Data Analyst
The generalist analyst track — take a messy export from any sector, clean it defensibly, and produce the table or figure that answers the question actually asked.
Cassion Learning Ecosystem
Data analysis with Python and R, taught on the data you actually work with: CommCare form dumps, KoboToolbox exports, DHIS2 aggregates, and the messy registers behind them. Built for monitoring and evaluation, public health, nutrition, humanitarian, education and NGO programme work.
Start from the job you hold or the one you are moving toward. Each path names its competencies and how they are assessed.
All pathsThe generalist analyst track — take a messy export from any sector, clean it defensibly, and produce the table or figure that answers the question actually asked.
For the specialist between the health information system and the people who must act on it — reporting completeness, aggregate data structures, and the denominators a routine system does not give you.
For the IM officer holding a cluster's shared data — assessment analysis, inter-agency reporting, and the responsibility questions that come with holding data about people in crisis.
For the manager accountable for a measurement function rather than an analysis — what to standardise, what to let teams decide, and how to tell whether the data your programme produces is worth its cost.
For the data scientist working where the constraint is not the model but the decision — small samples, targeting rules people can contest, and predictions that get acted on by a person visiting a household.
For the manager who has to act on analysis rather than produce it — reading a figure well enough to decide on it, and knowing which question to send back.
For the officer running surveys rather than reading them — sampling, estimation with the design accounted for, and the plausibility checks that decide whether a result can be published at all.
For the officer who now signs off other people's numbers — designing the measurement system rather than filling it in, and defending it in a review where the figures are contested.
The core track for a new M&E officer — indicator definition, routine data quality, and reporting that survives a donor review, taught in both Python and R.
For analysts working with routine health system data — coverage, cascades and completeness — with the epidemiological reasoning to interpret them.
Every technique is taught in Python and in R, because M&E teams are split across both.
All coursesBuild a dashboard someone opens twice, and write the donor report around it — including the refresh, the caveats and the definition panel that stops the monthly argument.
Charts that survive a printer, a projector and a sceptical reviewer, built in matplotlib and ggplot2 against the conventions each cluster already reads fluently.
Counterfactual thinking and the designs that support it — randomisation, difference-in-differences, matching and regression discontinuity — plus how to say what a before-after comparison cannot.
Every dataset ships a data dictionary, provenance, quality notes, and worked examples in both languages. All are synthetic or de-identified.
All datasets975 cases recorded one at a time across sixteen weeks and three districts, shipped with the population they came from, so attack rates and case fatality have denominators and age standardisation is possible.
1,100 admissions across six sites, carrying both MUAC and weight-for-height at admission, so the disagreement between the two admission criteria is measurable rather than assertable, and the treatment outcome every CMAM report is judged on.
996 household interviews drawn from a 470-area frame by stratified PPS sampling, shipped with the frame rather than with the weights, so selection probabilities have to be reconstructed before any estimate is made.