In 2017, when we designed the curriculum of the Master of Data Science (MDASC) program, an interdisciplinary program jointly offered by Departments of Statistics and Actuarial Science, and Computer Science of the University of Hong Kong, we developed a two-semester data science project as a core course. Statistics in action has been one of the major core values of reforming the undergraduate curriculum. When we delivered a talk titled “Statistical Curricula Development at the University of Hong Kong” in an invited paper session on “Developing Undergraduate Curricula for Statistical Workplaces Now and Future” at the World Statistics Congress in 2013, we mentioned several distinctive aspects of the development, including the need to integrate statistical thinking and reasoning, massaging data, interdisciplinary inquiry, and research-based teaching and learning (Yu & Li, 2013). Many of these are, from the point of view of a data scientist, applications oriented and interdisciplinary in nature. Nevertheless, it is relatively easy to collect research projects within the university. However, in our situation, industrial consultation is less developed compared with that at Boston University. We concur with the authors on the importance of embedding a substantial practicum in the curriculum. (2021) detail, one of the key components of their MSSP program is a two-semester statistics practicum course in which students are involved in a number of external-partner projects and consulting projects for the university community. Below we share some of our own experiences that resonate with the theme of Kolaczyk et al. They have addressed the important issue of theory versus practice in the training of a statistician/data scientist by placing a practicum course at the center of a data science program, and shared their valuable experience in their M.S. We would like to congratulate Professors Eric Kolaczyk, Haviland Wright, and Masanao Yajima for their very interesting and impressive work (“Statistics Practicum: Placing ‘Practice’ at the Center of Data Science Education,” this issue).
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