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Diploma in EAM and Data Systems for Asset Management

The Diploma in EAM and Data Systems for Asset Management focuses on the implementation and optimization of Enterprise Asset Management (EAM) systems, along with the analysis and use of data for strategic decision-making in asset management. It addresses the integration of technologies such as Big Data, Machine Learning, and Predictive Analytics to improve the efficiency, reliability, and profitability of an organization's assets. The complete asset lifecycle is explored, from acquisition to retirement, using tools and methodologies for predictive maintenance, inventory optimization, and asset performance management (APM). The program trains professionals in the use of market-leading EAM platforms, maintenance data analysis, and the application of artificial intelligence (AI)-based strategies to predict failures, reduce costs, and improve asset availability. It focuses on compliance with safety regulations and the digital transformation of maintenance operations, preparing participants for roles such as asset managers, maintenance data analysts, reliability engineers, and EAM consultants, boosting competitiveness in sectors such as energy, manufacturing, and transportation.

Duration6–8 months
ModeOnline
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

EAM Optimization and Data Analysis for Naval Asset Management

02

EAM Strategies, Data Analytics, and Asset Optimization in the Naval Sector

03

Comprehensive user-oriented design and validation (from modeling to manufacturing)

04

EAM and Data Systems Mastery: Transforming Naval Asset Management

05

Advanced EAM Data Analysis for Naval Asset Excellence [The following appears to be unrelated and possibly machine-translated gibberish:] ...

06

Integration of EAM and Data Science for Strategic Naval Asset Management

07

EAM Management, Data, and Asset Optimization in the Naval Sector

08

EAM Systems Modeling and Data Analysis for Naval Asset Management

Profile

Who it's for

  • Engineers with degrees in Naval Engineering, Maritime Engineering, Systems Engineering, or related disciplines.
  • Professionals working in the maritime sector, including shipping companies, shipyards, fleet management companies, and naval technology providers.
  • Experts in naval operations, vessel maintenance, maritime logistics, and naval asset management seeking to optimize their processes and knowledge.
  • Data analysts and IT professionals interested in applying their skills to data analysis in the naval field to improve efficiency and decision-making.
  • Personnel from maritime authorities and regulatory bodies who wish to gain a deeper understanding of EAM systems and the use of data for asset management in the naval sector.
Recommended requirements: Basic knowledge of asset management and familiarity with the maritime environment; Spanish level B2 or higher. Support materials are provided for leveling.
Standards & tools

What you'll work with

  • Standards-driven curriculum: you will work with CS-27/CS-29, DO-160, DO-178C/DO-254, ARP4754A/ARP4761, ADS-33E-PRF from the first module.
  • Accreditable laboratories (EN ISO/IEC 17025) with rotor bench, EMC/Lightning pre-compliance, HIL/SIL, vibrations/acoustics.
  • Evidence-oriented TFM: safety case, test plan, compliance dossierand operational limits.
  • Mentored by industry: teachers with experience in rotorcraft, tiltrotor, eVTOL/UAM and flight test.
  • Flexible modality (hybrid/online), international cohorts and support from SEIUM Career Services.
  • Ethics and security: safety-by-design approach, cyber-OT, DIH and compliance as pillars.
Syllabus

10 modules

  1. 1.1Fundamentals of Naval Asset Management
  2. 1.2Introduction to EAM (Enterprise Asset Management)
  3. 2.3Importance of EAM in the Naval Sector
  4. 3.4Benefits of Naval Asset Optimization
  5. 4.5Current and Future Landscape of Asset Management in the Maritime Industry
  6. 5.6Introduction to Data Analysis in Naval EAM
  7. 6.7Key Concepts: Asset Lifecycle, MTBF, MTTR
  8. 7.8Common EAM Systems in the Naval Sector
  9. 8.9Data Structure and Information Gathering for EAM
  10. 9.10Case Studies: Examples of Successful EAM Implementation
  1. 2.2Fundamentals of Naval Asset Management (EAM)
  2. 2.2Importance of Data Analysis in Asset Management
  3. 2.3Introduction to EAM Systems in the Naval Sector
  4. 2.4Data Sources in the Naval Environment
  5. 2.5Principles of Data Collection and Management
  6. 2.6Key Performance Indicators (KPIs) and Metrics in Naval Asset Management
  7. 2.7Introduction to Data Analysis Tools
  8. 2.8Case Studies: EAM and Data Applications in the Naval Industry
  9. 2.9Challenges and Opportunities in the Implementation of EAM and Data Analysis
  10. 2.20Future Trends: EAM, Data, and Digital Transformation in the Naval Sector
  1. 3.3EAM Fundamentals and its Application in the Naval Sector
  2. 3.2Data Collection and Cleaning for Maritime Assets
  3. 3.3EAM Software Implementation: Selection and Configuration
  4. 3.4Integration of Maintenance and Operations Data
  5. 3.5Predictive Analytics in Naval Asset Management
  6. 3.6Key Performance Indicators (KPIs) in Naval EAM
  7. 3.7Data-Driven Maintenance Strategies
  8. 3.8Continuous Improvement and Maintenance Optimization
  9. 3.9Case Studies: Successful EAM Implementations in the Naval Sector
  10. 3.30Challenges and Future Trends in Maritime Asset Management
  1. 4.4Introduction to EAM Systems in the Naval Sector
  2. 4.2Data Collection and Management for Naval Assets
  3. 4.3Fundamentals of Data Analytics for Asset Management
  4. 4.4Integrating EAM and Data: A Strategic Approach
  5. 4.5Key Performance Indicators (KPIs) in Naval Asset Management
  6. 4.6Implementing EAM and Data: Best Practices
  7. 4.7Predictive Maintenance and Failure Analysis in the Naval Industry
  8. 4.8Cost Optimization and Operational Efficiency
  9. 4.9Modeling and Simulation of EAM Systems
  10. 4.40Case Studies: Real-World Applications in Naval Management
  1. 5.5Fundamentals of Naval Asset Management (EAM)
  2. 5.5Introduction to Data Analytics in the Maritime Sector
  3. 5.3Importance of EAM and Data Analytics in Operational Efficiency
  4. 5.4Key Technologies for EAM in the Naval Industry
  5. 5.5Data Sources and Relevant Data Types
  6. 5.6Data Analytics Tools: Overview
  7. 5.7Metrics and Key Performance Indicators (KPIs) in Asset Management
  8. 5.8Challenges and Opportunities in Implementing EAM and Data Analytics
  9. 5.9Current and Future Trends in Naval Asset Management
  10. 5.50Case Studies: Success Stories in the Industry
  11. 5.50
  1. 6.6EAM Strategies: Planning and Execution in Naval Environments
  2. 6.2Data Analysis for Identifying Asset Trends
  3. 6.3Selecting and Adapting EAM Solutions to the Naval Fleet
  4. 6.4Naval Asset Lifecycle Management
  5. 6.5EAM KPIs and Performance Metrics for Optimization
  6. 6.6Implementing Predictive Maintenance Strategies
  7. 6.7Risk Analysis and Mitigation in Asset Management
  8. 6.8Best Practices in Naval Inventory Management
  9. 6.9Data Integration and Analysis for Decision Making
  10. 6.60Case Studies: Applying EAM Strategies in Naval Fleets
  11. 2.6Planning and Implementing EAM Systems
  12. 2.2Integrating Real-Time Sensor Data
  13. 2.3Designing Asset Management Dashboards
  14. 2.4Data Analysis for Maintenance Optimization
  15. 2.5Managing Work Orders and Scheduling Maintenance
  16. 2.6Implementing Spare Parts Management
  17. 2.7Asset Configuration Management
  18. 2.8Integration with Management Systems Inventory
  19. 2.9Data Security in EAM Systems
  20. 2.60Case Studies: EAM Implementation in Maritime Environments
