64th ISI World Statistics Congress - Ottawa, Canada

64th ISI World Statistics Congress - Ottawa, Canada

Data science for informed citizen: Learning at the intersection of data literacy, statistics and social justice


64th ISI World Statistics Congress - Ottawa, Canada

Format: CPS Abstract

Keywords: data, science

Session: CPS 58 - Data science

Tuesday 18 July 4 p.m. - 5:25 p.m. (Canada/Eastern)


Data science as a practical science has been conceived to address tangible problems in science, technology and society. Educating students in data science goes beyond teaching about algorithms, skills of manipulating data sets, selecting and applying appropriate analyses, and creating and interpreting visual representations of data. It also involves raising a critical understanding of how data are produced and how they can be used for particular purposes, including the role of context in interpreting data. It emphasizes developing an awareness for data ethics, and considering the implications for policy and society when powerful algorithms are used. Participation in democracy, in today’s digital and datafied society requires the development of a series of transversal skills which need to be fostered in educational institutions through critically oriented pedagogies that interweave technical data skills and practices together with statistical and media literacies. These features relate data science education and data literacy closely to the recently developed field of civic statistics.
This talk will focus on questions such as
• What dimensions of learning (e.g., technical, socio-political) are most important in an account of data science for informed citizen? How can we best support learners’ growth in these dimensions?
• How can data science education empower people to be agentic in both their local and global community?
• How can learning experiences be created that make visible each of the personal, community and societal interest embedded in data.
• What are the challenges of integrating data science into the school/undergraduate statistics courses or designing a data science curriculum at the school level/undergraduate statistics?
• How do we prepare people to cope with the complexity of big data? What knowledge, skills, and dispositions are required to develop data acumen in data science?

Data Science education is still in its infancy. More broadly, in this talk we will present some studies, that other teachers and researchers may use to align their work.