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Teacher since July 2025

Kojo Amankwah

Experiments, causal questions and responsible models, explained for decision makers

9

tutors built

4.5Sample

average from 23 reviews

437Sample

lessons taught by their tutors

About Kojo

I help analysts and product people answer the question behind most data work: did this change cause that result? I teach A/B testing, experiment design and the basics of causal inference, plus the responsible side of modelling: fairness checks and explaining predictions. My background is in product analytics and experimentation, where I learned that a clear sentence to a decision maker is worth more than a clever method. I teach with worked cases, sketches of causal diagrams, and lots of practice explaining results out loud. I also coach people preparing for data science interviews.

Knows about

  • A/B testing
  • experiment design
  • statistical power
  • sequential testing
  • causal inference
  • fairness checks
  • model interpretability
  • communicating results
  • data science interviews

Tutors by Kojo

9 tutors

Communicating Results to Non Experts

Communicating Results to Non Experts

Turn analysis into clear sentences, simple charts and decisions people act onAll levelsData science and statistics4.3(4)82 lessonsSampleKojo Amankwah$5Model Interpretability and Explanations

Model Interpretability and Explanations

Explain what drives a model's predictions, globally and case by case, without overclaimingIntermediateMachine learning4.3(3)64 lessonsSampleKojo Amankwah$7Designing Experiments: Power, Units and Blocking

Designing Experiments: Power, Units and Blocking

Plan experiments that can actually detect the effects you care aboutIntermediateData science and statistics4.7(3)64 lessonsSampleKojo Amankwah$7Correlation Is Not Causation

Correlation Is Not Causation

Learn the everyday ways correlations mislead and what real causal evidence looks likeBeginnerData science and statistics4.5(4)60 lessonsSampleKojo AmankwahFreeA/B Testing for Beginners

A/B Testing for Beginners

Run your first A/B test properly, from hypothesis to a decision you can defendBeginnerData science and statistics4.7(3)60 lessonsSampleKojo Amankwah$4Data Science Interview Practice

Data Science Interview Practice

Practise statistics, ML, SQL and case questions with structured feedback on your answersAll levelsData science and statistics4.3(3)57 lessonsSampleKojo Amankwah$8Fairness Checks for ML Models

Fairness Checks for ML Models

Measure how a model treats different groups and understand the trade offs between fairness metricsIntermediateMachine learning4.7(3)50 lessonsSampleKojo Amankwah$7Causal Inference Basics

Causal Inference Basics

Estimate causal effects from observational data and state your assumptions out loudAdvancedData science and statisticsNewKojo Amankwah$12Sequential Testing and the Peeking Problem

Sequential Testing and the Peeking Problem

Monitor experiments early and often without inflating your false positive rateAdvancedData science and statisticsNewKojo Amankwah$11

Recent reviews

What students said about Kojo's tutors.

  • Ana P.Sample

    Exactly the right level for a marketer. No heavy maths, but nothing important skipped.

    On A/B Testing for Beginners

  • Rahul S.Sample

    Useful rewrites of my own drafts. Chart lesson was good but brief.

    On Communicating Results to Non Experts

  • Femi O.Sample

    The 'so what?' questions were exactly what my director asks. My last summary got a decision in one meeting instead of three.

    On Communicating Results to Non Experts

  • Gwen L.Sample

    Helpful, though much of it was familiar from previous communication training. Most valuable for less experienced analysts.

    On Communicating Results to Non Experts

  • Chloe D.Sample

    I run a small online shop and this helped me realise my traffic was too low for tiny tweaks. Now I test bigger changes and write decision notes.

    On A/B Testing for Beginners

  • Peter J.Sample

    Practical and honest about trade offs. I would have liked a longer section on intersectional groups.

    On Fairness Checks for ML Models