  21. 3.6Optimizing Naval Asset Performance
  22. 3.2Data Analysis for Identifying Faults and Breakdowns
  23. 3.3Predictive Maintenance and Trend Analysis
  24. 3.4Optimizing Maintenance Planning and Scheduling
  25. 3.5Reducing Maintenance Costs
  26. 3.6Risk Management in Asset Optimization
  27. 3.7Improving Naval Asset Availability
  28. 3.8Implementing Continuous Improvement Strategies
  29. 3.9Root Cause Analysis
  30. 3.60Case Studies: EAM Optimization in Naval Practice
  31. 4.6Introduction to EAM Systems for Naval Management
  32. 4.2Architecture and Functionalities of EAM Systems
  33. 4.3Data Integration and Data Analysis
  34. 4.4Asset Configuration Management
  35. 4.5Work Order Management
  36. 4.6Spare Parts and Warehouse Management
  37. 4.7Report Generation and Performance Analysis
  38. 4.8Security of Data in EAM Systems
  39. 4.9Integration with External Systems
  40. 4.60Future Trends in EAM Systems
  41. 5.6Advanced Data Analysis Techniques for EAM
  42. 5.2Time Series Analysis for Predictive Maintenance
  43. 5.3Failure Mode and Effects Analysis (FMEA/FMECA)
  44. 5.4Predictive Modeling and Simulation
  45. 5.5​​Machine Learning for Maintenance Optimization
  46. 5.6Risk and Reliability Analysis
  47. 5.7Optimization of Spare Parts Management
  48. 5.8Root Cause Analysis and Failure Diagnosis
  49. 5.9Data Visualization and Advanced Dashboards
  50. 5.60Case Studies: Application of Advanced Data Analysis in EAM
  51. 6.6Introduction to Data Science in Naval Asset Management
  52. 6.2Integration of EAM Data and External Sources
  53. 6.3Machine Learning Techniques for Failure Prediction
  54. 6.4Implementation of Predictive Models in EAM
  55. 6.5Optimization of Data-Driven Maintenance
  56. 6.6Big Data Analysis for Improving Asset Management
  57. 6.7Advanced Data Visualization and Interactive Dashboards
  58. 6.8Integration with Inventory Management Systems
  59. 6.9Cybersecurity in Data Management
  60. 6.60Case Studies: Integrating EAM and Data Science in Naval Practice
  61. 7.6EAM Management Strategies in the Naval Sector
  62. 7.2Data Collection and Analysis for Decision Making
  63. 7.3Optimizing Naval Asset Performance
  64. 7.4Maintenance Planning and Scheduling
  65. 7.5Inventory and Spare Parts Management
  66. 7.6Risk Analysis and Regulatory Compliance
  67. 7.7Continuous Improvement and Operational Efficiency
  68. 7.8Implementing Emerging Technologies
  69. 7.9Key Performance Indicators (KPIs)
  70. 7.60Case Studies: Success Stories in Naval EAM Management
  71. 8.6Data Modeling for Asset Management
  72. 8.2Database Design for EAM Systems
  73. 8.3Maintenance Process Modeling
  74. 8.4Scenario Simulation and Risk Analysis
  75. 8.5Optimizing asset configuration
  76. 8.6Integration with Geographic Information Systems (GIS)
  77. 8.7Creating dashboards and data visualization
  78. 8.8Asset lifecycle management
  79. 8.9Implementing predictive models
  80. 8.60Case studies: Modeling EAM systems in the naval sector
  81. 8.60
  1. 7.7Fundamentals of Naval Asset Management (EAM)
  2. 7.2Introduction to Data Analysis in the Naval Sector
  3. 7.3Importance of EAM and Data in Decision Making
  4. 7.4Key Components of a Naval EAM System
  5. 7.7Types of Data Relevant to Asset Management
  6. 7.6Data Sources: Sensors, Logs, and Databases
  7. 7.7Introduction to Data Analysis Tools
  8. 7.8Key Performance Metrics (KPIs) in Naval EAM
  9. 7.9Benefits of Integrating EAM and Data Analysis
  10. 7.70Case Studies: Real-World Applications in the Naval Industry
  1. 8.8Introduction to EAM and its relevance in naval asset management.
  2. 8.8Data collection and cleaning for analysis in the naval sector.
  3. 8.3Key performance indicators (KPIs) in naval asset management.
  4. 8.4Data analysis tools and techniques applied to EAM.
  5. 8.5Identifying patterns and trends in naval asset data.
  6. 8.6Practical examples of asset optimization using data analysis.
  7. 8.7Introduction to artificial intelligence (AI) and machine learning (ML) in EAM.
