{"title":"Data Science in Times of Pan(dem)ic","authors":"S. Leonelli","doi":"10.1162/99608F92.FBB1BDD6","DOIUrl":"https://doi.org/10.1162/99608F92.FBB1BDD6","url":null,"abstract":"What are the priorities for data science in tackling COVID-19 and in which ways can big data analysis inform and support responses to the outbreak? It is imperative for data scientists to spend time and resources scoping, scrutinizing and questioning the possible scenarios of use of their work – particularly given the fast-paced knowledge production required by an emergency situation such as the coronavirus pandemic. In this paper I provide a scaffold for such considerations by identifying five ways in which the data science contributions to the pandemic response are imagined and projected into the future, and reflecting on how such imaginaries inform current allocations of investment and priorities within and beyond the scientific research landscape. The first two of these imaginaries, which consist of (1) population surveillance and (2) predictive modelling, have dominated the first wave of governmental and scientific responses with potentially problematic implications for both research and society. Placing more emphasis on the latter three imaginaries, which include (3) causal explanation, (4) evaluation of logistical decisions and (5) identification of social and environmental need, I argue, would provide a more balanced, sustainable and responsible avenue towards using data science to support human co-existence with coronavirus.","PeriodicalId":194618,"journal":{"name":"Issue 3.1, Winter 2021","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134006618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Statistics Practicum: Placing 'Practice' at the Center of Data Science Education","authors":"E. Kolaczyk, Haviland Wright, M. Yajima","doi":"10.1162/99608F92.2D65FC70","DOIUrl":"https://doi.org/10.1162/99608F92.2D65FC70","url":null,"abstract":"Much of the current post-secondary training in core data science fields treats ‘practice’ as something to be relegated to capstone projects or other final preparations before students leave their programs. Here we argue for a paradigm shift, placing a so-called Practicum course at the center of a data science program, intentionally organized as a hybrid between an educational classroom and an industry-like environment. As a case study, we detail our experience of the past five years developing the Statistics Practicum in Boston University’s MS in Statistical Practice (MSSP) program. We describe the motivation, organization, and logistics of our Practicum, as well as both successes and challenges we have faced. In particular, the challenge of fairly and effectively assessing student achievement and program impact in this novel setting is discussed.","PeriodicalId":194618,"journal":{"name":"Issue 3.1, Winter 2021","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121114348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}