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This text provides an accessible guide to the tools and techniques of data science that can be utilised for the analysis of complex systems. This text is an invaluable resource for scientists, engineers and social scientists who require effective analysis of large quantities of data.
List of contents
Preface; Part I. Introduction: 1. Facets of complex systems; Part II. Heterogeneity and Dependence: 2. Quantifying heterogeneity: Classical and Bayesian statistics; 3. Statistical analyses of time-varying phenomena; Part III. Patterns and Interlinkages: 4. Pattern recognition in complex systems: machine learning; 5. Interlinkages and heterogeneity: network theory. Part IV. Emergence: from Micros to Macro: 6. Interaction and emergence: agent-based models; 7. Epilogue; References; Index.
About the author
Anindya S. Chakrabarti is an Associate Professor of Economics and UTI Chair of Macroeconomics at the Indian Institute of Management Ahmedabad. His main research interests are macroeconomics, big data in economics, time series econometrics, network theory and complex systems.K. Shuvo Bakar is Senior Lecturer at the University of Sydney. His research interests are Bayesian modelling and computation to reduce uncertainty in inferential statements. He works on statistical machine learning methods and applications to real-life data-driven problems.Anirban Chakraborti is Dean of Research at the School of Engineering and Technology at BML Munjal University, India. His main research interests lie in the areas of econophysics, data science, quantum physics and nanomaterial science.
Summary
This book provides an accessible guide to the tools and techniques of data science that can be utilised for the analysis of complex systems. This text is an invaluable resource for scientists, engineers and social scientists who require effective analysis of large quantities of data.
