Data Science for Modeling Managerial and Socioeconomic Problems

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 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.

Acts as an essential guide to modern data science for students, practitioners, and managers Introduces concepts and skills that can help readers tackle real-world data analysis challenges, and presents interesting examples from real-life Serves as a reference book for special topics course in STEM certified MS in Engineering, Statistics, Economics and MBA programs

Auteur

Dr. Faiz Hamid is an Associate Professor at the Department of Management Sciences (erstwhile Department of Industrial and Management Engineering), Indian Institute of Technology (IIT) Kanpur. He earned his Ph.D. from the Indian Institute of Management Lucknow and completed a Postdoctoral Fellowship at Telecom SudParis, France. His research, published in reputable international journals, focuses on Combinatorial Optimization and Data Science with applications in Network Optimization, Transportation, and Sports. Prior to joining IIT Kanpur, he served as Functional Architect at JDA Software, Hyderabad (now Blue Yonder).

Dr. Deep Mukherjee is a Professor at the School of Economics and Public Policy, RV University and an Associate Professor in the Department of Economic Sciences, Indian Institute of Technology Kanpur. He obtained Ph.D. from the University of Connecticut and M.S. from the Indian Statistical Institute. His research and teaching interests lie in Applied Microeconomics and Econometrics. He has conducted significant field work as part of research projects in the form of extension visits, focus group discussions, and household surveys in various parts of India. Prior to joining Ph.D. program, he worked for GE Capital International Services (now, Genpact) as Business Analyst.

Contenu

Copulas and Dependence Modeling with Examples.- Causal Inference with Matching: Evaluation.- Anomaly Detection Methods: Application to Automated Vehicle Health Monitoring.