Most models that look impressive in a notebook never reach production, and many that do quietly degrade because nobody planned for data quality, leakage, bias or monitoring. Ten days of hands-on labs give analysts and engineers the habits that separate a reliable, maintainable system from a one-off experiment.
The first week moves from problem framing and success metrics through data cleaning with pandas, exploratory analysis, feature engineering and baseline models in scikit-learn, with disciplined train, validation and test splitting. The second week goes deeper into cross-validation, hyperparameter tuning, gradient boosting, handling imbalanced data, model interpretability with SHAP, fairness checks, reproducible pipelines, Git, experiment tracking with MLflow, testing, containers, REST deployment and drift monitoring. A final project takes a business problem from raw data to a deployed, documented model. The programme is designed for data analysts, junior data scientists, software developers moving into machine learning, statisticians, researchers and technical leads in banks, telecoms, government, health and development organisations. Delegates attend in class, online or in-house and receive a CPD-accredited certificate. Afterwards you can plan, build, validate and hand over a model that colleagues can trust, rerun and maintain.
Organisations in every sector now invest in data science, yet many projects stall between proof of concept and daily use. The causes are rarely about algorithms. They come from vague problem definitions, inconsistent data preparation, models validated on the wrong data, undocumented code that only its author can run, and no process for noticing when performance falls. Applying sound engineering and statistical practice from the first day of a project is the most reliable way to avoid these failures.
The Best Practices in Data Science & Machine Learning in Practice Training Course follows the full lifecycle over ten days. Participants begin with framing business questions and choosing metrics, then work through data quality, exploratory analysis, feature engineering and baseline modelling in Python. Later days address rigorous evaluation, tuning, ensembles, interpretability and fairness, followed by version control, reproducible pipelines, automated tests, packaging, deployment as a service and ongoing monitoring. Responsible use of data, privacy and documentation run throughout.
Every topic is taught in a live coding lab on realistic datasets drawn from finance, health, agriculture and telecommunications. Participants build a project repository as they go, review one another's work using a shared checklist and conclude with an end-to-end capstone that is presented to a panel of facilitators.
By the end of the course, participants will be able to:
Participants leave the course with:
The programme is built around live coding and review so that good habits become routine:
Day 1: Framing Data Science Problems
Day 2: Data Preparation and Quality
Day 3: Exploratory Analysis and Feature Engineering
Day 4: Baseline Models and Honest Evaluation
Day 5: Tuning and Tree-Based Methods
Day 6: Imbalanced, Messy and Small Data
Day 7: Interpretability, Fairness and Responsible Practice
Day 8: Reproducibility and Engineering Discipline
Day 9: Deployment and Monitoring
Day 10: Capstone Project and Handover
The course suits practitioners who already work with data and want to build models more reliably, including:
Participants who attend the ten days and submit the lab work and capstone project receive a CPD-accredited Certificate of Completion issued by Vision Reach Global Consultancy.
Upcoming cohorts
CPD-Accredited
Official invoice & confirmation letter provided
Team discount for 3+ seats
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Everything you need to know about this course before you register.
By the end of the Best Practices in Data Science & Machine Learning in Practice programme, you'll be able to translate a business question into a well-defined modelling task with measurable success criteria, clean, validate and document datasets using reproducible pandas workflows, engineer features and build baseline models before moving to complex methods, and evaluate models with appropriate splits, cross-validation and metrics, and avoid data leakage. The full breakdown of topics is covered session by session in the Course Outline tab above.
The course suits practitioners who already work with data and want to build models more reliably, including: Data analysts moving into predictive modelling, Junior and mid-level data scientists, Software developers and engineers adding machine learning to their skills, Statisticians and researchers who use Python or R, Business intelligence specialists in banks, insurers and telecom companies, Monitoring, evaluation and research officers in NGOs and development agencies, Government statisticians and planning officers, and Technical team leads who review and supervise data science work.
Best Practices in 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.
Best Practices in 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 Best Practices in Data Science & Machine Learning in Practice starts October 19, 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 Best Practices in 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.
Best Practices in Data Science & Machine Learning in Practice Training Course is pitched at intermediate 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 Best Practices in 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 — Best Practices in 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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