Advanced Data Analytics and Predictive Modelling Training Course

Course overview

Organisations collect transactional, operational, customer and digital data, yet many analysis teams still spend too much time producing retrospective dashboards and too little time estimating what will happen next. This course addresses the practical gap between descriptive reporting and decision-ready predictive analysis: selecting a business question, preparing defensible data, building models, testing their reliability and communicating recommendations that operational teams can act on. It is designed for professionals who need to move beyond spreadsheet analysis or basic Python notebooks without treating machine learning as a black box.

Participants work through an end-to-end predictive modelling workflow using Python, Jupyter Notebook, pandas and scikit-learn. They profile and clean data, engineer meaningful features, select suitable regression and classification approaches, handle imbalanced classes, validate models with cross-validation and assess performance using metrics such as RMSE, precision, recall, F1 score, ROC-AUC and lift. The course also covers model interpretation, bias checks, data leakage controls, reproducible analysis and the practical choices involved in deploying model outputs into business processes.

Teaching combines instructor-led technical demonstrations with guided notebook labs and a realistic business case. Participants build a predictive model from a raw dataset, document assumptions and validation decisions, and present a recommendation for how the model should be used, monitored and governed. They leave with an annotated Jupyter Notebook, a model evaluation report, a feature and data-quality log, and an action plan for applying the workflow to a live organisational use case.

The course is best suited to analysts, data professionals and technically minded managers who already work with data and need a rigorous, business-focused route into applied predictive modelling.

Course objectives

By the end of this course, participants will be able to:

• Frame a predictive analytics problem as a measurable business objective, target variable and decision rule
• Profile, cleanse and join analytical datasets using pandas DataFrames
• Engineer, encode and scale features while controlling for missing data and data leakage
• Build and tune regression and classification models using scikit-learn pipelines
• Evaluate model performance with cross-validation, confusion matrices, ROC-AUC, lift and error metrics
• Diagnose overfitting, class imbalance, multicollinearity and unstable model behaviour
• Interpret model outputs using feature importance, partial dependence and scenario-based explanations
• Produce a reproducible predictive modelling notebook and model evaluation report for stakeholders

Benefits of attending

Target competencies

Who should attend

• Data Analysts — who need to progress from descriptive reporting to validated predictive models
• Business Intelligence Analysts — who need to add forecasting and classification evidence to dashboard-led decision support
• Data Scientists — who require a structured, business-grounded approach to model validation and interpretation
• Analytics Managers — who oversee analytical delivery and must assess whether models are fit for operational use
• Digital Product Analysts — who need to predict churn, conversion, demand or customer behaviour from product data
• Risk and Operations Analysts — who need defensible scoring models for prioritisation, intervention or resource allocation

Requirements and prerequisites

Participants should be comfortable working with structured data and interpreting common business metrics. Prior experience writing basic Python is required, including variables, functions, lists, conditional logic and reading CSV files; familiarity with pandas DataFrames and Jupyter Notebook is strongly recommended. Participants should also understand descriptive statistics, correlation, distributions and the distinction between a target variable and input variables. Bring a laptop able to run a current Python environment or cloud notebook. Prior machine learning experience, advanced calculus, formal statistical proofs and production software engineering experience are not required.

Training methodology

The five days alternate short instructor-led explanations with progressively more demanding Jupyter Notebook labs. Participants inspect a realistic customer or operations dataset, formulate a prediction question, prepare features, build competing models and compare their results using appropriate validation methods. Instructor demonstrations show the rationale behind each Python and scikit-learn workflow before participants apply it in pairs or individually. Group review sessions focus on model assumptions, business costs and stakeholder communication. The final workshop requires each participant to document and present a deployable modelling recommendation for their chosen case.

Course outline

Day 1: Framing predictive analytics and preparing data

• Translating business decisions into prediction targets and success criteria
• Analytical unit of analysis and time-window design
• Data profiling with pandas DataFrames
• Missing-value patterns and treatment strategies
• Outlier detection using distributions and robust statistics
• Data joins, duplicate records and entity-resolution checks
• Training, validation and test-set design

Workshop: Participants profile a raw customer dataset and produce a data-quality log, target definition and initial modelling dataset.

Day 2: Feature engineering and baseline models

• Feature engineering from dates, categories and transactional history
• Categorical encoding and numerical feature scaling
• Feature selection using business logic and statistical evidence
• Data leakage detection in historical datasets
• scikit-learn Pipeline and ColumnTransformer construction
• Linear regression and logistic regression baselines
• Regularisation with Ridge and Lasso models

Workshop: Participants build a reusable preprocessing pipeline and a baseline regression or classification model with documented feature choices.

Day 3: Classification, forecasting and model tuning

• Decision trees and random forest modelling
• Gradient boosting model principles
• Hyperparameter tuning with GridSearchCV and RandomizedSearchCV
• K-fold and time-series cross-validation
• Class imbalance treatment with weighting and resampling
• Probability calibration and decision thresholds
• Regression forecasting metrics including MAE, RMSE and MAPE

Workshop: Participants train and tune competing models, then produce a comparison table showing validation results and selected parameters.

Day 4: Evaluation, interpretation and model risk

• Confusion matrices, precision, recall and F1 score
• ROC curves, precision-recall curves and ROC-AUC
• Lift charts and gains analysis for prioritisation decisions
• Residual analysis and error segmentation
• Feature importance and permutation importance
• Partial dependence plots and scenario-based explanations
• Bias, fairness and model governance checks

Workshop: Participants complete a model review pack containing performance charts, error analysis, interpretation findings and model-risk controls.

Day 5: Operationalising predictive insight

• Selecting a model against business cost and benefit criteria
• Model documentation using assumptions, limitations and lineage
• Reproducible notebooks and version-controlled analytical workflows
• Batch scoring and integrating predictions into business processes
• Data drift, performance drift and monitoring thresholds
• Communicating uncertainty to executive stakeholders
• Predictive analytics application roadmap and governance ownership

Workshop: Participants present an end-to-end predictive modelling recommendation and produce an application plan for a workplace use case.

Tools & standards covered

Python, Jupyter Notebook, pandas, scikit-learn

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions