Florence Nightingale Day Inspires Students to Pursue Statistics and Data Science — A Panel Discussion
Abstract Number:
1655
Submission Type:
Topic-Contributed Panel Session
Participants:
Shili Lin (1), Donald Estep (3), Jessica Kohlschmidt (2), Sidketa Fofana (2), David Kline (4), Shili Lin (1), Rohan Alexander (2)
Institutions:
(1) Ohio State University, N/A, (2) N/A, N/A, (3) Canadian Statistical Sciences Institute, N/A, (4) Wake Forest University School of Medicine, N/A
Chair:
Panelist(s):
Session Organizer:
Session Description:
Florence Nightingale was a pioneer in data visualization and the founder of modern nursing. She used data in a novel and effective way to provide better care for wounded soldiers in the Crimean War and for public health improvements more broadly. Her pioneering work included the creation of the pie chart, which is universally used in data visualization to this day. To honor Nightingale's legacy, the Florence Nightingale Day (FN Day) was jointly launched by ASA and CWS (Caucus for Women in Statistics) in 2018 to engage middle and high school students, promote future career opportunities in statistics and data science, and celebrate the contributions of women to these fields. The vision is to inspire a diverse group of young people to become future leaders in data-related disciplines. Pre-college students (ages 13 and above) from around the world are encouraged to attend this one-day free event. Components of the day include trivia games, panels of professional and student speakers, hands-on activities, and networking. In this session, the panelists will discuss their experiences hosting their FN Day in the United States and Canada. We will feature panelists whose sites have successfully recruited women and students from disadvantaged groups, and those from institutions that enroll over 85% of Hispanic students. Our panelists will also share information on how to host your own FN Day.
Sponsors:
Caucus for Women in Statistics 1
Committee on Outreach Education 3
SSC (Statistical Society of Canada) 2
Theme:
Statistics and Data Science: Informing Policy and Countering Misinformation
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Applied
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Estimated Audience Size
Small (<80)
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