Latest articles of Journal of Data Science

Privacy-preserving machine learning methods seek to train useful models that do not disclose information about the data on which they were trained. Such methods are vital when organizations train neural networks on sensitive individual-level data and seek to release the models publicly. Their goal poses a trade-off between predictive performance (utility) and privacy protection....

Survey researchers are increasingly adopting hybrid sampling designs to address the limitations of traditional probability sampling, especially when studying rare or hard-to-reach populations. Challenges such as high screening costs, low statistical efficiency, and operational constraints make purely probability-based approaches impractical in many contexts. This article uses pu...

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 instructio...