Data science programming presents many challenges for programmers entering the field. Roughly, data science programming can be broken up into several activities: data wrangling, analysis, modeling, or visualization. Data wrangling is an important first step that involves cleaning and shaping tabular data--or dataframes--into a form amenable for…

The intersection of Data Science (DS) and Library and Information Science (LIS) is rapidly developing, with a notable need for ongoing transdisciplinary training between practitioners in these two fields. The LIS Education and Data Science Integrated Network Group (LEADING) fellowship program and its ancillary community of practice (CoP) showcase…

The discipline of data science has gained substantial attention recently. This is mainly attributed to the technological advancement that led to an exponential increase in computing power and has made the generation and recording of enormous amounts of data possible on an everyday basis. It has become crucial for industries to wrangle, curate, and…

Research Reproducibility: Educating for Reproducibility, Pathways to Research Integrity was an interdisciplinary, conference hosted virtually by the University of Florida in December 2020. This event brought together educators, researchers, students, policy makers, and industry representatives from across the globe to explore best practices,…