“In Sudan I want people and the government to focus their attention on data science. We have data that can be digitised and used for predictions and to solve problems. Companies should put their community service money into data science education, in order to help prepare countries like Sudan for a technological future.”

Reem Elmahdi is Zindi’s country ambassador for Sudan, who champions data science in her country through teaching, networking and event planning.

Her journey so far

She began her career in the field by studying…

A collaboration between Zindi, Amazon Web Services and the South African National Space Agency has yielded a machine learning model that can identify informal settlements from satellite imagery. This tool will help the South African government with planning, providing essential services, and preventing crime in underserved communities across the country.

The image on the left shows the type of satellite data used in the competition; the image on the right shows the models’ pediction of informal settlements (blue). Image credit: Zindi.

“The idea was to get data scientists to work on a potential solution that SANSA could use to optimise our mapping processes,” says Managing Director of Earth Observations at SANSA, Andiswa Mlisa. This model will assist SANSA in mapping of informal settlements, a task that SANSA currently undertakes manually.

Employing the experts


Rose Delilah Gesicho is country ambassador for Zindi in Kenya, and she says she draws inspiration from a number of successful female data scientists in her network.

“Beyond the Zindi community, I look up to and follow notable women I have met through various volunteer and community roles, such as being a Program Coordinator for Nairobi Women in Machine Learning and Data Science (WiMLDS) and Learning Programme Manager…

Aspiring data science students work on UmojaHack Africa 2021 problems at their universities across Africa.

UmojaHack Africa 2021 was an unprecedented success, bringing more than 1000 students from 126 universities across Africa to compete on Zindi in a virtual machine learning hackathon on the weekend of 27–28 March. More than $10 000 USD in prizes were awarded to data science students from 9 African countries, and more than 8500 submissions were made to solve three real-world machine learning challenges on Zindi.

Students from 21 African countries joined the event, representing Algeria, Benin, Cameroon, Côte d’Ivoire, Egypt, Ethiopia, Ghana, Guinea, Kenya, Malawi, Morocco, Nigeria, Rwanda, Senegal, South Africa, Sudan, Tanzania, Tunisia, Uganda, Zambia and Zimbabwe.


Davis David represents Zindi at a dLab event.
Davis David represents Zindi at a dLab event.
Davis David represents Zindi at a dLab event.

Davis David is one of Zindi’s first ambassadors, representing Zindi in Tanzania since June 2019. He is the CEO of ParrotAI, helps run the Deep Learning IndabaX event in Tanzania, is a member of AI Exponential Thinker, and even manages to find time to write articles for Analytics Vidya and the Zindi blog.

“The best part of being a Zindi Ambassador is the spirit of teamwork from Zindi management and our ambassadors across Africa,” he says.

“Working with Zindi has motivated me to be more active and work harder to make sure my fellow data science and machine learning practitioners…

Mohamed Salem Jedidi is one of Zindi’s first and most accomplished users, and part of our first group of ambassadors. He consistently performs well in Zindi challenges and has won several, including the Traffic Jam challenge, Zindi’s largest prized competition to date. He is currently ranked #2 on Zindi’s leaderboard.

“Data science competitions are like a race, and you should be proud of…

“Zindi and Local Ocean Conservation (LOC) share the belief that we live in an interconnected world filled with talent; in our case the talent and technical expertise we needed was not in house. A Zindi campaign enables us to get solutions we wouldn’t otherwise have been able to get,” says Justin Beswick, CEO of Kenyan conservation organisation Local Ocean Conservation. “It’s really valuable to step out of the conservation space and tap into other industries, sectors and skills, such as Zindi’s data science community.”

LOC is a small organisation based in Watamu, Kenya that has been protecting and rehabilitating turtles…

“It is really gratifying for us at SAEON to be able to pull this kind of thing together using government-based data sources; it shows us what’s possible. We spend so much time and money working on climate change with government and non-government partners, but no one has done anything like this before.”

Dr Amelia Hilgart is talking about the recently-published African air quality dataset developed in partnership with Zindi, created using a model developed by Zindi users as part of a 60-hour hackathon earlier this year. …

“We asked Zindi to give us a way to predict when a customer would ‘churn’ or leave as a paying customer,” says Zimnat’s Chief Digital Officer, Oswin Zulu. “We built the winning models into a dashboard with a customer profile and the likelihood of them leaving the business, and that dashboard is now given to our customer care agents.”

The Zimnat Insurance Assurance Challenge on Zindi attracted just under 300 Zindi users from all over the world, and in 60 hours in May produced winning solutions from users in Nigeria, Tunisia and India. …

“Winning the Mtoto News challenge was a great motivation. It boosted my confidence and encouraged me to work harder. I went back to old courses, paying attention to every part, especially the mathematics.”

Lawrence Moruye’s data science journey is, in a lot of ways, the story of Zindi. He was in second year when he realised that data science was a promising career for someone like him, looking to combine a passion for mathematics and computer science. Just a few months later, Alfred Ongere of AI Kenya invited him to the Nairobi hackathon that launched the Zindi platform.

First steps



Zindi is a competition platform hosting a community of data scientists dedicated to solving Africa’s toughest challenges through machine learning & AI

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