Preface
The author has written this book based on his experience that spans roughly three decades in insurance, banking, and asset management. During his career, the author worked in IT, structured and managed highly technical investment portfolios (at some point oversaw €C24 billion in thousand investment funds), fulfilled many C-level roles (e.g. was CEO of KBCTFI SA [an asset manager in Poland], was CIO and COO for Eperon SA [a fund manager in Ireland] and sat on boards of investment funds, and was involved in big-data projects in London), and did quantitative analysis in risk departments of banks. This gave the author a unique and in-depth view of many areas ranging form analytics, big-data, databases, business requirements, financial modelling, etc.
In this book, the author presents a structured overview of his knowledge and experience for anyone whoworks with data and invites the reader to understand the bigger picture, and discover new aspects. This book also demystifies hype around machine learning and AI, by helping the reader to understand the models and programthem in R without spending toomuch time on the theory.
This book aims to be a starting point for quants, data scientists, modellers, etc. It aims to be the book that bridges different disciplines so that a specialist in one domain can grab this book, understand how his/her discipline fits in the bigger picture, and get enough material to understand the person who is specialized in a related discipline. Therefore, ...
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