Live Online Professional Data Science & Machine Learning in Practice Training Course
Live online, instructor-led · next cohort Oct 26, 2026 · from USD 1,700
As data science matures, organisations expect more than a promising notebook. They want analysis that stands up to review, code that colleagues can maintain, estimates that come with honest uncertainty and models that are documented, fair and ready to run in production. Practitioners who learnt on the job often have gaps in exactly these areas, and ten intensive days address them. You start with engagement scoping and stakeholder management, then move to production-quality Python with pytest, Ruff and Git workflows, and to larger data with SQL window functions, DuckDB, Polars, Parquet and PySpark.
Statistical rigour follows through bootstrapping, power analysis, multiple-testing control and Bayesian modelling in PyMC. Second-week sessions cover regularised models and gradient boosting tuned with Optuna, probability calibration and conformal prediction, causal inference with DoWhy and EconML, SHAP explanations, Fairlearn audits and model cards, and deployment using Docker, MLflow, GitHub Actions and Evidently. A peer-reviewed capstone completes the programme. Working data scientists, senior analysts, statisticians, quantitative researchers and ML engineers will gain most. Afterwards you can run a project to a recognisably professional standard and defend your methods under scrutiny. Delivery is classroom, online or in-house, with a CPD-accredited certificate.
At a Glance
How Live Online Training Works
Live online training, led by a trainer in real time, for participants anywhere.
More about live online training →- Format
- Live online, instructor-led
- Duration
- Mon - Fri (10 Days)
- Fee
- USD 1,700 (KES 160,000)
- Next start dates
- Oct 26, 2026, Nov 23, 2026, Dec 21, 2026
- Certificate
- Certificate of Completion
- What you need
- A computer with a reliable internet connection, a webcam and a headset
About this course
Data science has become a profession with expectations to match. Analysts who once worked alone in notebooks now contribute to shared codebases, defend findings in front of risk committees and auditors, and hand models to engineering teams who must run them for years. Weak statistical reasoning, untested code, unexplained models and overconfident forecasts are no longer tolerated, particularly in regulated sectors such as banking, insurance, health and public administration.
The Professional Data Science & Machine Learning in Practice Training Course is written for practitioners who can already build a model and want to do so to a higher standard. The first five days strengthen the foundations of professional work: scoping engagements, writing maintainable and tested Python, handling data at scale, statistical inference and a disciplined modelling toolkit. The second five days go further into calibration and uncertainty quantification, causal inference and experimentation, explainability, fairness and professional ethics, and production delivery, finishing with a capstone that is code-reviewed and presented as a client engagement.
Participants work in a shared Git repository from the first morning, review one another's code and write short technical memos as well as notebooks. Exercises draw on financial services, health, energy and public sector data, and the emphasis throughout is on defensible choices: why a method was chosen, what its assumptions are and how confident anyone should be in the answer.
Training Objectives
After the ten days, experienced practitioners will be able to:
- Scope a data science engagement with clear deliverables, assumptions, risks and acceptance criteria.
- Write modular, tested and version-controlled Python that others can review and maintain.
- Process large datasets efficiently with SQL, DuckDB, Polars and PySpark.
- Apply rigorous inference, including bootstrapping, power analysis and Bayesian estimation.
- Quantify predictive uncertainty through calibration, prediction intervals and conformal methods.
- Estimate causal effects from observational and experimental data and state the assumptions behind them.
- Document, explain and audit models for fairness using model cards, SHAP and Fairlearn.
- Deliver models through reproducible pipelines with automated testing, deployment and monitoring.
What you'll learn
Key Takeaways
Participants return to work with:
- A project template repository with environment files, tests, linting and continuous integration configured.
- An engagement scoping document and stakeholder plan they can reuse with internal or external clients.
- A statistical analysis plan covering power, multiple comparisons and uncertainty reporting.
- A calibrated model with conformal prediction intervals and a decision-threshold analysis.
- A causal effect estimate with a documented causal diagram and robustness checks.
- A completed model card and fairness audit for a model of their own.
- Written feedback from a structured code review of their capstone submission.
- A personal development plan listing target competencies and the evidence to collect for each.
