You're invited to our first ever Project Accelerator Night!
Come meet three great organizations and help them take the first steps towards successful data science projects, through conversation and collaborative brainstorming.
Cool Culture (http://www.coolculture.org/): Founded in 1999, Cool Culture’s mission is to drive new family visitation to New York City’s museums, zoos, and botanic gardens in support of young children’s emerging literacy and social resilience. With their lean staff of 11, they provide services to more than 50,000 families, 400 preschools, and 90 cultural institutions. Cool Culture is launching an app to help families take advantage of more NYC museums and cultural institutions. Their question for you: How can we use the data we collect to better understand the families we serve?
RentSpecs rates landlords based on the number of legal infractions their buildings have by combining housing violations, complaints and litigations in New York City into a simple letter grade to empower renters. Their goal is to enable people to make assertive decisions when finding a home, pressure demonstrated slumlords to shape up, and reward responsible landlords with free marketing – all while increasing access to data and improving the function of government.
RentSpecs has been working to perfect the algorithm they use to rate buildings. They'd like your advice. How can they get data from gov't websites more efficiently? Is their algorithm using their data to create the most accurate letter rating?
Kinvolved (https://kinvolved.com/) connects families, schools, and communities through shared, real-time student attendance data to improve student achievement. Kinvolved's portfolio of technology tools allow stakeholders to share student data that inform appropriate rewards and interventions.
Kinvolved would like to predict in advance whether or not a student needs some intervention to ensure they are more likely to get to school or after school activities on time. Using their data, they want to develop a predictive algorithm to engage family, teachers, and students in advance. Can you help them figure out how to get started?
Whether you're an experienced data scientist or brand new to the DataKind community, your voice is valued as we talk through these problems together and help each of our partner organizations take a step forward to answer their data questions. All skills and backgrounds welcome!
Thanks to our sponsors ThoughtWorks and Knight Foundation for their generous support.