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Big Data in Nuclear Plant Operation Course

Online 300-hour course in Big Data in nuclear plant operation. 6 modules covering IEC 62645 (cybersecurity in nuclear power plant I&C systems), Regulation (EU) 2016/679 (data protection) and Apache Spark. Assigned tutor and open enrolment.

Duration300 h
ModeOnline
Start datesOctober / March
LocationEurope (Madrid)
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Identify the sources and types of data generated in a nuclear power plant.

02

Design a Big Data architecture suited to nuclear OT environments.

03

Apply descriptive and predictive analytics techniques to operational data.

04

Develop predictive maintenance models for critical equipment.

05

Assess cybersecurity requirements and data quality during deployment.

06

Interpret the regulatory framework applicable to the use of analytics in the plant.

07

Prepare an analytics project aimed at improving availability.

Profile

Who it's for

  • Data scientist in the nuclear sector who wants to specialise in Big Data in nuclear plant operation.
  • Industrial data engineer who wants to specialise in Big Data in nuclear plant operation.
  • Predictive maintenance engineer who wants to specialise in Big Data in nuclear plant operation.
  • Plant performance analyst who wants to specialise in Big Data in nuclear plant operation.
  • OT data platform architect who wants to specialise in Big Data in nuclear plant operation.
  • Equipment reliability engineer who wants to specialise in Big Data in nuclear plant operation.
  • Engineering or science graduates looking to enter nuclear power plants.
Standards & tools

What you'll work with

  • IEC 62645 (cybersecurity in nuclear power plant I&C systems)
  • Regulation (EU) 2016/679 (data protection)
  • 10 CFR 50.65 (maintenance rule)
  • IEC 62443 (cybersecurity of industrial automation systems)
  • ISO 55001 (asset management)
  • ISO/IEC 27001 (information security)
  • 10 CFR 50 Appendix B (quality assurance)
Syllabus

6 modules · 300 h

  1. 1.1Data sources: process, maintenance and dosimetry
  2. 1.2Plant historians: PI System
  3. 1.3Process computer systems
  4. 1.4Volume, velocity and variety
  5. 1.5Data quality and validation
  6. 1.6KKS labelling and nomenclature
  7. 1.7Data integrity and regulatory requirements
  8. 1.8Classification of sensitive information
  9. 1.9Network architecture and security zones
  10. 1.10Workshop: inventory of a plant's data sources
  1. 2.1Data lakes and warehouses
  2. 2.2Hadoop and Spark ecosystem
  3. 2.3Time-series databases
  4. 2.4Data transfer with data diodes
  5. 2.5Cloud computing and sovereignty
  6. 2.6Cybersecurity according to IEC 62645
  7. 2.7Data governance
  8. 2.8Metadata and lineage
  9. 2.9Scalability and performance
  10. 2.10Practice: ingestion pipeline with Spark
  1. 3.1Exploratory signal analysis
  2. 3.2Anomaly detection
  3. 3.3Thermal performance monitoring
  4. 3.4Vibration trend analysis
  5. 3.5Sensor validation with analytical redundancy
  6. 3.6Online instrument calibration
  7. 3.7Fuel cycle optimisation
  8. 3.8Alarm management
  9. 3.9WANO performance indicators
  10. 3.10Laboratory: detecting drift in flow sensors
  1. 4.1Ageing management
  2. 4.2Pump and motor monitoring
  3. 4.3Oil analysis and thermography
  4. 4.4Remaining life models
  5. 4.5Supervised machine learning
  6. 4.6Recurrent neural networks and LSTM
  7. 4.7Digital twins of components
  8. 4.8Integration with the work order system
  9. 4.9Return on investment
  10. 4.10Case study: failure prediction in a cooling pump
  1. 5.1CSN and NRC requirements on software
  2. 5.2Safety and non-safety classification
  3. 5.3Validation of AI models
  4. 5.4Explainability for operators
  5. 5.5Organisational change management
  6. 5.6EPRI initiatives in analytics
  7. 5.7INL projects on digitalisation
  8. 5.8Privacy and staff protection
  9. 5.9Lessons from real deployments
  10. 5.10Case study: monitoring programme across a fleet
  1. 6.1Defining the operational problem
  2. 6.2Data and variable selection
  3. 6.3Preparation and cleaning
  4. 6.4Model development
  5. 6.5Validation and metrics
  6. 6.6Visualisation and dashboard
  7. 6.7Deployment plan
  8. 6.8Benefit assessment
  9. 6.9Report and presentation
  10. 6.10Final review with the tutor
Methodology, software & labs

How you'll learn

100 % online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: students progress at their own pace within the intake, which makes it possible to combine study with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Apache Spark, AVEVA PI System, Python (pandas, scikit-learn), Power BI, Grafana, in free, academic or evaluation versions. The tests are studied on real procedures for analysing plant process histories, anomaly detection in time series and predictive maintenance pilots. Assessment is continuous per module plus the final integrating project.

Accreditable laboratories(EN ISO/IEC 17025) with rotor test rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-oriented final project:safety case, test plan, compliance dossier and operational limits.
Industry mentoring:lecturers with experience in training, , eVTOL/UAM and flight test.
Flexible study mode(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:a safety-by-design approach, OT cyber, DIH and compliance as core pillars.
Capstone project

Build it end to end

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

Defining the operational problem

Identify the sources and types of data generated in a nuclear power plant.

Data and variable selection

Design a Big Data architecture suited to nuclear OT environments.

Preparation and cleaning

Apply descriptive and predictive analytics techniques to operational data.

Model development

Develop predictive maintenance models for critical equipment.

Validation and metrics

Assess cybersecurity requirements and data quality during deployment.

Career opportunities

Where this takes you

Data scientist in the nuclear sectorIndustrial data engineerPredictive maintenance engineerPlant performance analystOT data platform architectEquipment reliability engineer
Admissions & scholarships

Requirements

  • No prior degree is required.
  • Internet connection and a computer capable of running the programme's software.
  • Sufficient command of Spanish to follow technical regulatory reading.

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 is 300 hours in total, spread over 3 to 5 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.

You work with Apache Spark, AVEVA PI System, Python (pandas, scikit-learn), Power BI, Grafana. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.

A SEIUM's own diploma upon passing the continuous assessment and the final integrating project. This is in-house training, not an official qualification from the Ministry.

Yes. Modules passed in a course or postgraduate diploma in the same area can be credited towards the advanced programme. Please consult the credit recognition policy when enrolling.

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