Foreword
Thomas H Davenport
Foreword
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
I am happy to see this book. Despite economics traditionally being the social science with perhaps the heaviest use of data, statistical analysis and equations, economics and public finance have not been at the forefront of the adoption of data science. A variety of reasons have been advanced for this: some argue that economists have tended to focus on official government data, while data scientists prefer data from consumer behaviour; others say that economists primarily focus on causality, while data scientists primarily care about accurate prediction. Or perhaps another key difference is that data scientists tend to use open source programming tools to analyse relatively unstructured data, whereas economists use statistical analysis software to analyse structured data.
Whatever the underlying reason, I have noticed the following straws in the wind suggesting a historical gap between these fields.
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Data scientists are not terribly likely to have economics as their academic background; one study suggests that 13% have a degree in economics, but I suspect the numbers for post-doctoral degrees are smaller.
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Data science was slow to develop a foothold in
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