026.
215   $a1 online resource (625 pages)
225 1 $aContributions to Management Science,$x2197-716X
311 08$a981-9790-59-X 
327   $aCopulas and Dependence Modeling with Examples -- Causal Inference with Matching: Evaluation -- Anomaly Detection Methods: Application to Automated Vehicle Health Monitoring.
330   $aThis book leverages statistical analysis, data mining, and machine learning techniques to address managerial and socioeconomic problems. With the advent of modern technologies, massive amount of data, especially big data, proliferate from business transactions and users. Consequently, there is an ever-increasing demand for analyzing the data and gaining valuable insights. This book comprises 15 chapters: the first ten chapters cover methods from Statistics and Econometrics, while the next five chapters delve into selected Machine Learning techniques. By bringing together the expertise of eminent researchers from reputed universities worldwide, this volume provides a cohesive guide to understanding and applying data science methodologies to real-world problems. The book assumes basic knowledge of probability and statistics. Each chapter presents a blend of theoretical insights and practical case studies, ensuring that readers not only learn the techniques but also see their relevance and implementation in real-world scenarios. The chapters not only cover the theoretical underpinnings in a student-friendly language but also provide step-by-step guides for implementation using various software tools such as R, Python, Matlab, and SPSS. This is to instill confidence in the reader to apply such techniques to real-life problems. The book is designed for a broad spectrum of readership - empirical economists, business analysts, and post-graduate students aiming to learn and practice data science. Moreover, the book is designed in such a way that it can be used as a practical reference book for one semester-long Data Science course.
410  0$aContributions to Management Science,$x2197-716X
606   $aOperations research
606   $aProduction management
606   $aBig data
606   $aQuantitative research
606   $aStatistics
606   $aOperations Research and Decision Theory
606   $aOperations Management
606   $aBig Data
606   $aData Analysis and Big Data
606   $aApplied Statistics
615  0$aOperations research.
615  0$aProduction management.
615  0$aBig data.
615  0$aQuantitative research.
615  0$aStatistics.
615 14$aOperations Research and Decision Theory.
615 24$aOperations Management.
615 24$aBig Data.
615 24$aData Analysis and Big Data.
615 24$aApplied Statistics.
676   $a658.4033
700   $aHamid$b Faiz$01781595
701   $aH?a?mid$01889064
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9911054587603321
996   $aData Science for Modeling Managerial and Socioeconomic Problems$94529110
997   $aUNINA