This article presents a novel approach to introducing principal component analysis (PCA), using summary tables and descriptive statistics. Given its applicability across a variety of academic disciplines, this topic offers abundant opportunity for class discussion and activities. However, teaching PCA in an introductory class can be challenging…

Despite the increasing use of the statistical programming language R in statistics and data analysis (SDA), its implementation in communication science education is limited. Experiences, recommendations, and a critical exchange are therefore scarce. The following contribution addresses this very gap. At the Department of Journalism and…

R is becoming the standard for teaching statistics due to its flexibility, and open-source nature, replacing software programs like Minitab and SPSS. The main driver for reform within Scottish statistical undergraduate programs is the creation of the Scottish Qualification Authority's Higher Applications of Mathematics course which has statistics…

When incorporating programming into a statistics course, there are many pedagogical considerations. In R, one consideration is the particular R syntax used. This article reports on a head-to-head comparison of a pair of introductory statistics labs, one conducted in the formula syntax, the other in tidyverse. Pre- and post-surveys show minimal…

Laboratory automation and data science are valuable new skills for all chemists, but most pedagogical activities involving automation to date have focused on upper-level coursework. Herein, we describe a combined computational and experimental lab suitable for a first-year undergraduate general chemistry course, in which these topics are…

This paper proposes a framework for integrating generative artificial intelligence (AI) tools into statistical training for Doctor of Education (EdD) students. The rigorous demands of doctoral education, coupled with the challenges of learning complex statistical software and coding language, often lead to anxiety and frustration among students,…

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…

To meet the demands of industry, undergraduate business curricula must evolve to prepare analytics-enabled professionals in fields such as finance, accounting, human resource management, and marketing. In this article, we provide a case study of developing a rigorous, integrated finance and data analytics course that was delivered using a…

The Poisson distribution describes the probability of a certain number of events occurring in an interval of time when the occurrence of the individual events is independent of one another and the events occur with a fixed mean rate. Probably the best-known example of the Poisson distribution in the physics curriculum is the temporal distribution…

As vast amounts of data have become available in business in recent years, the demand for data scientists has been rising. The author of this article provides a tutorial on how one entry-level machine learning competition from Kaggle, an online community for data scientists, can be integrated into an undergraduate econometrics course as an…

The objective of this study is to present and discuss how data visualization can be incorporated into teaching approaches by business faculty in introductory business statistics to strengthen business students' practical skills. Data visualization lessens difficulties in learning statistics by providing opportunities to illustrate analytical…

Addressing societal issues in civil and environmental engineering increasingly requires skills in data science and programming. To date, there is not much known about the extent students are learning these skills in current civil and environmental engineering curricula. We conducted a survey of accredited civil and environmental engineering…

It has become increasingly important for future business professionals to understand statistical computing methods as data science has gained widespread use in contemporary organizational decision processes in recent years. Used by scores of academics and practitioners in a variety of fields, Monte Carlo simulation is one of the most broadly…

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 developed an interactive online textbook that interleaves R programming activities with text as a way to facilitate students' understanding of statistical ideas while minimizing the cognitive and emotional burden of learning programming. In this exploratory study, we characterize the attitudes and experiences of 672 undergraduate students as…