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Dinov article: Building consensus on data science education and training

By June 18, 2018Research

Dr. Ivo Dinov, professor of Computational Medicine and Bioinformatics, Human Behavior, and Biological Science, and associate director of MIDAS, recently published an article on the training and education curricula needs of data science.

Title: Quant data science meets dexterous artistry
Published in: International Journal of Data Science and Analytics
DOI: 10.1007/s41060-018-0138-6
Author: Ivo D Dinov
Abstract: Data science is a bridge discipline connecting fundamental science, applied disciplines, and the arts. The demand for novel data science methods is well established. However, there is much less agreement on the core aspects of representation, modeling, and analytics that involve huge and heterogeneous datasets. The scientific community needs to build consensus about data science education and training curricula, including the necessary entry matriculation prerequisites and the expected learning competency outcomes needed to tackle complex Big Data challenges. To meet the rapidly increasing demand for effective evidence-based practice and data analytic methods, research teams, funding agencies, academic institutions, politicians, and industry leaders should embrace innovation, promote high-risk projects, join forces to expand the technological capacity, and enhance the workforce skills.