May 28-30, 2024

Nelson Mganga

Data Scientist



Nelson works as a data scientist at Zindi, supporting organizations in designing problem statements to real-world challenges that can readily be solved through engagement with the vast community of data scientists on the Zindi platform. Nelson holds a Bachelor’s Degree in Statistics and completed the MITx MicroMasters in Data, Economics, and Development Policy, a background that has him actively engaged in the filed of Development Economics. More recently Nelson has been leading a research project with the International Maize and Wheat Improvement Center (CIMMYT) seeking to utilize data science to empower farmers and ultimately inform agricultural policy across Africa.


‘Longa, let’s talk’: reflections on the co-design of an automated speech recognition tool for low-resource and Bantu languages

ICTforAg 2023 2023-11-09T13:00:00-06:00

This talk reports on the progress of the development and testing of an automated speech recognition tool. The project is in collaboration with Farm Radio International, an NGO broadcasting valuable knowledge to farmers across Africa., via local radio networks. Farmers are able to call in and leave comments or ask questions, however, without the capacity to transcribe and translate them, many calls go unanswered. Longa is thus a tool, built using the latest in natural language processing software, which can analyze and aggregate these calls, with the intention of better tailoring radio shows to farmers. We are currently field-testing the model and exploring the onboarding process. The speakers present lessons learned thus far for organizations wishing to explore their own audio analytics strategy.

A day in the Life of Junior Data Scientists

ICTforAg 2023 2023-11-08T10:00:01-06:00

Three junior data scientists from Tanzania, India, and U.S. will present their agricultural data science work, highlighting their innovative approaches (data they analyze, tools they use, and analysis objectives), inspirational stories (challenges they face, how they overcome them, and what motivates them), and inclusion aspects (how they ensure their data science work contribute to the mission of their organizations/programs).