Path
Program Manager
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.
Competencies
Reading an indicator
BeginnerEstablish what a figure counts, what it excludes and what decision it can support, without recomputing it.
Uncertainty for decisions
IntermediateTell a difference that is real from one the data cannot support, and act accordingly rather than on rank order.
Interrogating an analysis
IntermediateAsk the three or four questions that reveal whether an analysis is sound, and recognise an answer that does not answer.
Acting under limitation
IntermediateTake a decision knowing what the data does not establish, and record which assumption the decision rests on.
Before you start
- No programming background, and no intention of becoming an analyst — this path is for commissioning and reading analysis, not producing it.
- Responsibility for a programme whose indicators you have to report on, and preferably one figure you have never fully trusted.
The route through
Stage 1
Ground the numbers
Read an indicator table the way the person who built it did, and know which question turns a plausible figure into a checked one.
Stage 2
Make the data trustworthy
Turn a raw export into a table you would defend line by line, and hand over a cleaning log that answers the auditor's question before it is asked.
Stage 3
Put the files together
Assemble several exports into one analysis table you would defend column by column, with every join proved, every grain stated and every denominator sourced.
Stage 4
Check it like an auditor
Assess your own data before a donor does — five dimensions with measures, a recount against the source, and a report where every finding has an owner and a date.
Stage 5
Define what you report
Write indicator definitions two analysts compute the same way, defend the denominator, and set a baseline and target that survive a mid-term review.
Stage 6
Put an interval on it
Analyse a cluster survey the way its design requires — weights from the frame, a measured design effect, and an interval that says what the sample can and cannot settle.
Stage 7
Know where it came from
Read a routine reporting system as the database it is, pull an extract you can point at months later, and answer what was counted for any figure it produces.
Stage 8
Apply it to nutrition
Take the methods into one sector — WHO growth standards, SMART plausibility, the IPC phases and the Sphere performance thresholds, on a survey and a treatment register.
Stage 9
Apply it to public health
Rates with person-time denominators, treatment cascades, coverage three ways, and an outbreak line list turned into a curve, attack rates and a case fatality you can defend.
Stage 10
Apply it to WASH
The JMP service ladders and the Sphere minimums on a household survey, then a monitoring register with repeat visits that turns one functionality rate into three and bounds each against the rounds nobody drove.
Stage 11
Apply it to food security
FCS, HHS, rCSI and the livelihood coping module built from raw components and found to disagree by a factor of seven, then a price series whose seasonality dwarfs the programme effect and the evidence table that reports both.
Stage 12
Apply it to protection
The analyses you must decline to publish, alongside the ones that matter — a consent-gated referral pathway, a nineteen-point equity gap located at a specific gate, and a caseload that explains two other tables.
Stage 13
Apply it to education
Gross against net enrolment on a projected denominator, two attendance numbers twenty-six points apart, a cohort through promotion and repetition, and two assessment rounds whose instruments differ.
Stage 14
Say how sure you are
An interval on every proportion, the right test for a comparison, an effect size beside every p-value, and the count of comparisons that turns two striking schools back into noise.
Stage 15
Model more than one thing at once
A coefficient is a comparison — which one, between which units, adjusted for what. An odds ratio your reader will misread, a covariate that removes 42% of the effect, and a model that explains three per cent and settles a targeting decision.
Stage 16
Say what caused it
A seven-point gain that is entirely the school year, a comparison group imbalanced on every characteristic measured, and the minimum detectable effect that decided the answer before any data existed.
Stage 17
Put it in front of them
The mark the comparison implies, an interval that stops a ranking, a palette that already means something to this audience, and a figure generated from the dataset so the chart and the sentence cannot drift.
Stage 18
Make it rerunnable
Raw data read-only and code the only thing edited, an environment pinned so a colleague's laptop gives your numbers, checks that stop the pipeline rather than producing a plausible wrong one, and a handover a successor can act on.
Stage 19
Put it in front of the decision
A page that answers one question rather than twenty, the definition panel that stops the monthly argument, and the report whose findings, limitations and recommendation survive being read separately.
Recommended projects
How the skill is assessed
Can read a ranked list and identify which parts of the order the data supports.
EvidenceAn allocation decision that names the sites whose separation is real and those whose is not.
Can state what a figure does not establish before acting on it.
EvidenceA decision note recording the limitation and the assumption taken.
Can send an analysis back with a specific question rather than a general doubt.
EvidenceA written request naming the denominator, exclusion or threshold in question.
A manager does not need to compute a prevalence. They need to know that the site ranked first and the site ranked fourth may not actually differ, because that is the difference between allocating stock where it is needed and allocating it where the sort order happened to land.
This path is deliberately the shortest one, and it is built from the reading side of three projects. The nutrition dashboard ranks twelve sites and then says plainly that only three are decisively above the district median — the other nine overlap. The school project produces a visit list and then states it ranks risk, not dropout, and that a home visit arriving as though the student has already dropped out will be the last one that family accepts.
The skill is knowing which of those sentences changes what you do.
What this path still needs
Three courses are published: Data Analysis Foundations, Python for Programme Data and R and the Tidyverse for Programme Data. The interpretation and communication content this role uses most sits in Communication and Delivery on the programme roadmap, which is not yet written. See the programme.