Diploma in EAM and Data Systems for Asset Management
Sobre nuestro 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.
Target keywords (natural in the text): EAM Systems, Asset Management, Data Analytics, Predictive Maintenance, Big Data, Machine Learning, Asset Optimization, EAM Diploma.
Diploma in EAM and Data Systems for Asset Management
- Modalidad: Online
- Duración: 8 meses
- Horas: 900 H
- Idioma: ES / EN
- Créditos: 60 ECTS
- Fecha de matrÃcula: 30-04-2026
- Fecha de inicio: 10-06-2026
- Plazas disponibles: 11
1.449 $
Competencias y resultados
Qué aprenderás
1. EAM Optimization and Data Analysis for Naval Asset Management
Here is the requested content:
- Master the application of Enterprise Asset Management (EAM) systems in the naval context, optimizing maintenance planning and execution.
- Deepen your understanding of data analysis for asset management, including identifying trends, failure patterns, and predicting useful life.
- Understand the principles of asset management optimization, including defining key performance indicators (KPIs) and implementing continuous improvement strategies.
- Evaluate and mitigate risks associated with naval asset management, using risk analysis and contingency planning tools.
- Analyze the life cycle of naval assets, from acquisition to decommissioning, optimizing costs and maximizing performance.
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1. EAM Strategies, Data Analytics, and Asset Optimization in the Naval Sector
Here is the requested content:
- Master Enterprise Asset Management (EAM) strategies applied to the naval fleet, including preventive and predictive maintenance planning.
- Apply Data Analytics techniques for analyzing sensor data, equipment performance, and optimizing fleet availability.
- Implement asset optimization models to maximize the lifespan of naval components and reduce operating costs.
- Use predictive analytics tools to anticipate failures and schedule maintenance efficiently.
- Learn to use specialized software for monitoring and managing naval assets.
- Understand and apply relevant regulations and standards for asset management in the naval sector.
- Develop data-driven decision-making skills to improve fleet performance and safety.
- Analyze and apply **key performance indicators (KPIs)** to evaluate the effectiveness of EAM strategies.
3. Comprehensive user-oriented design and validation (from modeling to manufacturing)
You will learn to integrate the entire product development process, from initial model conception to final validation, applying user-centered methodologies. You will develop skills in parametric design, ergonomics, simulation, sustainable materials, 3D visualization, and manufacturing management, ensuring efficient, safe solutions that meet current industry standards.
4. EAM and Data Systems Mastery: Transforming Naval Asset Management
4. Mastery of EAM and Data Systems: Transforming Naval Asset Management
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Gain in-depth knowledge of naval asset management through comprehensive data analysis and the implementation of Enterprise Asset Management (EAM) systems.
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Understand the complete asset lifecycle, from acquisition to final disposal, optimizing the planning, scheduling, and execution of maintenance tasks.
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Learn to use advanced EAM systems for asset tracking, inventory management, and scheduling of preventive and predictive maintenance.
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Analyze historical and real-time data to identify patterns, predict failures, and optimize the performance of naval assets.
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Apply predictive analytics and data modeling techniques for data-driven decision-making, improving operational efficiency and reducing costs.
Master the use of data analysis tools and specialized software for asset management, including data visualization and report generation.
Implement risk management strategies to mitigate the impacts of asset failures and ensure the availability and reliability of naval systems.
Optimize spare parts management and supply chain planning to ensure the timely availability of parts and components.
Understand the regulations and standards relevant to naval asset management, including safety, environmental, and legal compliance.
Develop leadership and management skills to oversee maintenance and operations teams, driving continuous improvement and innovation.
5. Advanced EAM Data Analysis for Naval Asset Excellence [The following appears to be unrelated and possibly machine-translated gibberish:] ...
5. Advanced EAM Data Analysis for Naval Asset Excellence
- Identify and assess the impact of failures on critical components.
- Apply advanced data analysis techniques for failure prediction.
- Use simulation models to optimize the performance of naval assets.
- Interpret data from sensors and real-time monitoring systems.
- Develop data-driven predictive maintenance strategies.
- Manage asset lifecycles and schedule maintenance activities.
- Optimize the availability and reliability of naval systems.
- Analyze trends and patterns in data to identify opportunities for improvement.
