Course Objective & Scope
Dashboards show what happened; explaining why and anticipating what comes next calls for machine learning tools. This course teaches analysts to apply ML correctly to analytical questions such as customer segmentation, identifying sales or churn drivers, flagging outlier records and short-term forecasting.
The focus is interpretable results rather than model engineering: SHAP explains variable effects and findings are turned into an analysis memo ready for management. The programme runs in-house on-site, live online or hybrid, adapted to the organisation's datasets.
Who Should Attend?
- Data and business analysts
- BI, reporting and performance management teams
- Marketing, finance and operations analytics specialists
- Prerequisite: analysis experience with SQL or Excel; basic Python recommended
Key Takeaways
- Pick the right ML method for an analytical question
- Build interpretable segments with k-means and hierarchical clustering
- Identify performance drivers with tree-based models and SHAP
- Flag outlier records with Isolation Forest
- Present findings in an analysis memo written in business language
Curriculum
From Analytical Question to ML Method
- Descriptive, diagnostic and predictive analysis
- Analysis environment with Python and pandas
Segmentation and Profiling
- k-means, hierarchical clustering, effect of scaling
- Translating segments into business language
Driver Analysis and Explanation
- Importance analysis with random forest and gradient boosting
- Interpreting variable effects with SHAP plots
Outliers and Simple Forecasting
- Flagging unusual records with Isolation Forest
- Short-term trend forecasts with uncertainty intervals
Analysis Memo and Presentation
- Visualising findings and stating their limits
- Preparing an analysis memo on the organisation's case
Other Courses in This Category
-
Categorical encoding, time and window-based features, target encoding, interaction and text features, feature selection and feature stores: a systematic approach to the step that most improves model performance on tabular data.
- Live Course
- 5 modules
-
Choosing the right metrics, evaluation tied to business KPIs, error analysis, fairness checks and model explanation with SHAP, LIME and PDP, so models can be presented to stakeholders reliably and understandably.
- Live Course
- 5 modules
-
Independent model validation: data and conceptual soundness checks, backtesting, stability (PSI), challenger models, stress tests and findings reporting, producing a validation report aligned with a model risk management framework.
- Live Course
- 5 modules
-
Time series forecasting with ARIMA, ETS, Prophet and LightGBM-based approaches, working hands-on with seasonality, exogenous variables, hierarchical series, backtesting and prediction intervals to build reliable forecasts.
- Live Course
- 5 modules
-
Detecting unusual behaviour in unlabelled data with statistical methods, Isolation Forest, LOF, autoencoders and time series anomaly techniques, including threshold setting, alert fatigue and evaluation strategies.
- Live Course
- 5 modules
-
Applied Machine Learning
End-to-end machine learning with Scikit-learn, XGBoost, LightGBM, and CatBoost, covering feature engineering, Optuna hyperparameter tuning, imbalanced data, and SHAP/LIME explainability.
- Live Course
- 3 modules
Get in touch
