SEMMA Data Mining Methodology for Data Science Training Course

Course overview

Data science teams often have capable analysts and powerful modelling platforms but no repeatable path from a business question to a validated, deployable model. Projects stall when samples are biased, exploratory findings are not translated into usable features, transformations cannot be reproduced, or model selection is based on accuracy alone. This SEMMA Data Mining Methodology course gives practitioners a disciplined workflow for managing these decisions across the Sample, Explore, Modify, Model and Assess stages.

Participants apply SEMMA to a realistic structured-data case using SAS data-mining tools. They learn to define an analytical target, construct representative development and validation samples, profile data quality and distributions, identify influential variables, engineer and transform predictors, build competing models, and assess performance using business-relevant measures. Technical work includes handling missing values, outliers and class imbalance; applying partitioning strategies; comparing decision trees, regression and neural-network models; and interpreting lift, ROC, confusion-matrix and profit-based results.

The course is delivered through instructor-led demonstrations, guided platform exercises and team review sessions. Each participant builds an auditable SEMMA project: a documented analytical dataset, transformation and modelling workflow, model-comparison report, and deployment recommendation. The final day connects model results to operational use, including monitoring assumptions, handover requirements and the evidence required to justify a model choice to technical and business stakeholders.

It is designed for analysts, data scientists and technical managers who already work with data and need a practical, SAS-oriented methodology for predictive modelling. Managers benefit from staff who can make model-development work more consistent, reviewable and aligned to measurable business decisions.

Course objectives

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

• Define a SEMMA project charter with a business target, analytical population and success measures
• Construct representative training, validation and test samples using stratification and data partitioning
• Profile data quality, distributions, missingness and outliers using exploratory data-mining techniques
• Engineer reproducible predictor variables through imputation, binning, transformations and feature selection
• Build decision tree, regression and neural-network candidate models in SAS data-mining workflows
• Compare competing models using ROC curves, lift charts, confusion matrices and profit-based assessment
• Document model assumptions, data lineage and performance evidence in a SEMMA model report
• Produce a deployment and monitoring recommendation for an approved predictive model

Benefits of attending

Target competencies

Who should attend

• Data Scientists — who need a repeatable method for building and defending predictive models
• Data Analysts — who move from reporting and SQL analysis into structured data-mining projects
• SAS Programmers — who need to use SAS modelling platforms within a recognised analytical workflow
• Machine Learning Engineers — who need transparent sampling, feature preparation and model-validation practices
• Business Intelligence Managers — who oversee analytical teams and need consistent model governance evidence
• Analytics Product Owners — who must connect predictive-model outputs to measurable operational decisions

Requirements and prerequisites

Participants should be comfortable working with tabular data and understand basic descriptive statistics, including mean, median, distributions, correlation and data quality checks. Prior experience writing SQL, SAS code, Python or R is useful, as is familiarity with spreadsheet-style data preparation. Participants should understand the distinction between categorical and numeric variables and have encountered regression or classification concepts, although they do not need to have built production models. No prior SEMMA experience, advanced calculus, neural-network theory or SAS Enterprise Miner certification is required. A laptop able to access the supplied SAS environment is needed for practical work.

Training methodology

The five-day programme alternates short instructor-led explanations of each SEMMA phase with guided work in SAS data-mining tools. Participants work from a shared business case and dataset, making the same decisions required in a real predictive-modelling assignment: defining a target, partitioning records, investigating anomalies, preparing variables, running competing models and defending an assessment choice. Demonstrations are followed by individual build exercises and peer model-review discussions. On the final day, participants convert their technical results into a model report, deployment recommendation and practical application plan for their own workplace.

Course outline

Day 1: Framing the SEMMA analytical workflow

• SEMMA phases and their relationship to predictive-model delivery
• Business problem framing and measurable analytical objectives
• Target-variable definition for classification and regression
• Analytical population, unit of analysis and observation windows
• Data-source inventory and data-lineage requirements
• Training, validation and test partition design
• Stratified sampling and class-imbalance considerations

Workshop: Participants create a SEMMA project charter and build a partitioned development sample for a customer-response case.

Day 2: Exploring data and diagnosing quality

• Univariate profiling of numeric and categorical variables
• Missing-value patterns and data-completeness measures
• Outlier detection using distribution plots and summary statistics
• Target association, correlation and variable screening
• Segment analysis and cross-tabulation by outcome class
• Data leakage detection and time-based validation risks
• Exploratory visualisation in SAS data-mining environments

Workshop: Participants produce an exploration notebook identifying data-quality issues, candidate predictors and leakage risks.

Day 3: Modifying data for model readiness

• Missing-value imputation strategies for numeric and categorical fields
• Outlier treatment, capping and robust transformation choices
• Binning and grouping continuous predictors
• Log, square-root and standardisation transformations
• Dummy-variable creation and categorical encoding
• Derived features, ratios and interaction terms
• Feature selection and multicollinearity management

Workshop: Participants create a reproducible modelling table with imputation rules, transformed fields and a feature-selection rationale.

Day 4: Building and comparing predictive models

• Baseline models and benchmark-performance expectations
• Logistic and linear regression model construction
• Decision tree growth, pruning and split criteria
• Neural-network model configuration and overfitting controls
• Ensemble and model-comparison workflows
• Hyperparameter tuning using validation data
• Model interpretability through variable importance and partial dependence

Workshop: Participants build three candidate models and submit a comparison table covering settings, variables and validation results.

Day 5: Assessing, deploying and governing models

• Confusion matrices, sensitivity, specificity and precision
• ROC curves, AUC and cumulative lift charts
• Profit matrices, cut-off selection and business decision thresholds
• Test-set confirmation and generalisation evidence
• Model documentation, reproducibility and approval packs
• PMML export and operational handover requirements
• Model monitoring for drift, performance decay and retraining triggers

Workshop: Participants present a final SEMMA model report with an assessment decision, deployment pathway and 90-day 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