Abstract: 

As data-driven decision-making has proliferated across sectors, nonprofits have lagged behind industry and academia due to funding and resource constraints. Nonprofits aiming to turn data into tangible, meaningful insights are faced with several challenges, including limited bandwidth and technical expertise to tackle data collection, management, and analysis; and limited data availability given the scale of their programming. Given these challenges, nonprofits’ efforts to adopt new technologies and engage in data collection and analysis has only recently taken shape.

Over the last 5 years, Data Clinic, the data and tech for good philanthropic arm of investment manager Two Sigma, has utilized employee volunteer teams to partner with nonprofits and develop pro bono solutions that enable these organizations to use data more effectively. Over the course of working on these projects, we noticed that some hurdles are common to multiple organizations and found across different sectors. While volunteer efforts can help organizations on an individual basis, we believe that open source tooling that addresses these specific challenges is key to scaling capacity and moving the entire sector forward.

In this vein, some of Data Clinic’s recent efforts have been around developing open source tools that aid nonprofits across various stages of the data pipeline. In this talk, we will highlight two such open source applications, Smooshr and NewerHoods. Aided by machine learning techniques, Smooshr is designed to help consolidate and redefine categories within data sets through an easy-to-use interactive web application. NewerHoods, on the other hand, is a web application that allows users to visualize and interact with spatial data using a geo-clustering technique to help observe underlying spatial patterns.

Bio: 

Kaushik Mohan is a data scientist at Data Clinic, where he develops data-driven applications and brings statistical analysis to help nonprofits answer research questions.

He has been working on solving social challenges using data over the last four years through stints at the Data Science for Social Good Fellowship and as the lead data scientist at m.Paani, a social start-up in India. Having worked on projects with governments to small businesses, he has experienced the impact data science can make at every level of society.