Diploma in Dataset Generation and Realism Metrics
Online postgraduate diploma of 800 hours in dataset generation and realism metrics. 8 modules covering the dataset life cycle, capture and annotation, synthetic data generation, realism and fidelity metrics, data utility and privacy, curation and versioning, and datasets for critical systems, with an assigned technical tutor.
Diploma in Dataset Generation and Realism Metrics — studying from the United Kingdom
Data science, artificial intelligence and simulation are central to engineering practice across the United Kingdom, with strong hubs in London, Cambridge, Oxford, Bristol and Edinburgh. Universities, research institutes such as the Alan Turing Institute and industrial users in aerospace, automotive, energy and finance demand skills in modelling, machine learning and computational analysis. Engineering simulation tools are widely used for design validation and digital twins. Organisations seek professionals who can turn large datasets into engineering decisions while meeting data protection and governance requirements.
Fees for the United Kingdom: £1,250 (Pound sterling), with instalments and employer sponsorship available.
Relevant bodies in the United Kingdom
Typical roles
Questions from the United Kingdom
Which UK bodies recognise data and AI qualifications?
In the UK, the British Computer Society (BCS), the Royal Statistical Society (RSS) and the Institute of Analytics are relevant professional bodies, and the IET covers many engineering-related AI and data areas. SEIUM's qualifications are private and not accredited by these bodies. Check directly with BCS or RSS whether your study would be considered towards their membership or certification.
Do UK employers in data and AI value private online qualifications?
UK employers in finance, retail, health, technology and public sector analytics typically assess candidates on technical skills, portfolio work and experience with real data. A private qualification may help demonstrate specialist learning, but its value depends on each employer's judgement. Publishing projects and showing practical results will usually matter most.
Can I study this online from the UK alongside work, and what English level is required?
Yes, the programmes are fully online and can be studied from anywhere in the UK without a student visa. Teaching is in English, so you need a solid working level of technical English. An assigned technical tutor will support you. Check the programme hours against your working commitments and ask about instalment payments or employer sponsorship.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Specify quality requirements for AI datasets.
Organise capture and annotation processes with quality control.
Generate synthetic tabular, image and time-series data.
Measure the realism of generated data with suitable metrics.
Evaluate the utility and privacy risk of synthetic data.
Curate and version datasets while avoiding leakage between partitions.
Document datasets for critical and regulated systems.
Who it's for
- AI data engineers responsible for building training datasets.
- Data quality managers in AI-driven organisations.
- Synthetic data engineers who need rigorous realism metrics.
- Data scientists looking to strengthen their data-centric practice.
- Annotation coordinators managing labelling teams and suppliers.
- MLOps engineers in charge of dataset versioning and curation.
- Engineering or science graduates seeking to enter the automotive sector.
What you'll work with
- ISO/IEC 5259-2:2024 (data quality measures for analytics and machine learning)
- ISO/IEC 25012:2008 (data quality model)
- ISO/IEC 23053:2022 (framework for AI systems using machine learning)
- Regulation (EU) 2024/1689 on Artificial Intelligence (EU AI Act)
- Regulation (EU) 2016/679 (GDPR) (General Data Protection Regulation)
- ISO/PAS 8800:2024 (safety and artificial intelligence in road vehicles)
- ASAM OpenLABEL (annotation format for multi-sensor data and scenarios)
- CVAT
- Label Studio
- SDV (Synthetic Data Vault)
- DVC
- Cleanlab
8 modules
1.1Datasets in the AI life cycle1.2Data requirements by task1.3Real, synthetic and augmented data1.4Data quality according to ISO/IEC 250121.5The ISO/IEC 5259 series on data quality for analytics1.6Data governance and ownership1.7Documentation with datasheets1.8Legal aspects and licences1.9Sampling bias1.10Workshop: specifying the requirements of a dataset
2.1Planning data capture2.2Designing annotation guidelines2.3CVAT and Label Studio tools2.4COCO and ASAM OpenLABEL formats2.5Inter-annotator agreement2.6Semi-automatic annotation with pre-labelling2.7Label quality control2.8Managing external annotators2.9Annotation cost and lead times2.10Practical exercise: annotation guideline and agreement measurement
3.1Synthetic tabular data with generative models3.2Synthetic images through rendering3.3Diffusion models for images3.4GANs and variational autoencoders3.5Synthetic time series3.6Privacy-preserving synthetic data3.7Classical data augmentation3.8Balancing minority classes3.9Controlling generated diversity3.10Lab: tabular data with SDV
4.1Fréchet Inception Distance4.2Kernel Inception Distance4.3Precision and recall for generative models4.4Density and coverage metrics4.5Statistical distances per variable4.6Multivariate correlations and dependencies4.7Classifier-based two-sample tests4.8Human perceptual evaluation4.9Limitations of the metrics4.10Lab: computing FID and precision-recall
5.1Train on synthetic, test on real5.2Utility for downstream tasks5.3Membership inference attacks5.4Distance to closest record5.5Re-identification risk in synthetic data5.6Differential privacy in generators5.7The utility-privacy trade-off5.8GDPR requirements5.9Evaluation reports5.10Practical exercise: utility and privacy evaluation with SDMetrics
6.1Deduplication and cleaning6.2Detecting incorrect labels6.3Data selection with active learning6.4Splitting into training, validation and test sets6.5Data leakage between partitions6.6Versioning with DVC6.7Data catalogues6.8Drift monitoring6.9The data-centric improvement cycle6.10Case study: cleaning a visual inspection dataset
7.1Data requirements in ISO/PAS 88007.2Representativeness with respect to the ODD7.3Data sufficiency and argumentation7.4Traceability of data to requirements7.5Data governance under the AI Act7.6Independent test data7.7Dataset audit7.8Safety risks of synthetic data7.9Dataset change management7.10Case study: dataset for a pedestrian detection system
8.1Defining data requirements8.2Capture and annotation strategy8.3Complementary synthetic generation8.4Realism evaluation8.5Utility and privacy evaluation8.6Curation and versioning8.7Dataset documentation8.8Conclusions and proposed improvements8.9Report and presentation8.10Final project: final review with the tutor
How you'll learn
Training is 100% online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so the programme fits alongside your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is CVAT, Label Studio, SDV (Synthetic Data Vault), DVC and Cleanlab, in free, academic or evaluation versions. Case studies are based on real procedures: annotation campaigns with inter-annotator agreement measurement, generation of synthetic tabular and image data, and realism and privacy evaluation of generated data. Assessment is continuous by module, plus the final integrating project.
Standards covered: ISO/IEC 5259-2:2024, ISO/IEC 25012:2008, ISO/IEC 23053:2022, Regulation (EU) 2024/1689 on Artificial Intelligence, Regulation (EU) 2016/679 (GDPR), ISO/PAS 8800:2024, ASAM OpenLABEL
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. A university degree is not required.
- An internet connection and a computer capable of running the programme software.
- English sufficient to read technical standards.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with CVAT, Label Studio, SDV (Synthetic Data Vault), DVC and Cleanlab, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute diploma, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.
Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.
Prior technical training or professional experience in the sector (no university degree required), a computer able to run the programme software and English sufficient to read technical standards.
Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.
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