- Integrate data from different sources for a comprehensive view of asset performance.
- Apply machine learning algorithms for early failure detection.
- Evaluate the impact of environmental conditions on the useful life of assets.
- Generate reports and presentations of results to support decision-making.
6. Integration of EAM and Data Science for Strategic Naval Asset Management
You will learn to integrate the entire product development process, from initial model conception to final validation, applying user-centered methodologies. You will develop skills in parametric design, ergonomics, simulation, sustainable materials, 3D visualization, and manufacturing management, ensuring efficient, safe solutions that meet current industry standards.
Para quien va dirigido nuestro:
Diploma in EAM and Data Systems for Asset Management
- 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-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.
1.1 Fundamentals of Naval Asset Management
1.2 Introduction to EAM (Enterprise Asset Management)
2.3 Importance of EAM in the Naval Sector
3.4 Benefits of Naval Asset Optimization
4.5 Current and Future Landscape of Asset Management in the Maritime Industry
5.6 Introduction to Data Analysis in Naval EAM
6.7 Key Concepts: Asset Lifecycle, MTBF, MTTR
7.8 Common EAM Systems in the Naval Sector
8.9 Data Structure and Information Gathering for EAM
9.10 Case Studies: Examples of Successful EAM Implementation
2.2 Fundamentals of Naval Asset Management (EAM)
2.2 Importance of Data Analysis in Asset Management
2.3 Introduction to EAM Systems in the Naval Sector
2.4 Data Sources in the Naval Environment
2.5 Principles of Data Collection and Management
2.6 Key Performance Indicators (KPIs) and Metrics in Naval Asset Management
2.7 Introduction to Data Analysis Tools
2.8 Case Studies: EAM and Data Applications in the Naval Industry
2.9 Challenges and Opportunities in the Implementation of EAM and Data Analysis
2.20 Future Trends: EAM, Data, and Digital Transformation in the Naval Sector
3.3 EAM Fundamentals and its Application in the Naval Sector
3.2 Data Collection and Cleaning for Maritime Assets
3.3 EAM Software Implementation: Selection and Configuration
3.4 Integration of Maintenance and Operations Data
3.5 Predictive Analytics in Naval Asset Management
3.6 Key Performance Indicators (KPIs) in Naval EAM
3.7 Data-Driven Maintenance Strategies
3.8 Continuous Improvement and Maintenance Optimization
3.9 Case Studies: Successful EAM Implementations in the Naval Sector
3.30 Challenges and Future Trends in Maritime Asset Management
4.4 Introduction to EAM Systems in the Naval Sector
4.2 Data Collection and Management for Naval Assets
4.3 Fundamentals of Data Analytics for Asset Management
4.4 Integrating EAM and Data: A Strategic Approach
4.5 Key Performance Indicators (KPIs) in Naval Asset Management
4.6 Implementing EAM and Data: Best Practices
4.7 Predictive Maintenance and Failure Analysis in the Naval Industry
4.8 Cost Optimization and Operational Efficiency
4.9 Modeling and Simulation of EAM Systems
4.40 Case Studies: Real-World Applications in Naval Management
5.5 Fundamentals of Naval Asset Management (EAM)
5.5 Introduction to Data Analytics in the Maritime Sector
5.3 Importance of EAM and Data Analytics in Operational Efficiency
5.4 Key Technologies for EAM in the Naval Industry
5.5 Data Sources and Relevant Data Types
5.6 Data Analytics Tools: Overview
5.7 Metrics and Key Performance Indicators (KPIs) in Asset Management
5.8 Challenges and Opportunities in Implementing EAM and Data Analytics
5.9 Current and Future Trends in Naval Asset Management
5.50 Case Studies: Success Stories in the Industry
5.50
6.6 EAM Strategies: Planning and Execution in Naval Environments
6.2 Data Analysis for Identifying Asset Trends
6.3 Selecting and Adapting EAM Solutions to the Naval Fleet
6.4 Naval Asset Lifecycle Management
6.5 EAM KPIs and Performance Metrics for Optimization
