Everyday Data Science: applied AI, agentic systems, machine learning, and AI in Africa, written by practitioners for people who build.
Practical AI, ML & data science for people who build.
Most Analytics Bugs Are Data Bugs. Five Checks That Catch Them Before the Meeting.
Five validation checks every analyst should run before trusting a table: duplicates, NULL key columns, out-of-range values, cross-field arithmetic, orphan references. Tested on a 2,010-row orders table with 50 planted defects; the checks flagged 51 rows, and the misses are as instructive as the catches.
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Agentic AI
All articles →
MCP, Explained for Data People: It Standardizes the Plumbing, Not the Thinking
Ibrahim Denis Fofanah·9 min

The Battle for the Agentic Interface: Meta Muse, OpenAI Dots and Grok Bot
Rodriquez Allen·9 min

Build a RAG System That Knows When It Doesn't Know
Rodriquez Allen·8 min
ML & Data Science
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Gradient Boosting Still Rules Tabular Data. The Foundation Models Are Gaining Ground.
Ibrahim Denis Fofanah·13 min

pandas vs Polars vs DuckDB on 3.5 Million Taxi Rides. DuckDB Won Every Query, and the Timings Were the Least Interesting Part.
Ibrahim Denis Fofanah·6 min

Your Retry Worked. That Is How You Got Two Rows.
Ibrahim Denis Fofanah·8 min
Research Digest
All papers →
RAG Systems Collapse When They Retrieve Their Own Writing. One Self-Authored Document Can Start It.
Ibrahim Denis Fofanah·6 min
Agent Reflection Does Not Beat a Retry. The Numbers From 16,946 Trials.
Ibrahim Denis Fofanah·7 min

Quantizing Half the Attention Hurt. Quantizing Both Paths Barely Did.
Ibrahim Denis Fofanah·12 min
Most read
- Google's AI Tutor Raised Maths Scores in Sierra Leone. It Raised Them Most for Students Who Were Already Ahead.Ibrahim Denis Fofanah · 12 min
- AnalysisExcel or Python First? What Data Analyst Job Postings Actually SayIbrahim Denis Fofanah · 5 min
- Deep DiveThe Agent Revolution Is Here, and Most Organizations Are Not ReadyIbrahim Denis Fofanah · 7 min
- Benchmark WatchSame Model, Same Benchmark: 54.8% or 99.9% Depending on the HarnessIbrahim Denis Fofanah · 9 min
- AnalysisBrain Waves to Words: What Brain2Qwerty Actually Does, and What It Doesn'tIbrahim Denis Fofanah · 6 min
Start here
New here? Go deeper than one article.
Step-by-step learning paths, one-page cheat sheets you can keep open while you work, and the book.
Learning paths
Building Agentic AI Systems
A practical path from why agents matter to building your first multi-agent pipeline: the landscape, the key design decisions, and the hands-on build.
6 parts →
The Practical Data Analyst
The learning path that matches what employers actually ask for. SQL appears in over 80% of data analyst job postings, Excel in 60%, Python in 50%. This is the order to learn them in, why that order works, and the trap that costs most beginners a year.
3 parts →
Data Thinking Series
Learn how experienced Data Scientists think before they write code. The mindset behind data analysis, machine learning and AI: instead of memorising Python, SQL or Pandas commands, you learn how professionals ask questions, understand business problems, validate data, and make better decisions in the age of AI.
3 parts →
Cheat sheets
Books & resources
📚 Understanding Agentic AI
By Ibrahim Denis Fofanah, the editor of Everyday Data Science. A practitioner guide to agent systems: the core concepts, the architectures, and how to apply them in real projects.
Get the book →Africa AI Spotlight
Building Intelligent Systems for the World's Fastest-Growing Markets
Africa is not just adopting AI, it's inventing new architectures for low-resource languages, unreliable infrastructure, and mobile-first contexts. Everyday Data Science brings you the stories nobody else is covering.
AI in Africa · News
UNDP and GSMA Back African-Language AI. The Announcement Is Real; the Numbers Are Not
Ibrahim Denis Fofanah · 6 min read
Compute · AI in Africa
12,000 GPUs Are Landing in Africa. The Real Bottleneck Was Never Just the Hardware.
Ibrahim Denis Fofanah · 7 min read·1
Policy Brief · AI Infrastructure Finance
Korea and AfDB Announced an Africa AI Hub. The Missing Number Is the Budget.
Ibrahim Denis Fofanah · 8 min read
Africa · Agriculture · Data Infrastructure
Africa’s Agricultural AI Has an Invisible Bottleneck: Ground Truth
Ibrahim Denis Fofanah · 8 min read
Ibrahim Denis Fofanah
Data Scientist & AI Researcher
From the Editor
Why I Built This, and Who It's For
I'm a data scientist and AI researcher at Pace University's Seidenberg School, and founder of the Rise Africa Foundation for STEM and Innovation in Sierra Leone. My research keeps circling one question: what happens to the people a system doesn't see? That question produced Artificial Frictional Unemployment, the finding that automated hiring systems reject qualified people not for lack of skill, but because of how algorithms read them (arXiv:2601.14534). And it produced the first machine learning model for crop yield prediction in Sierra Leone, where a country plans its food security largely blind, not because the technology is hard, but because nobody had built it (arXiv:2606.13959). I'm also the author of Understanding Agentic AI. I started Everyday Data Science because I got tired of reading AI writing by people who don't build things. Everything here is meant to be usable, and honest about what doesn't work, including my own results.
Writing on Everyday Data Science
