This work reports on the implementation of a self-contained data-literacy exercise designed for use in undergraduate classes to help students practice data literacy skills such as interpreting and evaluating evidence and assessing arguments based on data. The exercises use already developed data-visualizations to test and develop students' ability…

Despite vital professional development support through the National Art Education Association's Data Visualization Working Group and other scholars' engagement with the topic in the literature, pedagogical literature that connects contemporary data visualization methods to art teaching practices is limited. While acknowledging that many of us have…

This short paper presents an educational approach to teaching three popular methods for encoding DNA sequences: one-hot encoding, binary encoding, and integer encoding. Aimed at bioinformatics and computational biology students, our learning intervention focuses on developing practical skills in implementing these essential techniques for…

The design and analysis of experiments (DOE) has historically been an important part of an education in statistics, and with the increasing complexity of modern production processes and the advent of large-scale online experiments, it continues to be highly relevant. In this article, we provide an extensive review of the literature on DOE…

This article aims to present a first approximation of the conceptual field of measures of central tendency (MCT), grounded in the theory of conceptual fields. We propose six situations according to type of variable, data presentation (raw or grouped) and amount of data. We revisit specific situations for the mean and exemplify several…

Textbook data is essential for teaching statistics and data science methods because it is clean, allowing the instructor to focus on methodology. Ideally textbook datasets are refreshed regularly, especially when they are subsets taken from an ongoing data collection. It is also important to use contemporary data for teaching, to imbue the sense…

In science classrooms, students usually see and work with data that's intended to tell them right away about the natural world. Students then often treat the data we provide to them as factual, rather than as a source of evidence (Duschl 2008; Sandoval and Millwood 2005; Berland and Reiser 2009; McNeill and Berland 2017; Hancock, Kaput, and…

Businesses and other organizations across the globe are becoming more and more data-driven, using a combination of descriptive, diagnostic, predictive and prescriptive analytics to gain a strategic advantage through understanding the past, what we hope to happen in the future, and the ability to accurately predict future outcomes. These forms of…

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…

Abilities to interpret, analyze, and report data are in-demand job skills that many colleges and universities struggle to incorporate into the curriculum. The client-based project outlined in this study examines the experiential learning pedagogy used to teach students media analytics. This semester-long project encourages students to complete a…

The COVID-19 pandemic uniquely affected nearly all the subject matter in a typical principles of macroeconomics class. Fluctuations in the basic macroeconomic data in the COVID era were staggering and offer new teaching opportunities. In addition, because the recession was primarily driven by supply side shocks, the entire episode offers a unique…

In 2010, Nolan and Temple Lang proposed "integration of computing concepts into statistics curricula at all levels." The unprecedented growth in data and emphasis on data science has provided an impetus to finally realizing full implementations of this in new statistics and data science programs and courses. We discuss a proposal for the…

This article describes the motivation and design for introductory coursework in computation aimed at midcareer professionals who desire to work in data science and analytics but who have little or no background in programming. In particular, we describe how we use modern interactive computing platforms to accelerate the learning of our students…

Principal Component Analysis (PCA) is a powerful statistical technique for reducing the complexity of data and making patterns and relationships within the data more easily understandable. By using PCA, students can learn to identify the most important features of a data set, visualize relationships between variables, and make informed decisions…

Recent and rapid technological advances have given rise to an explosive growth of data, along with low-cost solutions for accessing, collecting, managing, and analyzing data. Despite the advances in technology and the availability of data, social work organizations routinely encounter data-related problems that have an impact on their…