Module 1
Prediction Problems, Data and Baselines
- Business decision and prediction target
- Forecast horizon and unit of analysis
- Training, validation and test data
- Naive and baseline predictions
Predictive Analytics Flagship
Forecasting and Predictive Analytics
Use forecasting and predictive methods to estimate future outcomes while recognising model assumptions, uncertainty and limitations.
Course overview
Develop predictive insight without confusing a model with certainty.
Forecasting and Predictive Analytics develops practical capability to frame prediction problems, prepare modelling data, build and compare predictive approaches and interpret performance responsibly.
Participants work with regression, time-series forecasting, classification concepts and model-evaluation measures, then connect predictions with scenarios, uncertainty and decision thresholds.
The course emphasises validation, out-of-sample performance and business usefulness rather than selecting a model solely because it fits historical data.
Learning outcomes
What participants will be able to do.
- Frame forecasting and prediction problems from business decisions.
- Prepare time-based and predictive modelling data appropriately.
- Build and interpret regression models at a practical level.
- Apply time-series concepts including trend, seasonality and forecast horizons.
- Explain classification and probability-based prediction.
- Evaluate models using holdout data and suitable performance measures.
- Identify overfitting, data leakage and model limitations.
- Communicate forecasts, uncertainty and decision implications clearly.
Who should attend
Designed for professionals who use data, analysis or evidence to improve decisions.
- Business and data analysts.
- Demand, planning and supply-chain professionals.
- Finance and risk analysts.
- Marketing and customer analytics teams.
- Managers who commission or use predictive models.
Prerequisites
Professionally relevant and application-focused.
Participants should have basic data-literacy and statistical-reasoning capability. Familiarity with spreadsheets or analytics tools is helpful.
Course structure
A five-Module journey from analytical understanding to workplace application.
The sequence may be delivered across five days or adapted to another approved format while preserving the learning outcomes and the five connected Modules.
Module 2
Regression for Predictive Analysis
- Simple and multiple regression concepts
- Features, coefficients and practical interpretation
- Residuals and model assumptions
- Prediction versus causal explanation
Module 3
Time-Series Forecasting
- Trend, seasonality and time dependence
- Moving averages and smoothing concepts
- Forecast windows and rolling evaluation
- Events, structural change and judgemental adjustment
Module 4
Classification and Model Evaluation
- Classification and probability scores
- Confusion matrix, precision and recall
- Thresholds and business costs of error
- Overfitting, leakage and generalisation
Module 5
Uncertainty, Scenarios and Decision Integration
- Prediction intervals and uncertainty
- Scenario and sensitivity analysis
- Model monitoring and change
- Integrated forecasting case and management recommendation
Learning experience
Move from understanding to application, production and workplace value.
Understand
Connect concepts with business decisions
Clarify methods, assumptions, evidence requirements and the decision context before applying tools.
Apply
Work through realistic analytical cases
Use datasets, scenarios and decision questions to practise analytical judgement in context.
Produce
Create practical analytical outputs
Develop artefacts that can be adapted to reporting, modelling, governance or decision-support work.
Review
Challenge evidence and analytical choices
Use peer review, validation criteria and facilitated critique to improve analytical reasoning.
Transfer
Apply the learning at work
Identify how to adapt the methods to organisational data, decisions, systems and governance requirements.
Completion requirements
Demonstrate participation, application and professional judgement.
- Participate actively in case discussions, data exercises and analytical workshops.
- Complete the principal analytical or decision-support outputs assigned during the programme.
- Contribute to the integrated case, model, dashboard or application workshop.
- Complete knowledge checks and a workplace application or study plan.
Participant outputs
Leave with practical analytical artefacts.
- Forecasting problem definition.
- Baseline forecast and evaluation sheet.
- Regression interpretation summary.
- Time-series forecast comparison.
- Classification threshold worksheet.
- Predictive insight and decision brief.
Delivery options
Select the learning format that fits your people and analytical environment.
Instructor-led
Live Classroom
Facilitated face-to-face learning with analytical cases, datasets, modelling tasks, discussion and immediate feedback.
Instructor-led
Live Virtual Classroom
Interactive online delivery using collaborative workspaces, data exercises, breakout analysis and guided model development.
Flexible
Blended Learning
A structured combination of preparation, live facilitation, applied assignments, analytical work and follow-up application.
Organisation-specific
Corporate and In-Company
Tailored delivery aligned with organisational datasets, measures, tools, governance, decisions and analytics maturity where appropriate.
Frequently asked questions
Course information and participation.
Do I need advanced statistics or machine learning experience?
No advanced experience is required, but participants should be comfortable with basic statistics and data interpretation.
Which software is used?
The course is method-led. Exercises may use spreadsheets, statistical software, Python/R or BI tools according to the delivery environment.
Does the course cover machine learning?
It introduces predictive modelling and classification concepts relevant to business analytics without becoming a software-engineering or deep-learning course.
Will we discuss forecast uncertainty?
Yes. Uncertainty, prediction intervals, model error and the consequences of acting on forecasts are central themes.
Does completion provide professional certification?
The course develops forecasting and predictive-analytics capability and may provide an INDENTRA course-completion record where applicable. It is not a third-party certification.
Take the next step
Build forecasts that support decisions without hiding uncertainty.
Discuss a predictive-analytics programme aligned with your organisation’s demand, planning, risk or performance questions.