The last few years have witnessed an explosion in the quantity and variety of data available in Africa, produced either as a by-product of digital services, from sensors or measuring devices, satellites, and from many other sources. A number of practical fields have been transformed by the ability to collect large volumes of data: for example, bioinformatics with the development of high-throughput sequencing technology capable of measuring gene expression in cells, or agriculture with the widespread availability of high-quality remote sensing data. For other data sources – such as mobile phone usage records from telecoms operators, which can be used to measure population movement and economic activity – we are just beginning to understand the practical possibilities.

Data science seeks to exploit advances in machine learning and statistics to make sense of the growing amounts of data available from various sources. In Africa, a number of problems in areas such as healthcare, agriculture, disaster response, and wildlife conservation would benefit greatly if domain experts were exposed to data science techniques. These skills would allow practitioners to extract useful information from these abundant sources of raw data

DSA 2019 Addis tentative speakers and instructors include:

• Neil Lawrence, Amazon UK and University of Sheffield
• John Quinn, Google AI and Makerere University
• Moustapha Cisse, Google AI
• Ernest Mwebaze, Google AI, Ghana, and Makerere University
• Dina Machuve, Nelson Mandela African Institute of Science and Technology
• Billy Okal, Voyage
• Elaine Nsoesie, Boston University
• Martin Mubangizi, Pulse Lab Kampala
• Charles I. Saidu, AUST/Baze University, Abuja
• Ayorkor Korsah, Ashesi University, Ghana
• Yannis Kalantidis, Facebook AI
• Ricardo Andrade, UCSF Institute for Global Health Sciences
• Damon Civin, Facebook
• Karl Fezer, arm