Training Methodology
The programme treats participants as working professionals and mirrors how strong data teams operate. It uses:
- A shared Git repository in which all lab work is committed, reviewed and tested
- Expert briefings that focus on assumptions, failure modes and judgement calls
- Advanced labs on financial, health, energy and public sector datasets
- Structured peer code reviews and critique of statistical write-ups
- Written technical memos alongside notebooks to build reporting discipline
- A capstone run as a simulated client engagement with a review panel
Day 1: The Professional Data Science Engagement
- What distinguishes professional practice from ad hoc analysis
- Stakeholder mapping and requirements interviews
- Writing a problem statement, scope and acceptance criteria
- Estimating effort and planning delivery iterations
- Data access, data sharing agreements and confidentiality
- Risk registers for analytical projects
- Communicating through technical memos and executive briefings
- Workshop: drafting an engagement plan for a realistic brief
Day 2: Production-Quality Python
- Structuring a project: packages, modules and configuration
- Reproducible environments with conda, uv or Poetry
- Writing functions with type hints and docstrings
- Unit tests and data tests with pytest
- Linting and formatting with Ruff and pre-commit hooks
- Git branching, pull requests and constructive code review
- Moving from exploratory notebooks to scripts and pipelines
- Lab: refactoring a messy notebook into a tested package
Day 3: Data Engineering Skills for Data Scientists
- Advanced SQL: common table expressions and window functions
- Columnar storage with Parquet and fast analytics with DuckDB
- Polars for high-performance data frames
- Introduction to PySpark for distributed processing
- Data validation with pandera or Great Expectations
- Data contracts and lineage between producers and consumers
- Orchestrating repeatable workflows with Prefect or Airflow
- Lab: building a validated feature table from several million rows
Day 4: Statistical Rigour and Inference
- Sampling designs, survey weights and representativeness
- Confidence intervals by formula and by bootstrap
- Hypothesis tests, p-values and common misinterpretations
- Statistical power and sample size calculations
- Multiple comparisons and false discovery rate control
- Regression diagnostics and robust standard errors
- Bayesian estimation with PyMC: priors, posteriors and credible intervals
- Lab: reanalysing a survey with proper uncertainty reporting
Day 5: A Disciplined Modelling Toolkit
- Regularised linear and generalised linear models
- Gradient boosting with LightGBM and CatBoost
- Choosing validation schemes: grouped, stratified and time-series splits
- Nested cross-validation to avoid optimistic estimates
- Hyperparameter optimisation with Optuna
- Learning curves for diagnosing bias and variance
- Experiment tracking and comparison with MLflow
- Lab: a fair benchmark of competing models on a credit dataset
Day 6: Uncertainty, Calibration and Decision Analysis
- Why well-calibrated probabilities matter for decisions
- Reliability diagrams, Brier score and calibration methods
- Quantile regression for prediction intervals
- Conformal prediction for distribution-free coverage
- Cost-sensitive thresholds and expected value analysis
- Communicating uncertainty to decision makers
- Lab: turning model scores into a calibrated, cost-optimised decision rule
Day 7: Causal Inference and Experimentation
- Correlation, confounding and causal diagrams
- Randomised experiments and A/B test design
- Propensity score matching and weighting
- Difference-in-differences and regression discontinuity
- Heterogeneous treatment effects and uplift modelling
- Causal tools in Python: DoWhy and EconML
- Sensitivity analysis and stating assumptions honestly
- Lab: estimating the effect of a programme or marketing intervention
Day 8: Explainability, Fairness and Professional Ethics
- Global and local explanations with SHAP and partial dependence
- Counterfactual explanations for affected individuals
- Fairness definitions and the trade-offs between them
- Bias auditing and mitigation with Fairlearn
- Model cards and datasheets for datasets
- Data protection principles: lawful basis, minimisation and anonymisation
- Professional codes of conduct and resisting pressure to overstate results
- Lab: auditing and documenting a lending or hiring model
Day 9: Delivering Models to Production
- Packaging models and pipelines for reuse
- Serving predictions through FastAPI in Docker containers
- Continuous integration and delivery with GitHub Actions
- Model registry, versioning and approval workflows
- Monitoring data drift and performance with Evidently
- Incident response and rollback for models in production
- Lab: an automated pipeline from commit to deployed model
Day 10: Peer-Reviewed Capstone Engagement
- Capstone brief: a client problem with real constraints
- Engagement plan and analysis plan sign-off
- Building and validating the solution in a shared repository
- Structured peer code review against an agreed checklist
- Technical memo and model card for the final solution
- Presentation and challenge session with a mock review panel
- Professional development planning and course close
Participants should already build models in Python. Typical roles include:
- Data scientists with one or more years of hands-on experience
- Senior data analysts and analytics engineers moving into modelling
- Statisticians and econometricians in central banks, statistics bureaus and research institutes
- Quantitative analysts and model developers in banking and insurance
- Machine learning engineers seeking stronger statistical grounding
- Model validators and internal audit specialists reviewing analytical work
- Health and epidemiological data scientists
- Data science consultants delivering client projects
- Lead analysts in development agencies running impact evaluation and targeting models
Participants who attend every session, commit their lab work to the course repository and present a reviewed capstone receive a CPD-accredited Certificate of Completion issued by Vision Reach Global Consultancy.
