In a recent edition of the of the Health and Data Science Review published by MIT, the elements and evolution of the interdisciplinary field of data science were explored from diverse perspectives ranging from statistics, to computer science, to cooking, thanks to editor-in-chief, Xiao-Li Meng.
For their part, Biostats Professor Xihong Lin and Xuming He, H.C. Carver Professor of Statistics at the University of Michigan, focused on the overlap between statistics and data science, proposing ten research areas that could make statistics and data science more impactful on science and society. The authors followed up with a rejoinder outlining the themes that had provoked particular discussion as well as some additional areas and strategies. These included, the integration of data science and domain knowledge, the need for fair, transparent and trustworthy data, the importance of mathematics in data science, and the role of collaboration between academia and industry. Additional themes included the need for greater communication and soft skills for the presentation of scientific findings, the creation of a data science community and associated academic infrastructure, and emphasis on data science education and training.
For more on the discussion, see the latest edition of the Harvard Data Science Review or Dr. Lin’s tweet summary.
