SEMMA Methodology for Analytical Model Development Training Course

Course overview

Analytical teams often have plenty of data and modelling tools but lack a disciplined route from a raw population to a model that can be trusted, compared and operationalised. SEMMA—Sample, Explore, Modify, Model and Assess—provides that route. This course helps analysts replace ad hoc model building with a repeatable process for selecting representative data, diagnosing quality and distribution issues, engineering predictive inputs, testing competing models and documenting performance decisions. It is designed for professionals who need to justify why a model was chosen, not merely produce an accuracy score.

Over five days, participants apply each SEMMA stage to a realistic predictive analytics case. They use SAS Enterprise Miner and SAS Viya Model Studio concepts to construct data-mining process flows; apply sampling, partitioning and metadata controls; explore distributions, missing values and relationships; transform, impute and select variables; and develop regression, decision tree and neural network models. The course also covers lift, ROC, misclassification, profit matrices, model comparison and score-code generation. Participants learn where SEMMA is strongest, how it differs from CRISP-DM, and how to integrate business objectives, governance and deployment requirements into a SEMMA-led workflow.

Delivery combines instructor demonstrations with guided lab work in a hosted analytics environment. Each day builds part of an end-to-end model pipeline, using a structured case dataset with documented business rules and model success measures. Participants leave with a completed SEMMA project pack: a process-flow design, sampling and data-preparation decisions, model comparison evidence, an assessment recommendation and an implementation handover template suitable for discussion with technical and business stakeholders.

The course is best suited to analysts and data professionals who already work with structured data and want a practical, SAS-centred methodology for developing defensible predictive models.

Course objectives

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

• Design a SEMMA process flow that links business objectives, data sources, modelling tasks and assessment criteria
• Apply representative sampling and train-validation-test partitioning methods to control model development bias
• Profile distributions, missing values, outliers and variable relationships using exploratory data analysis outputs
• Construct repeatable data-preparation steps for imputation, transformation, binning and metadata assignment
• Engineer and select predictive variables using correlation screening, variable importance and reduction methods
• Build and tune regression, decision tree and neural network models in a visual data-mining workflow
• Assess competing models using ROC curves, lift charts, confusion matrices, fit statistics and profit measures
• Produce a model recommendation pack containing score code, validation evidence, assumptions and deployment actions

Benefits of attending

Target competencies

Who should attend

• Data Analysts — who need a structured method for turning operational data into validated predictive models
• Data Scientists — who need to standardise model-development workflows and communicate model choices clearly
• Business Intelligence Analysts — who are moving from descriptive reporting into predictive analytics
• SAS Programmers — who need to use SAS Enterprise Miner or Viya visual workflows alongside SAS coding skills
• Analytics Managers — who must review model quality, comparability and readiness for operational use
• Risk and Fraud Analysts — who build classification models where false-positive and false-negative costs matter

Requirements and prerequisites

Participants should be comfortable working with tabular data and should understand basic statistical concepts, including variables, distributions, averages, correlation and the purpose of a predictive model. Experience using SAS, SQL, Excel, Python or another analytics tool to inspect and prepare data is helpful; prior use of SAS Enterprise Miner is not required. Participants should also be able to interpret simple model outputs such as regression coefficients or classification accuracy. Advanced mathematics, programming expertise, prior neural-network experience and prior knowledge of CRISP-DM are not required. The course supplies guided lab instructions and a hosted SAS environment.

Training methodology

The course is delivered through short instructor-led technical briefings followed by guided SAS-based labs. Participants work through a single predictive modelling case, progressing from a raw customer dataset to an assessed model recommendation. Demonstrations show how SEMMA activities are represented in SAS Enterprise Miner and SAS Viya Model Studio process flows, while exercises require participants to interpret diagnostics, configure transformations and compare results. Small-group review sessions challenge modelling choices against business costs and data risks. The final session includes an application-planning workshop in which participants adapt the project pack to a live workplace use case.

Course outline

Day 1: SEMMA foundations and analytical project framing

• SEMMA stages: Sample, Explore, Modify, Model and Assess
• SEMMA compared with CRISP-DM and model lifecycle governance
• Business problem statements, target definitions and success criteria
• Predictive versus descriptive analytics use cases
• SAS Enterprise Miner project structure and process-flow nodes
• SAS Viya Model Studio pipeline concepts
• Data roles, metadata and analytical dataset requirements

Workshop: Participants translate a customer-retention business brief into a SEMMA project charter, target definition, success metric and initial process-flow design.

Day 2: Sample and Explore: preparing a trustworthy modelling population

• Population definition and sampling-frame risks
• Simple random, stratified and oversampling techniques
• Training, validation and test partition design
• Class imbalance and rare-event sampling decisions
• Univariate distribution profiling and summary statistics
• Missing-value, outlier and data-quality diagnostics
• Association analysis using correlation, contingency tables and segment profiles

Workshop: Participants create a partitioned modelling sample and produce an exploratory data-quality report identifying imbalance, missingness and influential variables.

Day 3: Modify: transforming data into predictive inputs

• Measurement levels and metadata role assignment
• Missing-value imputation strategies for numeric and categorical fields
• Outlier treatment, capping and robust transformations
• Binning, grouping and weight-of-evidence style transformations
• Date, tenure and behavioural feature derivation
• Variable screening using correlation and redundancy analysis
• Data leakage detection and transformation reproducibility

Workshop: Participants build a documented modification branch that imputes, transforms and selects variables while recording leakage controls and business rationale.

Day 4: Model: building and comparing predictive models

• Baseline models and benchmark performance thresholds
• Logistic regression for binary classification
• Decision tree splitting, pruning and interpretability
• Neural network architecture and tuning considerations
• Variable importance and sensitivity interpretation
• Hyperparameter search using validation data
• Champion-challenger model comparison workflows

Workshop: Participants develop regression, decision tree and neural network challengers, then select provisional champion models using validation results.

Day 5: Assess: selecting, documenting and handing over a model

• Confusion matrices, sensitivity, specificity and precision
• ROC curves, AUC and cumulative lift charts
• Profit matrices and decision-threshold selection
• Overfitting diagnosis using train-validation-test comparisons
• Residual, error and segment-level performance analysis
• Score code generation and PMML model interchange
• Model documentation, implementation handover and monitoring triggers

Workshop: Participants complete a model assessment workshop and produce a champion-model recommendation, score-code handover and initial monitoring plan.

Tools & standards covered

SAS Enterprise Miner, SAS Viya Model Studio, SAS Studio, Predictive Model Markup Language (PMML)

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