Teaching Note—Data Science Training for Finance and Risk Analysis: a Pedagogical Approach with Integrating Online Platforms

EasyChair Preprint 8692

Abstract

The main discussion of this paper is a method of data science training, which allows responding to the complex challenges of finance. There is growing recognition of the importance of creating and deploying financial models for risk management, incorporating new data and Big Data sources, and benefiting from emerging technologies such as web technologies, remote data collection methods, user experience Platforms, and ensemble machine learning methods in finance and risk management. Automating, analyzing, 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. Adopting these technologies for financial problems necessitates new skills and knowledge about processes, quality assurance frameworks, technologies, security needs, privacy, and legal issues. This paper discusses 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 the results of a teaching experience, online and in-person, with a combination of different technologies and platforms in an integrated manner. The outcomes of an online Q/A on the Kaggle competition platform, a book club, a video platform, and a discussion group for teaching data science for finance are presented with their advantages, disadvantages, and vulnerabilities.

Keyphrases: Data Science, Finance, Risk, active learning, pedagogical

@booklet{EasyChair:8692,
  author    = {Afshin Ashofteh},
  title     = {Teaching Note—Data Science Training for Finance and Risk Analysis: a Pedagogical Approach with Integrating Online Platforms},
  howpublished = {EasyChair Preprint 8692},
  year      = {EasyChair, 2022}}