Journal of Data Science

Over the past three decades, the discipline of statistics has undergone profound transformation, driven by the rapid emergence of data science and artificial intelligence. These developments have reshaped methodological paradigms and introduced new challenges and opportunities for statistical education, particularly in China. In this context, Professor Xizhi Wu from the School of Statistics at Renmin University of China has remained closely engaged with the evolving landscape, demonstrating keen insight and a forward-looking perspective. Through sustained contributions to teaching, research, and educational reform, Professor Wu has deeply influenced generations of students and educators, playing a pivotal role in the advancement of statistical education. To document and reflect on this legacy, the Capital of Statistics conducted an in-depth interview with Professor Wu, focusing on his academic trajectory, professional contributions, and perspectives on the future of the discipline. The conversation also recounts meaningful interactions with his students, offering a multidimensional portrait of a life devoted to statistics.

A challenge that data scientists face is building an analytic product that is useful and trustworthy for a given audience. Previously, a set of principles for describing data analyses were defined that can be used to create a data analysis and to characterize the variation between analyses. Here, we introduce a concept called the alignment of a data analysis, which is between the data analyst and an audience. We define an aligned data analysis as the matching of principles between the analyst and the audience for whom the analysis is developed. In this paper, we propose a model for evaluating the alignment of a data analysis and describe some of its properties. We argue that more generally, this framework provides a language for characterizing alignment and can be used as a guide for practicing data scientists to building better data products.

Dr. David S. Salsburg’s career has been an exceptional one. He was the first statistician to work in Pfizer, Inc., and later became the first statistician from the pharmaceutical industry to be elected as an ASA fellow. He played a vital role as a statistician in Pfizer, Inc. at a time when the drug approval process was developed. For his contributions, Dr. Salsburg was awarded the Career Achievement Award of the Biostatistics Section of the Pharmaceutical Research and Manufacturers of America in 1994, for “significant contributions to the advancement of biostatistics in the pharmaceutical industry”. Dr. Salsburg also managed to achieve something rare among scientists, which is to popularize his field of research and make it accessible and enjoyable to laypeople. Dr. Salsburg is possibly best known for his book “The Lady Tasting Tea – How Statistics Revolutionized the 20th Century Science”, in which he combines simple and engaging explanations of statistical methods, and why they are needed, along with personal stories told with a great deal of generosity, fondness, and humor about the people who developed them. Dr. Salsburg’s admiration for the those statisticians shines through. In this interview, Dr. Salsburg shares his own stories and perspectives, from his childhood, through his service in the Navy and his long and productive career in Pfizer, Inc. to his equally productive retirement, in which he authored “The Lady Tasting Tea” and other books.

The last decade has seen a vast increase of the abundance of data, fuelling the need for data analytic tools that can keep up with the data size and complexity. This has changed the way we analyze data: moving from away from single data analysts working on their individual computers, to large clusters and distributed systems leveraged by dozens of data scientists. Technological advances have been addressing the scalability aspects, however, the resulting complexity necessitates that more people are involved in a data analysis than before. Collaboration and leveraging of other’s work becomes crucial in the modern, interconnected world of data science. In this article we propose and describe an open-source, web-based, collaborative visualization and data analysis platform RCloud. It de-couples the user from the location of the data analysis while preserving security, interactivity and visualization capabilities. Its collaborative features enable data scientists to explore, work together and share analyses in a seamless fashion. We describe the concepts and design decisions that enabled it to support large data science teams in the industry and academia.

In 2022 the American Statistical Association established the Riffenburgh Award, which recognizes exceptional innovation in extending statistical methods across diverse fields. Simultaneously, the Department of Statistics at the University of Connecticut proudly commemorated six decades of excellence, having evolved into a preeminent hub for academic, industrial, and governmental statistical grooming. To honor this legacy, a captivating virtual dialogue was conducted with the department’s visionary founder, Dr. Robert H. Riffenburgh, delving into his extraordinary career trajectory, profound insights into the statistical vocation, and heartfelt accounts from the faculty and students he personally nurtured. This multifaceted narrative documents the conversation with more detailed background information on each topic covered by the interview than what is presented in the video recording on YouTube.

Coronavirus and the COVID-19 pandemic have substantially altered the ways in which people learn, interact, and discover information. In the absence of everyday in-person interaction, how do people self-educate while living in isolation during such times? More specifically, do communities emerge in Google search trends related to coronavirus? Using a suite of network and community detection algorithms, we scrape and mine all Google search trends in America related to an initial search for “coronavirus,” starting with the first Google search on the term (January 16, 2020) to recently (August 11, 2020). Results indicate a near-constant shift in the structure of how people educate themselves on coronavirus. Queries in the earliest days focusing on “Wuhan” and “China”, then shift to “stimulus checks” at the height of the virus in the U.S., and finally shift to queries related to local surges of new cases in later days. A few communities emerge surrounding terms more overtly related to coronavirus (e.g., “cases”, “symptoms”, etc.). Yet, given the shift in related Google queries and the broader information environment, clear community structure for the full search space does not emerge.