Missing data can significantly influence results of epidemiological studies. The National Health and Nutrition Examination Survey (NHANES) is a popular epidemiological dataset. We examined recent practices related to the prevalence and the reporting of the amount of missing data, the underlying mechanisms, and the methods used for handling missing…

Biomedical data science education faces the challenge of preparing students for conducting rigorous research with increasingly complex and large datasets. At the same time, philosophers of science face the challenge of making their expertise accessible for scientists in such a way that it can improve everyday research practice. Here, we…

With increased focus on data literacy and data science education in K-12, little is known about what makes a data set preferable for use by classroom teachers. Given that educational designers often privilege authenticity, the purpose of this study is to examine how teachers use features of data sets to determine their suitability for authentic…

As the use of data in courses that incorporate statistical methods has become more prevalent, so has the need for tools for working with such data, including those for data creation and adjustment. While numerous tools exist that support faculty who teach statistical methods, many are focused on data analysis or theoretical concepts, and there…

Understanding data structures is fundamental for mastering algorithms. In order to solve problems and tasks, students must be able to choose the most appropriate data structure in which the data is stored and that helps in the process of the solution. Of course, there is no single correct solution, but in many cases, it is an important step to…

Data preparation (also called "wrangling" or "cleaning") -- the evaluation and manipulation of data prior to formal analysis -- is often dismissed as a precursor to meaningful engagement with a dataset. Here, we re-envision data preparation in light of calls to prepare students for a data-rich world. Traditionally, curricular…

Integration of computational data science (CDS) into the university curriculum offers several advantages for students, faculty and the institution. This article discusses the benefits to students of introducing CDS into the university curriculum with a focus on developing skills in cheminformatics, data analysis, structure--activity relationships,…