Data and Analytics for Data Science & Machine Learning in Practice Training Course in Kigali, Rwanda
Classroom training in Kigali · next cohort Oct 26, 2026 · from USD 3,800
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.
At a Glance
Training in Kigali, Rwanda
Kigali is Rwanda's clean, safe and well-connected capital, and one of Africa's leading conference cities.
More about training in Kigali →- Format
- Classroom, in Kigali
- Duration
- Mon - Fri (10 Days)
- Fee
- USD 3,800
- Next start dates
- Oct 26, 2026, Nov 23, 2026, Dec 21, 2026
- Certificate
- Certificate of Completion
- Airport
- Kigali International (KGL), about 10 km from the city centre
- Time zone
- Central Africa Time (UTC+2)
- Currency
- Rwandan franc (RWF). Fees are listed in USD.
- Business districts
- Kigali CBD, Kimihurura and Kacyiru
About this course
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.
Training Objectives
Participants who complete the programme will 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.
- Apply exploratory analysis and inferential statistics to understand drivers and relationships.
- Engineer features for tabular, categorical, text and time series data while avoiding leakage.
- Evaluate models with appropriate metrics, cross-validation and cost-sensitive threshold selection.
- Design and analyse A/B tests and other experiments to measure real-world impact.
- Communicate model evidence, limitations and recommendations to non-technical decision-makers.
What you'll learn
Key Takeaways
Participants complete the programme with:
- A library of reusable SQL and pandas notebooks for extraction, cleaning and profiling.
- A data quality checklist and validation script to run before any modelling project.
- An exploratory analysis report template with standard charts and statistical tests.
- A feature engineering playbook with leakage checks for common data types.
- A model evaluation framework covering metrics, calibration and threshold choice.
- An A/B test design and analysis template with sample size calculations.
- A completed capstone project taken from raw data to a presented recommendation.
Training Methodology
The course is taught as a sequence of guided labs, with each concept applied in code the same day. Participants work through:
- Jupyter notebooks prepared for every session, with starter code and worked solutions
- Realistic, anonymised data sets from finance, telecoms, health and public services
- Pair programming and code review to build good analytical habits
- Short statistical clinics that connect theory to the lab results
- Reproducibility practice with version-controlled notebooks and experiment tracking
- A capstone project presented to peers and facilitators
Day 1: Framing Analytics and Machine Learning Problems
- From business question to analytical question
- Descriptive, diagnostic, predictive and prescriptive analytics
- The CRISP-DM lifecycle and where projects go wrong
- Defining targets, success metrics and baselines
- Setting up Python, Jupyter and the core libraries
- Ethics, privacy and data access permissions
- Lab: scoping a customer churn prediction problem
Day 2: Data Acquisition with SQL
- Relational data models and reading an entity relationship diagram
- SELECT, filtering, joins and aggregation
- Window functions for running totals, ranks and lags
- Building one-row-per-entity modelling tables
- Point-in-time correctness and avoiding future data
- Connecting Python to databases and loading query results into pandas
- Lab: assembling a customer modelling table
Day 3: Data Cleaning and Quality Assurance
- Profiling data with pandas: types, ranges and distributions
- Missing data patterns and imputation choices
- Outliers, duplicates and inconsistent categories
- Validation rules and automated checks with Great Expectations or pandera
- Documenting cleaning decisions for reproducibility
- Reconciling extracted data to source totals
- Lab: cleaning and validating a messy transactions data set
Day 4: Exploratory and Statistical Analysis
- Univariate and bivariate exploration with matplotlib and seaborn
- Segmentation and cohort analysis
- Sampling, confidence intervals and hypothesis testing
- Correlation, association and the limits of both
- Linear and logistic regression for explanation
- Regression diagnostics and multicollinearity
- Lab: identifying the drivers of loan default
Day 5: Baseline Models and Evaluation
- Train, validation and test splits
- Building baseline models with scikit-learn
- Classification metrics: precision, recall, F1, ROC AUC and precision-recall curves
- Regression metrics: MAE, RMSE and MAPE
- Cross-validation and avoiding overfitting
- Confusion matrices and cost-based threshold choice
- Lab: comparing baseline classifiers
Day 6: Advanced Feature Engineering
- Encoding high-cardinality categorical variables
- Aggregation and recency, frequency and monetary features
- Text features with TF-IDF and simple embeddings
- Date, time and lag features
- Feature selection and importance screening
- Detecting and preventing target leakage
- Lab: building a feature pipeline with scikit-learn transformers
Day 7: Imbalanced Data, Time Series and Forecasting
- Class imbalance: resampling, class weights and suitable metrics
- Probability calibration
- Time series decomposition, seasonality and stationarity
- Forecasting with ARIMA, exponential smoothing and gradient boosting
- Time-based cross-validation and backtesting
- Measuring forecast accuracy and bias
- Lab: forecasting monthly demand or transaction volumes
Day 8: Model Diagnostics, Interpretability and Fairness
- Error analysis by segment
- Global and local interpretation with permutation importance and SHAP
- Partial dependence and individual conditional expectation plots
- Checking fairness across groups
- Stability and sensitivity testing
- Writing a model card
- Lab: diagnosing and explaining a credit scoring model
Day 9: Experiments, Impact Measurement and MLOps Analytics
- Designing A/B tests: hypotheses, randomisation and sample size
- Analysing experiment results and avoiding common pitfalls
- Uplift and quasi-experimental methods such as difference-in-differences
- Experiment tracking and model registry with MLflow
- Monitoring data drift and model performance after deployment
- Dashboards for model results in Power BI
- Lab: analysing a pricing or campaign experiment
Day 10: Capstone Project
- Capstone brief and data set selection
- Data extraction, quality checks and exploration
- Feature engineering and model building
- Evaluation, interpretation and impact estimate
- Preparing a decision-ready presentation
- Presentations and peer review
- Course review and learning plan
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
- Data engineers who want to understand modelling requirements
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.
Choose Where You Want to Learn
Data and Analytics for Data Science & Machine Learning in Practice Training Course Venues
RwandaKigaliNext Oct 26· 13 dates$3,800Viewing
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
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
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Frequently Asked Questions About Data and Analytics for Data Science & Machine Learning in Practice Training Course in Kigali
Everything you need to know about this course before you register.
Data and Analytics for Data Science & Machine Learning in Practice Training Course runs as classroom training in Kigali, Rwanda, in Monday-to-Friday cohorts that start every four weeks. The venue, daily timings and joining details are sent in your Joining Instructions email ahead of the start date.
The next Kigali cohort starts on October 26, 2026, and a new intake follows every four weeks. Pick a date under "Upcoming sessions in Kigali" above and click Register; your seat is confirmed once payment or a signed purchase order is received.
The classroom fee for Data and Analytics for Data Science & Machine Learning in Practice Training Course in Kigali is USD 3,800 per delegate, covering tuition and course materials. Register 3 or more delegates together and a 5% team discount is applied automatically.
Kigali is the only classroom city for Data and Analytics for Data Science & Machine Learning in Practice Training Course in Rwanda at the moment. You can also join a live online cohort from anywhere in Rwanda, or book it in-house at your organisation's own premises.
Yes — Data and Analytics for Data Science & Machine Learning in Practice Training Course can be delivered at your offices in Kigali or at a venue you choose, on dates that suit your team, with case studies tailored to your sector. Message our training advisors to request a proposal.
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.
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.
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