Aviation AI Data Analysis and Risk Prediction Course
At a glance
- Duration
- 5 days
- Format
- Classroom
- Cities
- Marbella, Tbilisi, Al Jubail, Manama, Cape town, Tokyo and more
- Next session
- 11 – 15 October 2026, Marbella
- Average fee
- 7,550 €
Overview
Aviation AI Data Analysis and Risk Prediction Foundation Course is a five-day foundation course for aviation operations, safety, data, maintenance, airport, and airline professionals, who leave with an Aviation AI Risk Prediction Review Pack. Participants examine data sources, quality, safety indicators, pattern detection, occurrence classification, predictions, uncertainty, and human oversight without building models. The course supports responsible interpretation of AI-assisted safety insights. Agile Leaders Training Center provides training in aviation AI data analysis and risk prediction.
Who Should Attend
- Teams responsible for aviation safety data and performance indicators
- Teams responsible for operational risk identification and prioritization
- Teams responsible for maintenance, reliability, and disruption analysis
- Teams responsible for airport, airline, and flight operations decisions
- Teams responsible for analytics governance and human oversight
The course assumes participants work with aviation operations or safety information and leaves out coding, model development, aircraft certification, engineering design, and regulatory approval.
Departments and Industries
The course supports data-informed risk decisions across airlines, airports, maintenance organizations, air navigation, ground services, and aviation authorities.
- Safety management, quality, and operational risk
- Flight, airport, and ground operations
- Maintenance, engineering liaison, and reliability
- Data analytics, digital transformation, and information governance
- Airlines, airports, service providers, and aviation consulting
Learning Objectives
By the end of this course, participants will be able to:
- Analyze aviation data sources, quality limitations, and operational context
- Use safety performance indicators and signals to frame risk questions
- Compare classification, anomaly detection, and prediction outputs
- Evaluate uncertainty, false alarms, data bias, and prediction limits
- Prioritize risks using AI evidence with human oversight
- Build an Aviation AI Risk Prediction Review Pack
Course Agenda
Day 1: Aviation AI and Safety Data
- Aviation AI Use Case and Decision Map
- Flight, Occurrence, Maintenance, Weather, and Airport Data Sources
- Safety Management System Data Context
- Structured, Unstructured, and Time-Series Data Comparison
- Human-Centric AI Responsibility Canvas
Day 2: Data Quality and Safety Indicators
- Data Completeness and Representativeness Checklist
- Data Accuracy, Consistency, Timeliness, and Provenance Review
- Safety Performance Indicator Definition Card
- Leading, Lagging, and Context Indicator Map
- Missing Data and Reporting Bias Assessment
Day 3: Patterns, Classification, and Prediction
- Pattern Detection and Trend Analysis Guide
- Occurrence Classification Decision Tree
- Anomaly Detection Signal Review
- Predictive Risk Score Interpretation Sheet
- Training, Validation, and Operational Data Distinction
Day 4: Prediction Limits and Risk Decisions
- Probability, Severity, and Exposure Comparison
- False Positive and False Negative Impact Matrix
- Uncertainty, Confidence, and Generalization Questions
- Human Review and Escalation Decision Path
- AI-Assisted Safety Risk Portfolio Prioritization
Day 5: Aviation Risk Prediction Practice
- Suggested Exercise: Diagnose an Aviation Data Quality Case
- Suggested Exercise: Interpret Safety Indicator Changes
- Suggested Exercise: Review Classification and Prediction Outputs
- Suggested Exercise: Prioritize Emerging Aviation Risks
- Capstone Exercise: Aviation AI Risk Prediction Review Pack
Practical Exercises
The course uses suggested activities to turn aviation data and AI outputs into responsible risk decisions.
- Suggested activity: map an aviation risk question to suitable data sources, indicators, owners, and limitations
- Suggested activity: inspect a fictional dataset for completeness, representativeness, missing values, and reporting bias
- Suggested activity: compare classification and prediction results while documenting uncertainty and error consequences
- Suggested activity: assemble a review pack linking evidence, risk priority, human decision authority, monitoring, and escalation
FAQs
Who suits aviation AI data analysis and risk prediction training, and what does it assume?
Aviation AI data analysis and risk prediction training suits operations, safety, maintenance, airport, airline, and data teams. It assumes familiarity with aviation activities or safety information but not programming or machine learning development.
How does aviation AI risk prediction differ from data science training?
Aviation AI risk prediction training focuses on interpreting data quality, indicators, patterns, predictions, uncertainty, and operational decisions. Data science training focuses on coding, algorithms, feature engineering, model training, and technical deployment.
What aviation data can support AI risk prediction?
Useful sources can include occurrence reports, flight data, maintenance records, reliability information, weather, airport operations, ground handling, safety audits, and operational performance indicators, subject to appropriate quality, access, and governance.
Why does human oversight matter in aviation AI risk prediction?
Human oversight connects model outputs with operational context, checks limitations and consequences, resolves conflicting evidence, assigns accountability, and decides whether a prediction should trigger investigation, monitoring, mitigation, or escalation.
