Data Science Africa (DSA) is proud to partner in delivering the Google DeepMind AI Research Foundations curriculum as part of our strategic commitment to strengthening AI capacity across the continent.

The Centre for Data Science and Artificial Intelligence (DSAIL), a Data Science Africa (DSA) affiliated centre, had the honour of hosting Prof. Neil Lawrence, Radzim Sendyka, and Frederick Lawrence from the University of Cambridge for the Machine Learning Foundations Course (MLFC) held from 1st to 24th September 2025 at Dedan Kimathi University, Nyeri, Kenya.

The Enterprise Medical Imaging in Zambia project, through funding provided by DSA's 2022 AAIR 2022 awards, have been developing a semi-automated medical image interpretation service using artificial intelligence (AI). As part of this project, they secured third place in the prestigious 2024 ZICTA Innovation Programme.

Why and How Satellite based Yield Prediction ?

Smallholder farming is a predominant global agricultural system. In the developing world, it takes the lion share, for instance, in Ethiopia, it produced about 95% of main crops and cash crops. Despite its dominance, this farming practice, largely, is characterized by low crop productivity and absence of, and if there is any, the use of old-age unproductive technologies. Consequently, millions of societies around the globe are threatened by food insecurity. Countries like Ethiopia, for instance, imported 30% - 50% of their wheat demand annually.

Data Science Africa (DSA) has built a strong foundation of data literacy through its various programs and training events and continues to nurture a community of African data scientists. At a recent event in Kigali, the community sought to take the impact of DSA to another level by exploring the potential of research and projects to support effective data-driven policy-making in Africa.

In today’s fast-paced world, the promise of Artificial Intelligence (AI) and Machine Learning (ML) is increasingly becoming a part of our daily lives. However, most of these advancements require powerful computing systems that consume substantial power and resources. What if we could harness the power of ML on small, resource-constrained devices? Enter TinyML—a game-changing field of machine learning that brings the transformative power of AI to the most modest of devices.

The growth in the use of smartphones in Uganda has facilitated the increase in Internet usage and internet subscribers in the country. The Internet has provided a fertile ground to generate huge amounts of information on various web and mobile based social media platforms. Social media platforms have overtaken traditional media in facilitating social interactions on real time events. The ever increasing number of social media users has resulted in an increase in social media content. This content has proven to be helpful for decision making. However, since there are no restrictions and limitations on the content posted on social media platforms, it has turned out that some of the posted content is invalid, inaccurate, potentially and intentionally harmful. This poses a need to develop solutions that can be used to detect false information and misinformation in social media text. Several studies have sought and continue to detect negative and inaccurate content shared on social media platforms. However, most of this research has focused on the English language and thus these applications can not be used in contexts that include low resourced languages like Luganda, a major local language in Uganda that is also commonly used on social media platforms.

Covid-19 intensified the urgent need for timely data and information for decision making, with data playing an important role in policies relating to disease surveillance, health and relief resources allocation and prioritization, cases and mortality prediction, amongst other areas. Drawing from real-world examples of the use of data science in the Covid-19 response, in this article, I review the strategies that made data use for Covid-19 a lot more possible than we have seen before.

10 Questions that need answering to develop a data-sharing framework for Africa.

Data science communities across Africa have created - and are still developing - innovative uses of data and statistics to support COVID-19 response teams in mitigating the COVID-19 pandemic. These solutions aim to: