Intermediate18 h
Dashboards and Reporting
Build 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.
Courses
Every technique, in Python and in R
Courses are built around routine programme data rather than tidy teaching datasets. You will meet missing values, inconsistent codes and disputed denominators, because that is what arrives.
Intermediate18 h
Data Visualisation for Programmes
Charts that survive a printer, a projector and a sceptical reviewer, built in matplotlib and ggplot2 against the conventions each cluster already reads fluently.
Advanced24 h
Impact Evaluation Methods
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.
Advanced20 h
Regression for Programme Data
Linear, logistic and multilevel models fitted to survey and routine data, with the interpretation written the way a non-statistician programme manager needs to read it.
Intermediate18 h
Reproducible Analysis Workflows
Make the analysis rerunnable by the person who replaces you — version control, pinned environments, parameterised reports and a project layout that survives a handover.
Intermediate24 h
Applied Statistics for Programmes
Uncertainty, comparison and significance, taught around the claims a programme report makes — not around a textbook sequence of tests.
Intermediate12 h
Data Quality Assessment
Run a routine data quality assessment the way a donor auditor will — verification factors, consistency over time, and a report that names the fix as well as the fault.
Intermediate14 h
Education Programme Analysis
Enrolment, attendance, retention and learning outcomes, with the school-year calendar and the enrolment-versus-attendance distinction that trips up every dashboard.
Intermediate18 h
Food Security Analysis and IPC
Compute FCS, rCSI, HHS and LCS correctly, then use them the way an IPC analysis workshop does — as converging evidence, not as a single score.
Intermediate16 h
Indicator Design and the LogFrame
Write indicator definitions precise enough that two analysts computing them independently get the same number, and trace each one back to the change it is supposed to evidence.
Intermediate24 h
Nutrition Analysis: CMAM and SMART
Anthropometry, admission and outcome indicators, and a SMART survey analysed end to end against the WHO growth standards and the Sphere performance thresholds.
Advanced16 h
Protection and GBV Data
Analyse referral, case management and incident data under the safety and confidentiality rules the sector imposes — including the analyses you must decline to publish.
Intermediate22 h
Public Health and Applied Epidemiology
Incidence, prevalence, cascades and coverage for HIV, TB, malaria and immunisation programmes, plus the outbreak curve you may have to draw at short notice.
Intermediate14 h
Routine Data and DHIS2
Work with an aggregate health information system — its data elements, org unit hierarchy and period logic — and answer the question that always follows: what exactly was counted?
Intermediate22 h
Survey Analysis, Sampling and Weighting
Analyse a two-stage cluster survey properly — weights, strata, design effect and confidence intervals — using the conventions DHS, MICS and SMART already impose.
Intermediate16 h
WASH Analysis
Water access, quality and water point functionality against the JMP service ladders and the Sphere indicators, including the chlorination residual nobody logs consistently.
Beginner16 h
Data Cleaning and Validation
Missing values, duplicates, impossible measurements and inconsistent coding — how to find each one, decide what to do about it, and leave a record a data auditor can follow.
Intermediate14 h
Joining and Reshaping Programme Data
Household and member rosters, repeat groups, admissions and discharges, and monthly aggregates that have to line up with a population denominator.
Beginner20 h
Python for Programme Data
The Python a monitoring and evaluation officer actually needs — environments, pandas, dates, categorical codes and file formats — taught on survey and registry exports rather than on toy data.
Beginner20 h
R and the Tidyverse for Programme Data
The same ground as the Python course, in R — projects and renv, readr and haven, the dplyr verbs, forcats and tidyr — taught on survey and registry exports, with the defaults that differ from pandas named rather than glossed.
Beginner16 h
Data Analysis Foundations for M&E
Load, clean and summarise routine programme data in both Python and R, then turn it into the indicator tables a donor report actually needs.