Increasing the Modelling & Simulations Community Capabilities Through the Use of Datascience: A Review

Abstract

Data science possesses the capacity to derive significant insights from extensive and complex databases. Nevertheless, the process of interpreting data science is an enormous challenge due to its consistent involvement with vast quantities of unorganised and diverse data. In this research, we have examined a crucial subject in the field of data science: the utilisation of interpretability tools. Our focus is on analysing data science models and methodologies, assessing their efficacy, and clarifying the decision-making process of machine learning algorithms. In summary, we recognise the necessity for additional investigation into interpretability tools to create resilient and transparent tools that will improve the interpretability and reliability of data science models and methodologies.