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Record Nr. |
UNINA9911054587603321 |
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Autore |
Hamid Faiz |
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Titolo |
Data Science for Modeling Managerial and Socioeconomic Problems : Concepts, Techniques, and Applications / / edited by Faiz Hamid, Deep Mukherjee |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2026 |
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ISBN |
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Edizione |
[1st ed. 2026.] |
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Descrizione fisica |
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1 online resource (625 pages) |
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Collana |
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Contributions to Management Science, , 2197-716X |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Operations research |
Production management |
Big data |
Quantitative research |
Statistics |
Operations Research and Decision Theory |
Operations Management |
Big Data |
Data Analysis and Big Data |
Applied Statistics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Copulas and Dependence Modeling with Examples -- Causal Inference with Matching: Evaluation -- Anomaly Detection Methods: Application to Automated Vehicle Health Monitoring. |
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Sommario/riassunto |
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This 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 |
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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. |
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