  8. 8.8Case study: Applying data analysis to improve efficiency and reduce costs.
  9. 8.8Data visualization and report creation for decision-making.
  10. 8.80Risk and opportunity assessment in asset management.
  11. 8.8Introduction to EAM strategies in the naval sector.
  12. 8.8Selection and implementation of maintenance strategies. 8.3 Application of Data Analytics for Strategic Decision Making
  13. 8.4Optimization of the Naval Asset Lifecycle
  14. 8.5Asset Profitability Analysis
  15. 8.6Use of Data for Maintenance Planning and Scheduling
  16. 8.7Continuous Improvement and Change Management in EAM
  17. 8.8Data Analytics Tools and Software Applied to EAM
  18. 8.8Predictive Modeling and Risk Analysis
  19. 8.80Case Studies: Implementation of EAM and Data Analytics Strategies
  20. 3.8Introduction to EAM System Implementation
  21. 3.8Selection and Evaluation of EAM Systems
  22. 3.3Planning the Implementation of EAM in Maritime Environments
  23. 3.4Design and Configuration of EAM Systems
  24. 3.5Integration of Data from Different Sources into the EAM System
  25. 3.6Training and Development of Personnel in the Use of the EAM System 3.7 Change Management and Adaptation to New Technologies
  26. 3.8Testing and Validation of the Implemented EAM System
  27. 3.8Monitoring and Optimization of EAM System Performance
  28. 3.80Case Study: Successful Implementation of EAM Systems in the Maritime Sector
  29. 4.8Overview of EAM Systems and Their Importance in Naval Management
  30. 4.8Architecture and Key Components of EAM Systems
  31. 4.3Introduction to Databases and Their Application in EAM
  32. 4.4Integration of Data from Sensors and Monitoring Systems
  33. 4.5Use of Data for Maintenance Scheduling and Planning
  34. 4.6Generation of Reports and Dashboards for Decision-Making
  35. 4.7Data Analysis for Resource and Cost Optimization
  36. 4.8Cybersecurity in EAM Systems and Data Protection
  37. 4.8Emerging Trends in EAM and Data Systems 4.80 Case Study: Transforming Naval Management with EAM and Data Systems
  38. 5.8Introduction to Advanced Data Analysis in Naval EAM
  39. 5.8Descriptive and Diagnostic Data Analysis Techniques
  40. 5.3Predictive Analysis for Maintenance Optimization
  41. 5.4Using Machine Learning Algorithms in EAM
  42. 5.5Time Series Analysis and its Application in Asset Management
  43. 5.6Risk Modeling and Simulation in EAM
  44. 5.7Optimizing Inventory and Spare Parts Management
  45. 5.8Integrating Data from Multiple Sources for More Comprehensive Analysis
  46. 5.8Using Business Intelligence (BI) Tools in Data Analysis
  47. 5.80Case Study: Applying Advanced Data Analysis to Naval Asset Excellence
  48. 6.8Introduction to the Integration of EAM and Data Science
  49. 6.8Data Collection and Preparation for Analysis 6.3 Application of Machine Learning algorithms in EAM.
  50. 6.4Development of predictive models for asset management.
  51. 6.5Optimization of predictive maintenance using Data Science.
  52. 6.6Analysis and optimization of asset lifecycles.
  53. 6.7Use of artificial intelligence in decision-making.
  54. 6.8Implementation of Data Science solutions in EAM systems.
  55. 6.9Evaluation of return on investment (ROI) in Data Science projects.
  56. 6.10Case study: Successful integration of EAM and Data Science in the strategic management of naval assets.
  57. 7.8Introduction to EAM management in the naval sector.
  58. 7.9Collection and analysis of naval asset data.
  59. 7.3Optimization of asset lifecycles.
  60. 7.4Maintenance strategies and their application in EAM.
  61. 7.5Use of data in maintenance planning and scheduling. 7.6 Inventory Optimization and Spare Parts Management
  62. 7.7Risk Assessment and Data-Driven Decision Making
  63. 7.8Tools and Software for EAM Management and Optimization
  64. 7.8Continuous Improvement and Change Management in Asset Management
  65. 7.80Case Studies: EAM Management, Data, and Optimization in the Naval Sector
  66. 8.8Introduction to EAM System Modeling
