A two-person team from the UNICEF’s Office of Innovation in New York recently joined DSTI in Sierra Leone to collaborate on a Machine Learning “Hackathon”
As part of efforts to develop the technology and innovation ecosystem to support development of Sierra Leone, UNICEF is collaborating with the Directorate of Science, Technology and Innovation (DSTI) in the Office of the President, on a knowledge exchange partnership, around innovative Machine Learning techniques which, it is hoped, will add value to Government’s work around data for decision making in the country.
Among others, I hope this collaboration will successfully help us clean up the data system and link it up with other learning applications.
A two-person team from the UNICEF’s Office of Innovation in New York recently joined DSTI in Sierra Leone to collaborate on a Machine Learning “Hackathon” to work on data from the education sector in support of the Government’s Free Quality School Education initiative. Officials from different Government Ministries, Departments and Agencies joined the team to enhance their knowledge of Machine Learning and advanced data analysis techniques, for use in their own areas of government.
Shane O’Connor, Technology for Development Specialist at UNICEF Sierra Leone, stated that the opportunity afforded by this collaboration is huge. “With the President’s establishment of the DSTI and with UNICEF’s collaboration, there really is great potential for a step change in how Technology and Innovation can be leveraged to deliver for Sierra Leone,” he said.
David Sengeh, the Chief Innovation Officer, acknowledged UNICEF’s enduring partnership with the Government of Sierra Leone. He stated that his work with the UNICEF team has been inspiring and an essential collaboration. “Among others, I hope this collaboration will successfully help us clean up the data system and link it up with other learning applications,” he said.
It is envisaged that this prototype of a working model will help strengthen the country’s data management structures while at the same time working to address “real world” data needs.