Machine learning projects that disappoint usually fail on the data rather than the algorithm: tables that do not join, leakage that inflates accuracy, features nobody can explain and results nobody can act on. Ten days of hands-on labs build the analytical groundwork that separates useful models from impressive demos. You extract and join data with SQL, profile and clean it in Python with pandas, test data quality, and explore distributions, correlations and segments with matplotlib and seaborn. Statistical sessions cover sampling, hypothesis tests, confidence intervals and regression diagnostics.
The second week moves to feature engineering for tabular, text and time series data, cross-validation, evaluation metrics, threshold choice, SHAP-based interpretation, A/B testing and uplift measurement, then tracking experiments with MLflow and presenting results in Power BI. Data analysts, aspiring data scientists, BI developers, statisticians, research and M&E officers, actuaries and risk analysts in banking, telecoms, health, retail, government and NGOs benefit most. Join in the classroom, online or in-house and earn a CPD-accredited certificate. Afterwards you can prepare a reliable modelling data set, judge whether a model truly adds value and explain the evidence to decision-makers.
Organisations across Africa are hiring data scientists and investing in machine learning platforms, yet many models never reach production or are quietly abandoned because the underlying data was poorly understood. Strong analytics practice, meaning disciplined extraction, quality checks, exploratory work, sound statistics and honest evaluation, is what turns machine learning from an experiment into a dependable decision tool. It is also the skill set that analysts and BI professionals need to move confidently into data science.
The Data and Analytics for Data Science & Machine Learning in Practice Training Course runs over ten days and follows the analytical path of a real project. Days one to five cover framing the business question, writing SQL to assemble modelling tables, cleaning and validating data, exploratory and statistical analysis, and building and evaluating baseline models with scikit-learn. Days six to ten go deeper: advanced feature engineering, imbalanced data and time series, model diagnostics and interpretability, experiment design and causal reasoning, reproducible pipelines with monitoring after deployment, and a capstone project that takes a raw data set through to a presented recommendation.
Every concept is practised in Jupyter notebooks on realistic data from finance, telecoms, health and public services. Participants keep their notebooks, SQL scripts and templates for use in their own work.
Participants who complete the programme will be able to:
Participants complete the programme with:
The course is taught as a sequence of guided labs, with each concept applied in code the same day. Participants work through:
Day 1: Framing Analytics and Machine Learning Problems
Day 2: Data Acquisition with SQL
Day 3: Data Cleaning and Quality Assurance
Day 4: Exploratory and Statistical Analysis
Day 5: Baseline Models and Evaluation
Day 6: Advanced Feature Engineering
Day 7: Imbalanced Data, Time Series and Forecasting
Day 8: Model Diagnostics, Interpretability and Fairness
Day 9: Experiments, Impact Measurement and MLOps Analytics
Day 10: Capstone Project
The course is designed for analytically minded professionals who want to strengthen the data side of machine learning, including:
A CPD-accredited Certificate of Completion issued by Vision Reach Global Consultancy is awarded to participants who attend the ten days and submit the lab notebooks and capstone project.
Participants also keep their code, templates and capstone presentation, which form a practical portfolio they can share with employers or use to support continuing professional development records.
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 Data and Analytics for Data Science & Machine Learning in Practice programme, you'll be able to frame business problems as analytical and machine learning questions with clear success measures, write sql queries that extract, join and aggregate data into analysis-ready modelling tables, profile, clean and validate data in pandas, documenting quality issues and treatments, and apply exploratory analysis and inferential statistics to understand drivers and relationships. The full breakdown of topics is covered session by session in the Course Outline tab above.
The course is designed for analytically minded professionals who want to strengthen the data side of machine learning, including: Data analysts and business intelligence developers moving into data science, Junior and mid-level data scientists, Statisticians and economists in research institutions and national statistics offices, Monitoring, evaluation and research officers working with large data sets, Risk, credit and fraud analysts in banks, insurers and microfinance institutions, Actuaries and pricing analysts, Telecoms, retail and marketing analysts working on churn and customer models, Health information and epidemiology data officers, and Data engineers who want to understand modelling requirements.
Data and Analytics for 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.
Data and Analytics for 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 Data and Analytics for Data Science & Machine Learning in Practice starts November 9, 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 Data and Analytics for 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.
Data and Analytics for 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 Data and Analytics for 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 — Data and Analytics for 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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