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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.

Duration800 hours · 6–8 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
Enrol now View syllabus Ask an advisor

Diploma in Dataset Generation and Realism Metrics — studying from India

India's data and analytics capability is among the largest in the world, with major centres in Bengaluru, Hyderabad, Pune, Chennai and Gurugram. Global technology firms and domestic IT companies such as Tata Consultancy Services, Infosys, Wipro and HCLTech run large engineering and analytics operations. Engineering organisations increasingly apply AI, simulation and digital twin techniques to product design and manufacturing. Data protection rules are shaped by the Ministry of Electronics and Information Technology, while the National Association of Software and Service Companies represents the sector. Engineers here build models, analytics pipelines and simulation workflows across industries.

Fees for India: ₹159,000 (Indian Rupee), with instalments and employer sponsorship available.

Relevant bodies in India

  • Ministry of Electronics and Information Technology (MeitY)
  • National Association of Software and Service Companies (NASSCOM)
  • Bureau of Indian Standards (BIS)
  • Indian Statistical Institute

Typical roles

  • Data Scientist
  • Machine Learning Engineer
  • Simulation Engineer
  • Data Analytics Engineer

Questions from India

Are there professional bodies in India that recognise data and AI engineering?

India does not have a statutory licence for data scientists or AI engineers. Relevant professional bodies include the Computer Society of India (CSI), the Institution of Engineers (India) and IEEE India sections. SEIUM's qualifications are private, not state degrees and not accredited by a national body, so their value depends on how employers view them.

Which employers in India hire data and AI professionals?

Demand is strong in IT services and consulting firms, banks and fintech companies, e-commerce and healthcare organisations, and research labs, especially in Bengaluru, Hyderabad, Pune and the NCR. A private online programme can support a career change or upskilling, but employers mainly assess coding, statistics and project portfolios alongside your qualifications.

Can I study this while working full time in India?

Yes. The programme is fully online and taught in English, so you can study from home without a student visa. Schedules are set in IST, so plan around your working hours. Specialist courses are about 300 hours, diplomas 800 and master's about 1,900. Each student has an assigned technical tutor, and fees can be paid in instalments. Ask your employer whether they will sponsor the fees.

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Learning outcomes

What you will be able to do

01

Specify quality requirements for AI datasets.

02

Organise capture and annotation processes with quality control.

03

Generate synthetic tabular, image and time-series data.

04

Measure the realism of generated data with suitable metrics.

05

Evaluate the utility and privacy risk of synthetic data.

06

Curate and version datasets while avoiding leakage between partitions.

07

Document datasets for critical and regulated systems.

Profile

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.
Standards & tools

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
Syllabus

8 modules

  1. 1.1Datasets in the AI life cycle
  2. 1.2Data requirements by task
  3. 1.3Real, synthetic and augmented data
  4. 1.4Data quality according to ISO/IEC 25012
  5. 1.5The ISO/IEC 5259 series on data quality for analytics
  6. 1.6Data governance and ownership
  7. 1.7Documentation with datasheets
  8. 1.8Legal aspects and licences
  9. 1.9Sampling bias
  10. 1.10Workshop: specifying the requirements of a dataset
  1. 2.1Planning data capture
  2. 2.2Designing annotation guidelines
  3. 2.3CVAT and Label Studio tools
  4. 2.4COCO and ASAM OpenLABEL formats
  5. 2.5Inter-annotator agreement
  6. 2.6Semi-automatic annotation with pre-labelling
  7. 2.7Label quality control
  8. 2.8Managing external annotators
  9. 2.9Annotation cost and lead times
  10. 2.10Practical exercise: annotation guideline and agreement measurement
  1. 3.1Synthetic tabular data with generative models
  2. 3.2Synthetic images through rendering
  3. 3.3Diffusion models for images
  4. 3.4GANs and variational autoencoders
  5. 3.5Synthetic time series
  6. 3.6Privacy-preserving synthetic data
  7. 3.7Classical data augmentation
  8. 3.8Balancing minority classes
  9. 3.9Controlling generated diversity
  10. 3.10Lab: tabular data with SDV
  1. 4.1Fréchet Inception Distance
  2. 4.2Kernel Inception Distance
  3. 4.3Precision and recall for generative models
  4. 4.4Density and coverage metrics
  5. 4.5Statistical distances per variable
  6. 4.6Multivariate correlations and dependencies
  7. 4.7Classifier-based two-sample tests
  8. 4.8Human perceptual evaluation
  9. 4.9Limitations of the metrics
  10. 4.10Lab: computing FID and precision-recall
  1. 5.1Train on synthetic, test on real
  2. 5.2Utility for downstream tasks
  3. 5.3Membership inference attacks
  4. 5.4Distance to closest record
  5. 5.5Re-identification risk in synthetic data
  6. 5.6Differential privacy in generators
  7. 5.7The utility-privacy trade-off
  8. 5.8GDPR requirements
  9. 5.9Evaluation reports
  10. 5.10Practical exercise: utility and privacy evaluation with SDMetrics
  1. 6.1Deduplication and cleaning
  2. 6.2Detecting incorrect labels
  3. 6.3Data selection with active learning
  4. 6.4Splitting into training, validation and test sets
  5. 6.5Data leakage between partitions
  6. 6.6Versioning with DVC
  7. 6.7Data catalogues
  8. 6.8Drift monitoring
  9. 6.9The data-centric improvement cycle
  10. 6.10Case study: cleaning a visual inspection dataset
  1. 7.1Data requirements in ISO/PAS 8800
  2. 7.2Representativeness with respect to the ODD
  3. 7.3Data sufficiency and argumentation
  4. 7.4Traceability of data to requirements
  5. 7.5Data governance under the AI Act
  6. 7.6Independent test data
  7. 7.7Dataset audit
  8. 7.8Safety risks of synthetic data
  9. 7.9Dataset change management
  10. 7.10Case study: dataset for a pedestrian detection system
  1. 8.1Defining data requirements
  2. 8.2Capture and annotation strategy
  3. 8.3Complementary synthetic generation
  4. 8.4Realism evaluation
  5. 8.5Utility and privacy evaluation
  6. 8.6Curation and versioning
  7. 8.7Dataset documentation
  8. 8.8Conclusions and proposed improvements
  9. 8.9Report and presentation
  10. 8.10Final project: final review with the tutor
Methodology, software & labs

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

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO/IEC 5259-2, ISO/IEC 25012 and ISO/IEC 23053 applied to real cases, not as a theoretical annex.
Industry software:CVAT, Label Studio, SDV (Synthetic Data Vault), DVC and Cleanlab, in free, academic or evaluation versions.
Final project:a documented dataset combining real and synthetic data, evaluated for realism, utility and privacy, and curated and versioned for production, reviewed by your tutor.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Data requirements
Define the data requirements and the capture and annotation strategy.
Complementary synthetic generation
Generate synthetic data to fill the gaps in the real dataset.
Realism, utility and privacy
Evaluate the realism, utility and privacy risk of the generated data.
Curation and documentation
Curate, version and document the final dataset.
Final project
Conclusions and proposed improvements, written up in a report and presentation reviewed by your tutor.
Career opportunities

Where this takes you

AI data engineerData quality managerSynthetic data engineerData scientistAnnotation coordinatorMLOps engineer
Admissions & scholarships

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.

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FAQ

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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