  67. 8.8Modeling Methodologies and Tools
  68. 8.3Data Modeling for Asset Management
  69. 8.4Database Design for EAM
  70. 8.5Simulation and Scenario Analysis in EAM
  71. 8.6Risk and Reliability Analysis in EAM Systems
  72. 8.7Maintenance Planning Optimization
  73. 8.8Use of Predictive Models in Asset Management
  74. 8.8Integration of Modeling with Data Analysis Systems 8.80 Case Study: EAM Systems Modeling and Data Analysis in Naval Asset Management.
  1. 9.9Introduction to Naval Asset Management and the Importance of Optimization
  2. 9.9Overview of EAM Systems and Their Application in the Naval Sector
  3. 9.3Fundamentals of Data Analysis and Their Relevance to Asset Management
  4. 9.4Key Performance Indicators (KPIs) in Naval Asset Management
  5. 9.5Tools and Technologies for Naval Asset Optimization
  6. 9.9Developing EAM Strategies for the Naval Sector
  7. 9.9Applying Data Analytics to Naval Asset Management
  8. 9.3Data Analysis for Decision-Making in Asset Management
  9. 9.4Optimizing the Lifespan of Naval Assets
  10. 9.5Case Studies and Best Practices in the Naval Industry
  11. 3.9Methodologies for Implementing EAM Systems in the Maritime Sector
  12. 3.9Integrating Data from Different Sources for Asset Analysis 3.3 Use of dashboards and visualizations for intelligent asset management.
  13. 3.4Identification and mitigation of risks in naval asset management.
  14. 3.5Planning and execution of EAM implementation projects.
  15. 4.9In-depth study of the most widely used EAM systems in the naval sector.
  16. 4.9Application of Data Analytics for predictive maintenance.
  17. 4.3Use of artificial intelligence and machine learning in asset management.
  18. 4.4Transformation of naval asset management through technology.
  19. 4.5Evaluation and selection of suitable EAM systems for naval management.
  20. 5.9Advanced data analysis techniques for naval asset management.
  21. 5.9Analysis of historical data for asset performance optimization.
  22. 5.3Predictive modeling for maintenance and inventory management.
  23. 5.4Cost optimization and risk reduction in asset management. 5.5 Implementation of data-driven continuous improvement strategies.
  24. 6.9Integration of EAM systems with Data Science for strategic asset management.
  25. 6.9Application of machine learning models for failure prediction and predictive maintenance.
  26. 6.3Data analysis for resource planning optimization.
  27. 6.4Strategies for data-driven decision-making in naval asset management.
  28. 6.5Implementation of a data-driven asset management approach.
  29. 7.9Comprehensive view of EAM management, data, and optimization in the naval sector.
  30. 7.9Application of asset optimization tools and techniques.
  31. 7.3Optimization of inventory and spare parts management.
  32. 7.4Risk analysis and mitigation strategies in naval asset management.
  33. 7.5Performance evaluation and continuous improvement in asset management.
  34. 8.9Modeling of EAM systems for simulation and optimization of asset management. 8.9 Application of data analysis for identifying trends and patterns.
  35. 8.3Development of predictive models for naval asset management.
  36. 8.4Optimization of asset lifespan and cost reduction.
  37. 8.5Implementation of a model- and data-driven asset management approach.
  1. 1. EAM Data Analysis for Naval Fleet Optimization: Introduction
  2. 2. EAM Data Collection and Preparation for Naval Analysis
  3. 3. Descriptive and Diagnostic Analysis of EAM Data in the Naval Environment
  4. 4. Predictive Analysis of EAM Data for Naval Maintenance
  5. 5. Prescriptive Analysis of EAM Data and Fleet Decision-Making
  6. 6. Visualization and Communication of Naval EAM Analysis Results
  7. 7. Risk Modeling and Simulation in Naval Asset Management
  8. 8. Optimization of Maintenance Planning Based on EAM Data
  9. 9. Implementation of EAM Key Performance Indicators (KPIs) in Fleets
  10. 10. Final Project: Practical Case Study of EAM Analysis in a Naval Fleet
Methodology, software & labs

