Classification, a fundamental data analytics task, has widespread applications across various academic disciplines, such as marketing, finance, sociology, psychology, education, and public health. Its versatility enables researchers to explore diverse research questions and extract valuable insights from data. Therefore, it is crucial to extend…

Statistical competitions like ASA DataFest and the Women in Data Science (WiDS) Datathon give students valuable experience working with real, challenging data. By participating, students practice important statistics and data science skills including data wrangling, visualization, modeling, communication, and teamwork. However, while advanced…

As data have become more prevalent in academia, industry, and daily life, it is imperative that undergraduate students are equipped with the skills needed to analyze data in the modern environment. In recent years there has been a lot of work innovating introductory statistics courses and developing introductory data science courses; however,…

Data analytics and programming skills are increasingly important in the humanities, especially in disciplines like linguistics due to the rapid growth of natural language processing (NLP) technologies. However, attitudes and perceptions of students as novice learners, and the attendant pedagogical implications, remain underexplored. This article…

Data Analytics has grown dramatically in importance and in the level of business deployments in recent years. It is used across most functional areas and applications, some of the latter including market campaigns, detecting fraud, determining credit, identifying assembly line defects, health services and many others. Indeed, the realm of…

Teaching practical skills is of particular interest in the study of human medicine. With regard to medical statistics this means the use of statistical software, which may be effectively taught by a flipped classroom approach. As a pilot study, we designed and implemented an elective course on medical statistics that focused on hands-on data…

In this article we present and discuss the importance and relevance of the inclusion of spatial data analysis as part of the undergraduate statistics curriculum. Given its usefulness and applicability across a range of disciplines, spatial data are a topic that should receive more emphasis within undergraduate statistics curricula. There exist…

Data science is collaborative and its students should learn teamwork and collaboration. Yet it can be a challenge to fit the teaching of such skills into the data science curriculum. Team-Based Learning (TBL) is a pedagogical strategy that can help educators teach data science better by flipping the classroom to employ small-group collaborative…

We provide a computational exercise suitable for early introduction in an undergraduate statistics or data science course that allows students to "play the whole game" of data science: performing both data collection and data analysis. While many teaching resources exist for data analysis, such resources are not as abundant for data…

Engaging and motivating students in undergraduate statistics courses can be enhanced by using topical peer-reviewed publications for analyses as part of course assignments. Given the popularity of on-campus therapy dog stress-reduction programs, this topic fosters buy-in from students whilst providing information regarding the importance of mental…

While correlated data methods (like random effect models and generalized estimating equations) are commonly applied in practice, students may struggle with understanding the reasons that standard regression techniques fail if applied to correlated outcomes. To this end, this article presents an in-class activity using results from Monte Carlo…

Over the last 20 years, statistics preparation has become vital for a broad range of scientific fields, and statistics coursework has been readily incorporated into undergraduate and graduate programs. However, a gap remains between the computational skills taught in statistics service courses and those required for the use of statistics in…

We describe a unique environment in which undergraduate students from various STEM and social science disciplines are trained in data provenance and reproducible methods, and then apply that knowledge to real, conditionally accepted manuscripts and associated replication packages. We describe in detail the recruitment, training, and regular…

Teaching students statistical programming languages while simultaneously teaching them how to debug erroneous code is challenging. The traditional programming course focuses on error-free learning in class while students' experiences outside of class typically involve error-full learning. While error-free teaching consists of focused lectures…

We advocate for greater emphasis in training students about data management, within the context of supporting experience in reproducible workflows. We introduce the "L"ancaster "U"niversity "ST"atistics "RE"sources (LUSTRE) package, used to manage student research project data in psychology and build…