Data Science Training for Finance and Risk Analysis: A Pedagogical Approach with Integrating Online Platforms

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The main discussion of this paper is a method of data science training,
which allows responding to the complex challenges of finance. To create and
deploy financial models for risk management, the ability to incorporate new
data and Big Data sources, as well as benefit from emerging technologies
such as web technologies, remote data collection methods, user experience
Platforms, and ensemble machine learning methods, becomes increasingly
important. Automating, analysing, and optimizing a set of complex financial
systems requires a wide range of skills and competencies that are rarely
taught in typical finance and econometrics courses. Adoption of these technologies for financial problems necessitates new skills, and knowledge about processes, quality assurance frameworks, technologies, security needs, privacy, and legal issues. In this paper, I discuss a pedagogical approach for
data science training in finance and risk analysis, with a graphical summary
of necessary skills. A case study of active learning and learning by doing
for financial data science course is presented, following with the results of a
teaching experience of this course, online and in-person, with a combination
of diferent technologies and platforms in an integrated manner. The outcomes
of an online Q/A on the Kaggle competition platform, an online book
club, an online video platform, and an online discussion group for this course
are presented with their advantages and disadvantages, and vulnerabilities.
Original languageEnglish
Number of pages2
Publication statusPublished - 1 Jul 2022
Event9th International Conference on Risk Analysis (ICRA9) - The University of Perugia and the University of Salerno, Perugia, Italy
Duration: 25 May 202227 May 2022
Conference number: 9th


Conference9th International Conference on Risk Analysis (ICRA9)
Abbreviated titleICRA9
Internet address


  • Data science
  • Finance
  • Risk
  • Pedagogical
  • Active learning


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