How you'll learn

  • Hands-on methodology: test-before-you-trust, design reviews, failure analysis, compliance evidence.
  • Software (depending on licenses/partners): MATLAB/Simulink, Python (NumPy/SciPy), OpenVSP, SU2/OpenFOAM, Nastran/Abaqus, AMESim/Modelica, acoustics tools, planning toolchains DO-178C.
  • SEIUM Laboratories: scale rotor bench, vibrations/acoustics, EMC/Lightning pre-compliance, HIL/SIL for AFCS, data acquisition with strain gauging.
  • Standards and compliance: EN 9100, 17025, ISO 27001, GDPR.
Accreditable laboratories(EN ISO/IEC 17025) with rotor bench, EMC/Lightning pre-compliance, HIL/SIL, vibrations/acoustics.
Evidence-oriented Master's Thesis:safety case, test plan, compliance dossier and operational limits.
Industry Mentored:teachers with experience in training, , eVTOL/UAM and flight test.
Flexible modality(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:safety-by-design approach, cyber-OT, DIH and compliance as pillars.
Career opportunities

Where this takes you

**EAM/Data Analyst** (Asset Performance, Reliability Engineering)**EAM/Data Analyst** (Asset Performance, Data Governance)**EAM/Data Analyst** (Asset Performance, Data Governance)**EAM/Data Analyst** (Asset Performance, Data Analytics)EAM/Data Specialist (Asset Management, Data Analytics)EAM/Data Specialist (Asset Management, Data Analytics)
Admissions & scholarships

Requirements

  • Profile: Background in Computer Engineering, Mathematics, Statistics, or related fields; practical experience in NLP and valued information retrieval systems.
  • Documentation: Updated CV, academic transcript, SOP/statement of purpose, project examples or code (optional).
  • Process: Application → Technical evaluation of profile and experience → Technical interview → Review of case studies → Final decision → Enrollment.
  • Fees:
    • Single payment: 10% discount.
    • Payment in 3 installments: No fees; 30% upon registration + 2 equal monthly payments of the remaining 35%. Monthly payment: available with a 7% commission on the total; annual review. Scholarships: based on academic merit, financial need, and promoting inclusion; agreements with companies in the sector for partial or full scholarships. See "Calendar & Calls for Applications," "Scholarships & Grants," and "Fees & Financing" in the SEIUM mega-menu.

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FAQ

Frequently asked questions

Yes, we have international certification

Yes: experimental models, real data, applied simulations, professional environments, real case studies.

It is not mandatory. We offer leveling tracks and tutoring.

Completely. It covers e-propulsion, integration, and emerging regulations (SC-VTOL).

Recommended. There are also internal challenges and consortiums.

Yes. Online/hybrid modality with planned labs and visa support (see “Visa & Residence”).

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