Conclusion
Participants take back an Aviation AI Risk Prediction Review Pack linking risk questions, data quality, safety indicators, classification, predictions, uncertainty, error impacts, priorities, owners, monitoring, and escalation. The pack improves the consistency of AI-assisted evidence review. It preserves accountable human decisions in aviation safety and operations.
credits: 5 credit per day
Course Mode: full-time
Provider: Agile Leaders Training Center
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Events for this Course
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Marbella 11 – 15 October 2026
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Tbilisi 12 – 16 October 2026
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Al Jubail 18 – 22 October 2026
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Manama 25 – 29 October 2026
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Cape town 25 – 29 October 2026
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Tokyo 2 – 6 November 2026
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Berlin 2 – 6 November 2026
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Zanzibar 8 – 12 November 2026
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Phuket 15 – 19 November 2026
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Riyadh 29 November – 3 December 2026
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Amsterdam 30 November – 4 December 2026
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Muscat 6 – 10 December 2026
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Casablanca 7 – 11 December 2026
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Singapore 21 – 25 December 2026
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Rome 28 December 2026 – 1 January 2027
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Nairobi 3 – 7 January 2027
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Prague 4 – 8 January 2027
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Bangkok 17 – 21 January 2027
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Chicago 31 January – 4 February 2027
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Istanbul 1 – 5 February 2027
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Milan 8 – 12 February 2027
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Barcelona 15 – 19 February 2027
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San Diego 15 – 19 February 2027
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New York 22 – 26 February 2027
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Madrid 1 – 5 March 2027
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Jakarta 8 – 12 March 2027
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Seoul 8 – 12 March 2027
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Paris 15 – 19 March 2027
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Kuwait 21 – 25 March 2027
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Johannesburg 28 March – 1 April 2027
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Dubai 29 March – 2 April 2027
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London 12 – 16 April 2027
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Zoom 19 – 23 April 2027
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Munich 19 – 23 April 2027
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Tashkent 25 – 29 April 2027
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Accra 2 – 6 May 2027
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Vienna 10 – 14 May 2027
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Montreux 10 – 14 May 2027
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Nice 24 – 28 May 2027
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Sharm El-Sheikh 14 – 18 June 2027
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Athens 14 – 18 June 2027
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Bali 20 – 24 June 2027
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Abu Dhabi 28 June – 2 July 2027
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Trabzon 4 – 8 July 2027
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Dubai 12 – 16 July 2027
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Toronto 25 – 29 July 2027
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Baku 2 – 6 August 2027
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Doha 8 – 12 August 2027
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Kuala Lumpur 16 – 20 August 2027
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Langkawi 22 – 26 August 2027
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Abu Dhabi 23 – 27 August 2027
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Frankfurt 30 August – 3 September 2027
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London 6 – 10 September 2027
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Porto 6 – 10 September 2027
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Amman 19 – 23 September 2027
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Lisbon 20 – 24 September 2027
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Geneva 26 – 30 September 2027
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Cairo 11 – 15 October 2027
| Image | Location | Dates | Duration | Mode | Price | Actions |
|---|---|---|---|---|---|---|
|
|
Athens |
Week 24, 2027 14 – 18 June 2027 |
5 Days | Onsite | €7,500 | |
|
|
Bali |
Week 24, 2027 20 – 24 June 2027 |
5 Days | Onsite | €6,500 | |
|
|
Abu Dhabi |
Week 26, 2027 28 June – 2 July 2027 |
5 Days | Onsite | €6,500 | |
|
|
Trabzon |
Week 26, 2027 4 – 8 July 2027 |
5 Days | Onsite | €8,000 | |
|
|
Dubai |
Week 28, 2027 12 – 16 July 2027 |
5 Days | Onsite | €6,500 | |
|
|
Toronto |
Week 29, 2027 25 – 29 July 2027 |
5 Days | Onsite | €16,000 | |
|
|
Baku |
Week 31, 2027 2 – 6 August 2027 |
5 Days | Onsite | €8,000 | |
|
|
Doha |
Week 31, 2027 8 – 12 August 2027 |
5 Days | Onsite | €7,000 | |
|
|
Kuala Lumpur |
Week 33, 2027 16 – 20 August 2027 |
5 Days | Onsite | €6,500 | |
|
|
Langkawi |
Week 33, 2027 22 – 26 August 2027 |
5 Days | Onsite | €8,000 | |
|
|
Abu Dhabi |
Week 34, 2027 23 – 27 August 2027 |
5 Days | Onsite | €6,500 | |
|
|
Frankfurt |
Week 35, 2027 30 August – 3 September 2027 |
5 Days | Onsite | €6,500 | |
|
|
London |
Week 36, 2027 6 – 10 September 2027 |
5 Days | Onsite | €6,500 | |
|
|
Porto |
Week 36, 2027 6 – 10 September 2027 |
5 Days | Onsite | €6,500 | |
|
|
Amman |
Week 37, 2027 19 – 23 September 2027 |
5 Days | Onsite | €6,000 | |
|
|
Lisbon |
Week 38, 2027 20 – 24 September 2027 |
5 Days | Onsite | €6,500 | |
|
|
Geneva |
Week 38, 2027 26 – 30 September 2027 |
5 Days | Onsite | €8,000 | |
|
|
Cairo |
Week 41, 2027 11 – 15 October 2027 |
5 Days | Onsite | €5,200 |
Frequently asked questions
What does this course cover?
OverviewAviation AI Data Analysis and Risk Prediction Foundation Course is a five-day foundation course for aviation operations, safety, data, maintenance, airport, and airline professionals, who leave with an Aviation AI Risk Prediction Review Pack. Participants examine data sources, quality, safety indicators, pattern detection, occurrence classificatio…
Are training dates available?
Yes. Available dates and destinations are listed in the course dates section on this page.
How can I register?
Choose an available date on this page and complete the registration form, or send a programme enquiry.
Can I download the course brochure?
Yes. Use the brochure download link provided on this page.
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