Protected: Data Science Artificial Intelligence and You in Healthcare (DSAIY)
Program Leaders, PhD, Senior Research Scientist, 成人快播
, PhD, Director of Client Services & Science Specialist, East Bay Educational Collaborative
, Principal Research Scientist, Massachusetts Institute of Technology
, CEO of Scoutlier by Aecern
Project SummaryData Science, AI and You (DSAIY) in healthcare is a grade 9-12 semester-long high school curriculum and workforce experience (datathon) piloted with 12 Rhode Island teachers and schools via an award (#2148451) from the U.S. National Science Foundation (NSF) Innovative Experiences for Students and Teachers (ITEST).
DSAIY is engaging, fun, timely, and attainable for all students, including those with no coding or statistics background. The curriculum is scaffolded, differentiated, and easily edited to teach students. It introduces students to bias in machine learning, how this can impact healthcare outcomes, critical data science, machine learning tools, and skills to succeed in our data and AI-oriented world.
GoalsWithin an authentic, culturally responsive course and workforce healthcare datathon, introduce high school students to
- Data and machine learning bias that can impact healthcare outcomes.
- Tools data scientists use to train machines.
- Careers and mentors from high-paying fields in data science, machine learning, and healthcare.
Through a network of medical doctors, data scientists, teachers, career mentors, college students, and community members
- Inspire students, particularly racial minorities and girls, to self-advocate, create support networks, and pursue related careers.
- Contribute towards creating future leaders who are technologically and data literate.
, PhD, Senior Research Scientist, 成人快播
, PhD, Director of Client Services & Science Specialist, East Bay Educational Collaborative
, Principal Research Scientist, Massachusetts Institute of Technology
, CEO of Scoutlier by Aecern
Data Science, AI and You (DSAIY) in healthcare is a grade 9-12 semester-long high school curriculum and workforce experience (datathon) piloted with 12 Rhode Island teachers and schools via an award (#2148451) from the U.S. National Science Foundation (NSF) Innovative Experiences for Students and Teachers (ITEST).
DSAIY is engaging, fun, timely, and attainable for all students, including those with no coding or statistics background. The curriculum is scaffolded, differentiated, and easily edited to teach students. It introduces students to bias in machine learning, how this can impact healthcare outcomes, critical data science, machine learning tools, and skills to succeed in our data and AI-oriented world.
GoalsWithin an authentic, culturally responsive course and workforce healthcare datathon, introduce high school students to
- Data and machine learning bias that can impact healthcare outcomes.
- Tools data scientists use to train machines.
- Careers and mentors from high-paying fields in data science, machine learning, and healthcare.
Through a network of medical doctors, data scientists, teachers, career mentors, college students, and community members
- Inspire students, particularly racial minorities and girls, to self-advocate, create support networks, and pursue related careers.
- Contribute towards creating future leaders who are technologically and data literate.
Within an authentic, culturally responsive course and workforce healthcare datathon, introduce high school students to
- Data and machine learning bias that can impact healthcare outcomes.
- Tools data scientists use to train machines.
- Careers and mentors from high-paying fields in data science, machine learning, and healthcare.
Through a network of medical doctors, data scientists, teachers, career mentors, college students, and community members
- Inspire students, particularly racial minorities and girls, to self-advocate, create support networks, and pursue related careers.
- Contribute towards creating future leaders who are technologically and data literate.
Funder:
Award Number:
#2148451
East Bay Educational Collaborative











