Associate Professor Dr Selina Successfully Registered Copyright of the C-PDERM Framework

In the data science life cycle, having a systematic process for extracting, transforming, and analyzing data is crucial. While the well-known CRISP-DM framework addresses key phases of a typical data science project, it does not cater to the unique requirements of specific data types, such as count data or text data. Count data is commonly encountered in many fields from healthcare to marketing but existing frameworks often focus on general regression methods (e.g., linear or logistic regression) or on isolated modelling stages. Recognizing this gap, the C-PDERM Framework was developed to integrate key methodologies for count data modelling, providing a comprehensive, step-by-step structure for handling correlated and longitudinal count data in data science projects.

This copyright registration marks an important milestone in advancing methodological innovation in data science, offering a robust and industry-relevant framework for researchers, practitioners, and educators.