HANDS-ON STATISTICAL ANALYSIS TRAINING . OCTOBER, 2026 COHORT . LAGOS & ONLINE
YOU HAVE THE DATA? NOW, LEARN TO MAKE SENSE OF IT
Analyse your research data like the biostatisticians who publish it.
A hands-on Stata and machine-learning training for health researchers who want practical confidence in analysing and interpreting data, taught by working epidemiologists and biostatisticians, using real datasets, from your first descriptive table to advanced survival models and AI-assisted analysis.
HANDS-ON STATISTICAL ANALYSIS TRAINING . OCTOBER, 2026 COHORT
YOU HAVE THE DATA? NOW, LEARN TO MAKE SENSE OF IT
Analyse your research data like the biostatisticians who publish it.
A hands-on Stata and machine-learning training for health researchers who want practical confidence in analysing and interpreting data, taught by working epidemiologists and biostatisticians.
9+
Years of practical Training
2000+
Health Researchers Trained
2
Professional Training Level
Physical + Online
Flexible Learning Options
9+
Years of practical Training
2000+
Health Researchers Trained
2
Professional Training Level
Physical + Online
Flexible Learning Options
The gap between data and findings
Great research shouldn't stall at the analysis stage
- You have clean, hard-won data but no clear plan for which test actually answers your research question.
- Reviewers send papers back over statistical method, not the science, a fixable problem with the right training.
- Stata feels like a black box, so results get reported without full confidence in what's driving them.
- Machine learning and AI are reshaping data analysis, and self-teaching from scattered tutorials is slow going.
“Practical skills. Real data. Real impact.” that’s the whole premise of this programme: you leave able to run and defend your own analysis, not just recite formulas.
Fruitful Vine International, programme philosophy
The gap between data and findings
Great research shouldn't stall at the analysis stage
- You have clean, hard-won data but no clear plan for which test actually answers your research question.
- Reviewers send papers back over statistical method, not the science, a fixable problem with the right training.
- Stata feels like a black box, so results get reported without full confidence in what's driving them.
- Machine learning and AI are reshaping data analysis, and self-teaching from scattered tutorials is slow going.
“Practical skills. Real data. Real impact.” that’s the whole premise of this programme: you leave able to run and defend your own analysis, not just recite formulas.
Fruitful Vine International, programme philosophy