Journal of Data Science

When computations such as statistical simulations need to be carried out on a high performance computing (HPC) cluster, typical questions arise among researchers or practitioners. How do I interact with a HPC cluster? Do I need to type a long host name and also a password on every single login or file transfer? Why does my locally working code not run anymore on the HPC cluster? How can I install the latest versions of software on a HPC cluster to match my local setup? How can I submit a job and monitor its progress? This tutorial provides answers to such questions with experiments on an example HPC cluster.

When collaborating with students, colleagues and practitioners, one soon realizes the lack of efficiency when sending around emails with multiple attachments, especially if changes are made in several types of documents (for example, text, code, PDF) and simultaneously by several collaborators. Using a version control system (VCS) can largely improve joint workflows, from file sharing, including merging changes from different collaborators, to providing access to past versions of the shared work, while allowing each collaborator to work under her/his preferred setup (for example, text editor or file manager). There exists lots of technical or specialized information and literature about VCSes online, but, as often, this is rather overwhelming for beginners. Knowing the basics well is more important than getting lost in the vast amount of possible options VCSes offer. Also, the basics are sufficient to enjoy using VCSes and to see their value in collaborative work, additional features can still be picked up along the way once necessary. We focus on such fundamentals of the centralized VCS SVN and the distributed VCS Git. We explain in simple terms how these systems can be set up and interacted with to increase efficiency in collaborative workflows.

This study investigates perceptions and use of generative artificial intelligence (GenAI) tools among students and faculty in statistics and data science at a historically Black college or university. Survey data from 119 valid student responses and 14 faculty responses were used to examine familiarity, usage patterns, perceived benefits, awareness of limitations, and instructional support needs. Students reported substantial use of GenAI, with ChatGPT as the dominant tool, primarily for coding assistance and writing support. Although student perceptions of AI in data science workflows and careers were generally positive, confidence in interpreting AI-generated outputs was limited, and concerns about accuracy, reliability, and over-reliance were common. Faculty also viewed GenAI favorably, but self-rated proficiency and the frequency of classroom integration remained limited. Comparisons across student subgroups suggested that familiarity with GenAI and awareness of its limitations varied more by academic level than by gender. These findings highlight a gap between AI adoption and AI literacy and underscore the need for structured training, validation practices, and clearer institutional guidance for responsible AI integration in data science education.