Teaching Statistics, the journal of the Teaching Statistics Trust, has published a new special issue. The special issue “Teaching Data Science and Statistics: foundation and introductory” is edited by the journal’s editor-in-chief Helen MacGillivray along with Jim Ridgway and Rob Gould. The special issue reflects upon the change of the journal’s subtitle to include the teaching of data science as well as statistics. A portion of the preface by MacGillivray, Ridgway, and Gould explaining the goals of the special issue is included below:

Over recent years, some submissions to the journal have started reflecting, and hopefully influencing, the various impacts on teaching of developments in data science, statistics, and their confluence. These are seen not only in the increasing inclusion of technology in teaching statistics but also in the data and contexts considered and the broadening of statistical issues, explorations, presentations, and discussions at introductory levels, whether school, undergraduate, or postgraduate/workplace in other disciplines. This special issue is intended to provide impetus and inspiration to all readers and authors in furthering this progress and to celebrate the new subtitle of the journal, in the increasing awareness of what data science is and how statistics and data science work together in tackling real and complex datasets and problems involving complex data. The editorial continues some aspects of this discussion.

The overall aim of the special issue is to be useful to all who are actively involved in data education to school age students or to tertiary students at the introductory level, particularly across disciplines—so consistent with the aims and scope of the journal but with the emphasis on data science and statistics in the data science era. The special issue has been more than a year in the making, with both invited and contributed papers, and all reviewed by reviewers of international standing and experience. As always, the emphasis is on scholarly writing and the good practice of teaching, and many papers refer to, and build on, previous excellent work and literature and include valuable bibliographies.

The scope of data education is indeed broad, so it is not surprising that the special issue spans a diverse mix of topics. There are papers demonstrating data sourcing, wrangling, exploration, presentation, and interpretation in real case studies and complex real contexts in industry, COVID, contemporary social issues, and contexts of direct interest to students with issues chosen and explored by them. There are papers discussing technology tools and their teaching for data science and statistics, including R in general, R Markdown, CODAP, Python, APIs, and others. There are current and updated considerations of the perennial and ongoing challenges of language and technology in teaching. There are papers discussing and providing ways forward in providing sound foundations across all educational levels from primary to the workplace, in data science and statistics for current and future learning. There are classroom-ready learning resources and assistance, with a number of papers providing additional materials for teaching, resulting in over 10 supplementary documents which will be available online only. And there are excellent discussions of what is needed in data science and statistics teaching and learning. All papers include at least some aspects of looking back—including some with analysis of past and current failings—and looking forward, with all looking to broaden the pedagogies and practices of the learning and teaching of the sciences of data and statistics, providing pathways and opening new vistas.

Read the entire special issue here.