Select the generation method suited to tabular, text, image or time-series data requirements.
Digital Transformation & Artificial Intelligence
Generating and Validating Synthetic Data for Machine Learning Models
Design, generate and statistically validate synthetic datasets for machine learning, covering generative methods, fidelity metrics, privacy risk testing and governance sign-off.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
Machine learning teams frequently need more data than they can ethically or practically collect: rare fraud cases, sensitive health records, dangerous failure scenarios, or simply not enough labelled examples. This programme shows how to generate synthetic data that is statistically faithful to real records without exposing the individuals or systems behind them, and how to prove that fidelity before anyone trusts the result. Statistical simulation, copula-based methods, generative adversarial networks and diffusion-based generators are each applied to tabular, text, image and time-series data, followed by the validation techniques that separate usable synthetic data from data that looks plausible but misleads downstream models: distributional fidelity tests, train-on-synthetic-test-on-real evaluation, membership inference testing and differential privacy calibration. Worked datasets, generation exercises and structured expert review run throughout, so that by the final module a synthetic dataset built in class has already been fidelity-tested, privacy-scored and documented for a governance sign-off.
Expected Learning Outcomes
Build synthetic datasets that augment rare events and correct class imbalance without duplicating source records.
Apply statistical fidelity tests to confirm synthetic data preserves marginal and joint distributions.
Evaluate downstream model utility using train-on-synthetic-test-on-real protocols.
Quantify re-identification and membership inference risk before sharing synthetic datasets externally.
Calibrate differential privacy budgets to balance data utility against disclosure risk.
Document provenance, limitations and governance sign-off for synthetic datasets entering production.
Who Should Attend
Data scientists building training datasets for machine learning models with limited or sensitive source data.
Machine learning engineers responsible for data pipelines feeding production models.
Data governance and privacy officers assessing synthetic data before external release.
Analytics teams in banking, insurance or healthcare working with regulated personal data.
Software test engineers generating synthetic datasets for QA and staging environments.
AI product managers deciding when synthetic data can substitute for scarce real-world data.
Course Modules
Select any module to see its sessions and points.
01Generation Methods for Structured and Tabular Data
2 sessions · 8 points
Session 1Statistical and Rule-Based Simulation Techniques
- Building bootstrapped resamples and copula-based multivariate simulations that preserve realistic correlations between variables.
- Designing agent-based simulations to generate synthetic transaction or population data with realistic behavioural rules.
- Writing rule-based scenario generators to produce edge cases that real data rarely contains in sufficient volume.
- Comparing simulation outputs against real reference distributions before accepting them as a usable baseline dataset.
Session 2Deep Generative Models for Tabular and Sequential Data
- Training generative adversarial network variants such as CTGAN and CopulaGAN on structured tabular data.
- Applying variational autoencoders to learn a compressed representation that generates new, realistic records.
- Adapting diffusion-based generators, originally built for images, to structured and sequential business data.
- Generating synthetic time series and event logs that preserve temporal patterns such as seasonality and trend.
02Extending Synthesis to Unstructured and Rare-Event Data
2 sessions · 8 points
Session 1Text, Image and Sensor Data Synthesis
- Using large language models with structured prompt templates to generate labelled text examples for rare categories.
- Generating synthetic training images with diffusion models to cover object classes under-represented in real footage.
- Simulating sensor and IoT time-series readings to test analytics pipelines before real deployment data exists.
- Weighing the added complexity of synthetic audio and speech data against the availability of real recorded alternatives.
Session 2Augmenting Rare Events and Correcting Class Imbalance
- Applying SMOTE and its variants to oversample rare classes without simply duplicating existing minority records.
- Generating safety-critical edge cases for autonomous systems that would be too dangerous or rare to collect from the real world.
- Producing adversarial examples that stress-test a model's robustness to inputs near its decision boundary.
- Building fraud scenario variants that stretch a detection model's coverage beyond historically observed patterns.
03Validating Fidelity, Utility and Privacy
2 sessions · 8 points
Session 1Statistical Fidelity and Utility Testing
- Running Kolmogorov-Smirnov and chi-square tests to compare synthetic and real marginal distributions column by column.
- Comparing correlation matrices and joint distributions to confirm relationships between variables have been preserved.
- Calculating the propensity score mean-squared error to measure how easily a classifier tells synthetic from real records apart.
- Applying the train-on-synthetic-test-on-real protocol to check whether a model trained on synthetic data performs on real holdout data.
Session 2Privacy Risk Assessment and Disclosure Control
- Running membership inference attacks to test whether an adversary could tell if a specific record was used to generate the data.
- Checking quasi-identifiers against k-anonymity and l-diversity thresholds before considering external release.
- Calibrating a differential privacy budget, or epsilon value, to balance statistical utility against disclosure risk.
- Scoring re-identification risk formally before sharing a synthetic dataset outside the organisation that created it.
04Expert Review, Governance and Production Integration
2 sessions · 8 points
Session 1Bias, Fairness and Domain Validation with Subject Experts
- Comparing subgroup distributions between synthetic and real data to check that fairness properties have not been distorted.
- Running structured expert review workshops where domain specialists sign off on the plausibility of generated records.
- Documenting known limitations and failure modes discovered during expert review for future users of the dataset.
- Feeding expert feedback back into generation parameters through an iterative loop between data science and domain teams.
Session 2Documentation, Governance and Pipeline Integration
- Producing a datasheet that documents the generation method, parameters and intended use of each synthetic dataset.
- Versioning and tracking the provenance of synthetic datasets as they move through the data pipeline.
- Routing regulated-sector synthetic datasets through a formal approval and risk sign-off workflow before use.
- Monitoring drift between synthetic training data and live production data as source systems evolve over time.
What the participant receives
4 course modules
A structured syllabus
8 training sessions
across 5 days
32 detailed points
Applied, detailed content
Accredited attendance certificate
On completing the programme
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