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…

In the era of artificial intelligence (AI), big data (BD), and digital transformation (DT), analytics students should gain the ability to solve business problems by integrating various methods. This teaching brief illustrates how two such methods--Bayesian analysis and Markov chains--can be combined to enhance student learning using the Analytics…

The general growth in public affairs programs offering hybrid and online courses to reach a wide variety of students, along with the necessity of doing so during a global health pandemic, calls for an investigation of best practices in teaching public affairs statistics and research-oriented courses. These courses often require the use of a…

This case uses data analytic techniques to expose students to the context of the initial coin offering marketplace. The exercise is well-suited as a group activity in an undergraduate or graduate business analytics course in which students have been taught analytic techniques such as word cloud, descriptive statistics, basic visualizations, and…

Traditionally, statistical computing courses have taught the syntax of a particular programming language or specific statistical computation methods. Since Nolan and Temple Lang's seminal paper, we have seen a greater emphasis on data wrangling, reproducible research, and visualization. This shift better prepares students for careers working with…

A version control system records changes to a file or set of files over time so that changes can be tracked and specific versions of a file can be recalled later. As such, it is an essential element of a reproducible workflow that deserves due consideration among the learning objectives of statistics courses. This article describes experiences and…

Student evaluation is a key consideration for educational program administrators because program success depends on students' ability to demonstrate successful development of core competencies. Student evaluations must therefore be aligned with learning objectives and overall program goals. Graduate level educational programs typically incorporate…

Data Mining (DM) is one of the most offered courses in data analytics education. However, the design and delivery of DM courses present a number of challenges and issues that stem from the DM's interdisciplinary nature and the industry expectations to generate a broader range of skills from the analytics programs. In this research, we identified…