Insights for African data pros
Tutorials, career advice, and industry analysis from the Datika team.
Getting started with Pandas: A practical guide for African datasets
Learn how to clean, transform, and analyse real-world African business data using pandas — from M-Pesa transaction records to agricultural datasets.
SQL window functions you're not using (but should be)
ROW_NUMBER, RANK, LAG, LEAD — window functions are one of the most underused tools in SQL. Here's a practical breakdown with real examples.
How African startups are using ML to transform agriculture
From crop disease detection to yield prediction, African agritech is leading the world in applied machine learning. We profile five companies doing it.
Analysing M-Pesa transaction data with Python
A step-by-step tutorial on parsing M-Pesa transaction statements, aggregating spending patterns, and visualising insights with matplotlib.
Breaking into data science in Africa: A 2026 guide
The skills you need, the companies hiring, and the salary expectations for data science roles across Kenya, Nigeria, South Africa, and Ghana.
Power BI vs Tableau: Which should you learn first?
An honest comparison for African learners — cost, job market demand, learning curve, and M365 integration. The answer might surprise you.