Statistical Analysis in Python for Decision Makers
Read the numbers before you trust them. In five days you will move from distributions to regression in Python — pandas, SciPy, statsmodels — and finish able to interrogate any analysis, challenge its claims and commission better ones.
The executive challenge
The problem this program solves
This is statistics for the people who sign off on decisions, taught hands-on rather than as theory. You will run every analysis yourself in Python on real business data, so the concepts live in your hands, not just your notes. Taught by faculty led by Dr. Kennedy Mwai (Roche) who spend their working lives stress-testing analyses before executives act on them.
Context
Why this matters now
Capability
What you will be able to do
- Describe, visualise and communicate real datasets honestly — central tendency, spread, distribution shape and their traps
- Reason correctly with sampling, confidence intervals and p-values, including what statistical significance does not tell you
- Run and interpret t-tests, chi-square tests and ANOVA in Python on business questions
- Build and interpret linear and logistic regressions in statsmodels, including confounding and multicollinearity
- Design and interpret an A/B test with appropriate power, and recognise the five ways tests get misread
- Review any incoming analysis with a structured checklist: data provenance, assumptions, effect sizes, robustness
The IPAT Course Standard
The five-day journey
Each day is a deliberate progression in managerial capability — from strategic context, to analytical frameworks, to applied practice, to executive judgement under pressure, to a capstone that produces something your organisation keeps.
IPAT Signature Case™
Did the Promotion Work? A Retail Pricing Inquiry
A Nairobi-headquartered retail chain ran a two-week price promotion in 40 of its 120 stores. Sales in promoted stores rose 18 percent and the category team declared victory. But the promotion had been placed in exactly the stores with seasonal peaks — and a same-store, pre-post comparison against a regression-adjusted control told a different story: roughly a third of the lift would have happened anyway, and margin after discounting was negative for two product lines.
Your decision: Report the 18 percent uplift and scale the programme, or present the regression-adjusted result showing a marginal, category-specific effect — knowing the second finding triggers an awkward review of how promotions get designed.
Real-World Case Study
A Thousand Concurrent Experiments: Booking.com’s Testing Culture
Booking.com published the largest study ever run of its experimentation culture — over a thousand concurrent A/B tests analysed in a KDD (2020) paper co-authored with its data scientists. The findings were uncomfortable: most “successful” tests delivered tiny effects, statistical significance frequently conflicted with business materiality, and interference between concurrent experiments was real. Rigour was a routine, not a project.
The lesson: Statistical rigour is an organisational habit: the firms that profit from testing are the ones whose leaders read effect sizes and guardrails, not just significance flags.
IPAT Data Lab™
A product team has shipped four “significant” wins this year but revenue has not moved — and leadership now doubts the analytics function itself.
Dataset: A realistic e-commerce experiment dataset with a low-powered primary metric, guardrail metrics, seasonality and a segment effect hiding the average.
Your task: Diagnose the four tests in Python — power, peeking, multiple comparisons, segment analysis — rebuild the primary analysis in statsmodels, and write the remediation plan for the testing process.
A diagnosis report and a remediated analysis you would defend to a CEO.
Data → Analysis → Decision, not Data → Code → Certificate
IPAT DIOS™ Framework · Data Intelligence for Official Statistics & Organisations
From raw data to strategic intelligence in five phases
Software-led programmes at IPAT do not teach syntax — they teach statistical data architecture, the discipline that turns statistical analysis data into a strategic asset. The programme runs on the five-phase IPAT DIOS™ arc, adapted end-to-end to Python & pandas.
Assess
Diagnose the current state of statistical analysis data processing: a baseline of Python & pandas proficiency, data silos, manual bottlenecks and reproducibility gaps in the workflows your institution runs today.
Architect
Design standardized, reproducible data structures: consistent naming conventions, master schemas and version-controlled projects in Git, so every statistical analysis analysis stands on the same interoperable foundation.
Automate
Replace manual, error-prone steps with automated pipelines: ingest, clean and transform statistical analysis data with pandas, NumPy and SciPy, and automate with reproducible Jupyter and Quarto pipelines the routine reporting that consumes weeks of analyst time.
Analyze
Create decision intelligence: cross-tabulation with proper variance estimation, time-series visualization, geospatial mapping and predictive modelling — applied to realistic, anonymized statistical analysis data.
Accelerate
Build for what is next: AI-ready datasets, machine learning for imputation and estimation, and interactive Streamlit and Plotly dashboards that put statistical analysis intelligence directly in decision-makers’ hands.
Day 5 · The Capstone
What will you change when you return to work?
Build a twelve-month analytical and AI roadmap for your organisation: the value case, the data foundations, the governance, and the first 90 days.
Methodology
Frameworks you will master
- • IPAT DIOS™ Decision-Integrity Standard
- • pandas / SciPy / statsmodels toolchain
- • Bootstrap and simulation-based inference
- • A/B testing power and guardrail design
- • The analysis-review checklist method
Career Outcomes
Where this program takes you
Salary bands reflect 2025–26 market benchmarking across African markets plus global mobility ranges.
Cohorts
Upcoming cohorts
New dates are being scheduled
Register with your preferred start date and our admissions team will match you to the next available cohort — or contact us to arrange an in-house delivery for your organisation.
Register your interestExplore further
Related programmes
Learners of Statistical Analysis in Python for Decision Makers frequently pair it with these offerings from the same divisions.