Abstract:
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Data Science (DS) as a discipline seeks to bring new approaches to further scientific understanding and elucidate essential relationships with an emphasis on “large” datasets. The tools of DS are highly computational and do not follow traditional statistical approaches. In pharmaceutical engineering and research, large datasets are found throughout the drug development process, in both clinical and nonclinical studies. Therefore these represent fertile areas for the application of DS approaches, related to AI, ML and others. Standard traditional statistical approaches are giving way to these newer methods, and consequently important questions arise regarding the respective roles of Statistics and DS. This round table seeks to bring together individuals from both disciplines to discuss the following questions among others : 1. What is DS, how does it differ from Statistics. 2. What are the appropriate roles of DS and Statistics in furthering better understanding of pharmaceutical scientific and engineering studies. 3. What are the hurdles preventing the Pharmaceutical Sciences from utilizing data science tools? 4. How does one build a business case for DS approaches.
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