Data Science in Economics and Finance for Decision Makers

Edited By Per Nymand-Andersen 

Publisher RiskBooks.


Data Science in Economics and Finance 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 digitalization and Fintech.

This 400 page 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.

“Economies are complex, adaptive systems full of heterogeneous agents [and markets] whose interactions have thus far been almost impossible to discern.” With the advent of digitalization  or digital evolution (disruption), digital data and rapidly-developing technology are beginning to totally transform the worlds of finance and economics, and society at large.   The new world of Fintech has emerged, disrupting traditional banking and finance and it can no longer be ignored.    

Data Science in Economics and Finance provides an overview of how digital transformation and data science can support decision-making under uncertainty and provides multiple perspectives on managing digital data. Split into four sections this title offers essential insights both to practitioners of data-science tools and techniques as well as to policymakers, who are increasingly dependent on the use of digital data for aiding sustainable decisions for the collective benefit of society.

  • Part I: data science and quality in economics and finance: A broad overview of digitalization and digital transformation in economics and finance covering both their potential and challenges.
  • Part II: data science techniques: Describes the core concepts of data science tools and techniques, including machine learning, AI, network analysis and the expression of digital data using visual techniques.
  • Part III: data: from the experimental to generating insights: Provides an overview of the challenges of building data science infrastructures and provides several “big data” case studies in the public, private and academic sectors.
  • Part IV: digital data vision and the social benefits of big data: Sets out a vision for the free movement of digital data, and discusses the challenges for legal enablers, journalists and a human-centric approach to AI.

Data Science in Economics and Finance not only explores new techniques and tools which advance our understanding of systemic processes, such as AI, but also thoughtfully assesses the inadequacies and challenges put forth by the digital revolution, described as a “‘how to’ guide to the future of data and modelling” that will stimulate economists, statisticians, data scientists, central bankers, financial-market participants, regulators, Fintech firms and any decision-makers.”

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