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CourseDefense, Military Engineering & Land Systems Applications open

Course in Ethics in Big Data Applied to Defence

Online 300-hour course in ethics in big data applied to defence. 6 modules covering Regulation (EU) 2016/679 (GDPR), Organic Law 3/2018 (LOPDGDD) and Apache Hadoop. Assigned tutor and open enrolment.

Duration300 h
ModeOnline
Start datesSet by the faculty
AwardSEIUM Institute certificate
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Learning outcomes

What you will be able to do

01

Describe the uses of big data in defence and its value chain.

02

Apply the privacy and data protection framework to defence systems.

03

Identify biases and ethical risks in data analysis.

04

Design data governance and security policies.

05

Assign responsibilities and human oversight mechanisms.

06

Assess the quality and integrity of the data used.

07

Produce an ethical assessment of a defence data system.

Profile

Who it's for

  • Data engineer who wants to specialise in Ethics in Big Data Applied to Defence.
  • Data governance manager who wants to specialise in Ethics in Big Data Applied to Defence.
  • Data protection officer who wants to specialise in Ethics in Big Data Applied to Defence.
  • Cybersecurity analyst who wants to specialise in Ethics in Big Data Applied to Defence.
  • Big data architect who wants to specialise in Ethics in Big Data Applied to Defence.
  • Information systems auditor who wants to specialise in Ethics in Big Data Applied to Defence.
  • Graduates in engineering or science looking to enter the defence sector.
Standards & tools

What you'll work with

  • Regulation (EU) 2016/679 (GDPR)
  • Organic Law 3/2018 (LOPDGDD)
  • Royal Decree 311/2022 (National Security Framework)
  • ISO/IEC 27701 (privacy management)
  • ISO/IEC 20547 (big data reference architecture)
  • ISO 8000 (data quality)
  • ISO/IEC 27001 (information security)
Syllabus

6 modules · 300 h

  1. 1.1The five Vs of big data
  2. 1.2Data sources in defence: sensors, satellites and networks
  3. 1.3Open-source intelligence (OSINT)
  4. 1.4Multi-source data fusion
  5. 1.5Data architectures: lakes and platforms
  6. 1.6Predictive analytics in logistics and maintenance
  7. 1.7Data-driven situational awareness
  8. 1.8Data in cyber defence
  9. 1.9European defence data projects
  10. 1.10Practical case: data architecture of a command centre
  1. 2.1GDPR and the national security exclusion
  2. 2.2Directive (EU) 2016/680 and police processing
  3. 2.3Organic Law 7/2021
  4. 2.4Personal data in OSINT
  5. 2.5Anonymisation and pseudonymisation
  6. 2.6Re-identification and data combination
  7. 2.7Social media data and metadata
  8. 2.8Mass surveillance and European case law
  9. 2.9Data protection impact assessment
  10. 2.10Workshop: impact assessment of an OSINT project
  1. 3.1Consent and reasonable expectations
  2. 3.2Bias in data and predictive models
  3. 3.3Profiling of individuals and groups
  4. 3.4Discrimination and use of sensitive data
  5. 3.5Opacity of models
  6. 3.6Proportionality and necessity
  7. 3.7Effects on the civilian population
  8. 3.8Research ethics with conflict data
  9. 3.9Humanitarian data and the do-no-harm principle
  10. 3.10Practice: ethical analysis of a risk prediction model
  1. 4.1Data governance in defence organisations
  2. 4.2Data quality and lineage
  3. 4.3Information classification and access control
  4. 4.4Data sovereignty and defence cloud
  5. 4.5European data spaces
  6. 4.6Cybersecurity of data platforms
  7. 4.7Data retention and destruction
  8. 4.8Access auditing and traceability
  9. 4.9Data sharing with allies
  10. 4.10Laboratory: designing an access control model
  1. 5.1Parliamentary and judicial oversight
  2. 5.2Data protection authorities
  3. 5.3Transparency and accountability
  4. 5.4Technology companies as suppliers
  5. 5.5Public procurement of data platforms
  6. 5.6Academic research with defence data
  7. 5.7Codes of conduct for analysts
  8. 5.8Whistleblowing
  9. 5.9Lessons from known surveillance cases
  10. 5.10Practical case: analysis of the Snowden revelations
  1. 6.1Selection of the big data system
  2. 6.2Data sources and flows
  3. 6.3Legal analysis of data protection
  4. 6.4Ethical risk assessment
  5. 6.5Governance and security measures
  6. 6.6Oversight mechanisms
  7. 6.7Design recommendations
  8. 6.8Monitoring plan
  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 timetable: students progress at their own pace within the intake, which makes it possible to combine the programme with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Apache Hadoop, Apache Kafka, Elasticsearch, Power BI, Python (pandas), in free, academic or evaluation versions. Practical exercises are based on real procedures for data protection impact assessments, data governance audits and big data use-case analysis. Assessment is continuous by module, plus the final integrative project.

Accreditable laboratories(EN ISO/IEC 17025) with a rotor test rig, EMC/Lightning pre-compliance, HIL/SIL and vibration/acoustics.
Master's Thesis focused on evidence:safety case, test plan, compliance dossier and operational limits.
Industry mentoring:lecturers with a track record in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and security:a safety-by-design approach, with ciber-OT, 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.

Selection of the big data system

Describe the uses of big data in defence and its value chain.

Data sources and flows

Apply the privacy and data protection framework to defence systems.

Legal analysis of data protection

Identify biases and ethical risks in data analysis.

Ethical risk assessment

Design data governance and security policies.

Governance and security measures

Assign responsibilities and human oversight mechanisms.

Career opportunities

Where this takes you

Data engineerData governance managerData protection officerCybersecurity analystBig data architectInformation systems auditor
Admissions & scholarships

Requirements

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

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 comprises 300 hours in total, spread over 3 to 5 months. That is an average of 10 to 15 hours a week, compatible with full-time work.

The programme uses Apache Hadoop, Apache Kafka, Elasticsearch, Power BI, Python (pandas). Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.

A SEIUM Institute diploma upon passing the continuous assessment and the final integrative project. It is in-house training, not an official Ministry qualification.

Yes. Modules passed in a Course or Postgraduate Diploma in the same area are credited towards the advanced programme. Check the credit transfer policy when you enrol.

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