Teaching reproducibility and responsible workflows - Nick Horton (USA)
Abstract
Modern statistics and data science utilizes an iterative data analysis process to solve problems and extract meaning from data in a reproducible manner.
The National Academies of Science, Engineering, and Medicine’s (NASEM) 2018 „Data Science for Undergraduates“ consensus study identified the importance of workflow and reproducibility as a component of data acumen. But data science is increasingly important in primary and secondary education. How can we help students scaffold their analyses and foster responsible workflows as they begin to develop data fluency? In this talk, I will explore data tools and approaches that are intended to help students develop these important capacities.
Bio Nick Horton
Dr. Horton is Beitzel Professor of Technology and Society (Statistics and Data Science) at Amherst College. He is passionate about improving quantitative and computational literacy for students with a variety of backgrounds and has worked to deepen engagement and mastery of higher-level concepts and data acumen.
He is the editor of the Journal of Statistics and Data Science Education and is co-chair of the NASEM Committee on Applied and Theoretical Statistics. Nick served as the co-chair of the Planning Committee for the NASEM Workshop on K-12 Data Science and as chair of the Committee of Presidents of Statistical Societies. Dr. Horton has published more than 190 papers in statistics and biomedical research and four books on statistical computing and data science. He has been the recipient of a number of teaching awards and the American Statistical Association Founders Award. Dr. Horton is a fellow of the American Statistical Association and the American Association for the Advancement of Science.
