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Data Science in Economics and Finance for Decision Makers
Discipline: Operational Risk, Regulation
First published:
ISBN: 978-1-78272-394-3
Data Science in Economics and Finance for Decision Makers is a key resource for any financial-market participant, policy-maker, central banker, economist or decision-maker required to understand the impact and opportunities presented by the transformation of digitalisation and fintech.
This book is a comprehensive overview of the data-science tools and techniques that already exist and that are emerging. ECB stalwart Per Nymand-Andersen has brought together over 20 global experts from both the private and public sectors, as well as authors from academia and the media in this expansive title, helping the reader to better understand the impact of digital data and the significant changes international economies and financial markets are undergoing, and the new challenges these changes pose.
Contents
Foreword
Introduction
Digitalisation and transformation in economics and finance
Big data for policymaking in economics and finance: the potential and challenges
Quality matters: for insightful quality advice, get to know your big data
Statistics and machine learning: variations on a theme
Advanced statistical analysis of large-scale Web-based data
Text analysis
Prudential stress testing in financial networks
Data visualization: developing capabilities to make decisions and communicate
Data science in economics and finance: tools, infrastructure and challenges
Data science and machine learning for a data-driven central bank
Large-scale commercial data for economic analysis
Artificial intelligence and data are transforming the modern newsroom: a Bloomberg case study
Implementing big data solutions
A borderless market for digital data
Legal/ethical aspects and privacy: enabling free data flows
Assessing the trustworthiness of artificial intelligence
“Big tech”, journalism and the future of knowledge