About
Practical AI, ML & data science for people who build.
Everyday Data Science is an independent publication written by practitioners: people who ship models, maintain pipelines and run the experiment before they write about it. We cover applied AI, agentic systems, machine learning and data work, with a steady focus on what is being built in Africa.
What we stand for
The standards every piece is edited against, whoever writes it.
Sources you can check
Claims link to the paper, dataset, benchmark or filing behind them, so you can read the evidence yourself.
We say what would prove us wrong
Most pieces end by naming the result that would overturn them. A conclusion you cannot test is an opinion.
See the scoreboard →Code you can run
Tutorials ship working code and real outputs, not pseudo-code that only works in the screenshot.
AI in Africa, taken seriously
We cover what is being built across the continent with the same rigour as everything else, not as a side note.
When we're wrong, we say so
If a piece gets something wrong, we fix it in the article and note what changed, rather than quietly editing it away.
Read the corrections log →Who writes here
Data scientists, engineers and researchers writing about what they build.
Founding editor
Ibrahim Denis Fofanah
Data Scientist & AI Researcher
Data scientist and AI researcher at Pace University. I coined Artificial Frictional Unemployment, and built the first machine learning model for crop yield prediction in Sierra Leone. Author of Understanding Agentic AI. I write about agentic systems and applied ML, with a bias toward what actually works, and who gets left out when it doesn't.
Read Ibrahim's work →Write for us
Pitch an idea or republish a post from your own blog.
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Get in touch
hello@everydaydatascience.com