The certificate states the course title, dates and mode of delivery. The capstone repository, model card and technical memo together form a professional portfolio that participants can show to employers or clients.
Choose Where You Want to Learn
Professional Data Science & Machine Learning in Practice Training Course Venues
KenyaNairobiNext Nov 9· 13 dates$3,000- KenyaMombasaNext Oct 26· 13 dates$3,200
- KenyaNaivashaNext Nov 2· 13 dates$3,000
- KenyaKisumuNext Oct 26· 13 dates$3,000
- KenyaMachakosNext Nov 2· 13 dates$3,200
UgandaKampalaNext Nov 2· 13 dates$3,800
RwandaKigaliNext Oct 26· 13 dates$3,800
TanzaniaArushaNext Nov 9· 13 dates$3,800- TanzaniaDodomaNext Oct 26· 13 dates$3,600
- TanzaniaDar es SalaamNext Oct 26· 13 dates$4,000
- TanzaniaZanzibarNext Nov 16· 13 dates$5,200
NigeriaLagosNext Nov 16· 13 dates$5,600- NigeriaAbujaNext Nov 9· 13 dates$5,600
South AfricaPretoriaNext Nov 2· 13 dates$6,400- South AfricaJohannesburgNext Nov 2· 13 dates$6,800
- South AfricaCape TownNext Oct 26· 13 dates$7,600
UAEDubaiNext Oct 26· 13 dates$7,600
GhanaAccraNext Nov 16· 13 dates$9,000
Scroll sideways to see all 18 cities →
Frequently Asked Questions About Live Online Professional Data Science & Machine Learning in Practice Training Course
Everything you need to know about this course before you register.
By the end of the Professional Data Science & Machine Learning in Practice programme, you'll be able to scope a data science engagement with clear deliverables, assumptions, risks and acceptance criteria, write modular, tested and version-controlled python that others can review and maintain, process large datasets efficiently with sql, duckdb, polars and pyspark, and apply rigorous inference, including bootstrapping, power analysis and bayesian estimation. The full breakdown of topics is covered session by session in the Course Outline tab above.
Participants should already build models in Python. Typical roles include: Data scientists with one or more years of hands-on experience, Senior data analysts and analytics engineers moving into modelling, Statisticians and econometricians in central banks, statistics bureaus and research institutes, Quantitative analysts and model developers in banking and insurance, Machine learning engineers seeking stronger statistical grounding, Model validators and internal audit specialists reviewing analytical work, Health and epidemiological data scientists, Data science consultants delivering client projects, and Lead analysts in development agencies running impact evaluation and targeting models.
Professional Data Science & Machine Learning in Practice Training Course typically runs as 10 Days. It's available as in-person classroom, live virtual, and in-house corporate training — every course can also be delivered on-site for your team on dates that suit you.
Professional Data Science & Machine Learning in Practice Training Course is scheduled in-classroom in Nairobi, Kenya, Mombasa, Kenya, Naivasha, Kenya, and Kisumu, Kenya, and 14 other locations, plus a live interactive virtual classroom you can join from anywhere. Check the schedule panel above for exact upcoming dates and fees in each location.
The next live virtual cohort of Professional Data Science & Machine Learning in Practice starts October 26, 2026, with new classroom cohorts also running on a rolling basis. Pick a date and location in the schedule panel above, then click "Register for the Course" — it takes a few minutes and your seat is confirmed once payment or a signed purchase order is received.
Yes — delegates who meet the attendance requirement receive a Certificate of Completion for Professional Data Science & Machine Learning in Practice Training Course from Vision Reach Global Consultancy, issued in the name you register with, so double-check the spelling at checkout.
Professional Data Science & Machine Learning in Practice Training Course is pitched at advanced professionals. If you're unsure whether it's the right fit for your current role or background, message our training advisors before you register and they'll help you confirm.
Fees for Professional Data Science & Machine Learning in Practice Training Course vary by delivery location and format and are shown in real time in the schedule panel above once you pick a date. Register 3 or more delegates on the same course together and a 5% team discount is applied automatically — larger cohorts can request a custom corporate quote.
Yes — Professional Data Science & Machine Learning in Practice Training Course can be delivered on-site at your offices (or virtually for distributed teams), with case studies and examples tailored to your industry and the specific challenges your team is working through. Switch to the "In-House" tab in the schedule panel above to request a proposal.
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