6.6 Implementing Predictive Maintenance Strategies
6.7 Risk Analysis and Mitigation in Asset Management
6.8 Best Practices in Naval Inventory Management
6.9 Data Integration and Analysis for Decision Making
6.60 Case Studies: Applying EAM Strategies in Naval Fleets
2.6 Planning and Implementing EAM Systems
2.2 Integrating Real-Time Sensor Data
2.3 Designing Asset Management Dashboards
2.4 Data Analysis for Maintenance Optimization
2.5 Managing Work Orders and Scheduling Maintenance
2.6 Implementing Spare Parts Management
2.7 Asset Configuration Management
2.8 Integration with Management Systems Inventory
2.9 Data Security in EAM Systems
2.60 Case Studies: EAM Implementation in Maritime Environments
3.6 Optimizing Naval Asset Performance
3.2 Data Analysis for Identifying Faults and Breakdowns
3.3 Predictive Maintenance and Trend Analysis
3.4 Optimizing Maintenance Planning and Scheduling
3.5 Reducing Maintenance Costs
3.6 Risk Management in Asset Optimization
3.7 Improving Naval Asset Availability
3.8 Implementing Continuous Improvement Strategies
3.9 Root Cause Analysis
3.60 Case Studies: EAM Optimization in Naval Practice
4.6 Introduction to EAM Systems for Naval Management
4.2 Architecture and Functionalities of EAM Systems
4.3 Data Integration and Data Analysis
4.4 Asset Configuration Management
4.5 Work Order Management
4.6 Spare Parts and Warehouse Management
4.7 Report Generation and Performance Analysis
4.8 Security of Data in EAM Systems
4.9 Integration with External Systems
4.60 Future Trends in EAM Systems
5.6 Advanced Data Analysis Techniques for EAM
5.2 Time Series Analysis for Predictive Maintenance
5.3 Failure Mode and Effects Analysis (FMEA/FMECA)
5.4 Predictive Modeling and Simulation
5.5 ​​Machine Learning for Maintenance Optimization
5.6 Risk and Reliability Analysis
5.7 Optimization of Spare Parts Management
5.8 Root Cause Analysis and Failure Diagnosis
5.9 Data Visualization and Advanced Dashboards
5.60 Case Studies: Application of Advanced Data Analysis in EAM
6.6 Introduction to Data Science in Naval Asset Management
6.2 Integration of EAM Data and External Sources
6.3 Machine Learning Techniques for Failure Prediction
6.4 Implementation of Predictive Models in EAM
6.5 Optimization of Data-Driven Maintenance
6.6 Big Data Analysis for Improving Asset Management
6.7 Advanced Data Visualization and Interactive Dashboards
6.8 Integration with Inventory Management Systems
6.9 Cybersecurity in Data Management
6.60 Case Studies: Integrating EAM and Data Science in Naval Practice
7.6 EAM Management Strategies in the Naval Sector
7.2 Data Collection and Analysis for Decision Making
7.3 Optimizing Naval Asset Performance
7.4 Maintenance Planning and Scheduling
7.5 Inventory and Spare Parts Management
7.6 Risk Analysis and Regulatory Compliance
7.7 Continuous Improvement and Operational Efficiency
7.8 Implementing Emerging Technologies
7.9 Key Performance Indicators (KPIs)
7.60 Case Studies: Success Stories in Naval EAM Management
8.6 Data Modeling for Asset Management
8.2 Database Design for EAM Systems
8.3 Maintenance Process Modeling
8.4 Scenario Simulation and Risk Analysis
8.5 Optimizing asset configuration
8.6 Integration with Geographic Information Systems (GIS)
8.7 Creating dashboards and data visualization
8.8 Asset lifecycle management
8.9 Implementing predictive models
8.60 Case studies: Modeling EAM systems in the naval sector
8.60
7.7 Fundamentals of Naval Asset Management (EAM)
7.2 Introduction to Data Analysis in the Naval Sector
7.3 Importance of EAM and Data in Decision Making
7.4 Key Components of a Naval EAM System
7.7 Types of Data Relevant to Asset Management
7.6 Data Sources: Sensors, Logs, and Databases
7.7 Introduction to Data Analysis Tools
7.8 Key Performance Metrics (KPIs) in Naval EAM
7.9 Benefits of Integrating EAM and Data Analysis
7.70 Case Studies: Real-World Applications in the Naval Industry
8.8 Introduction to EAM and its relevance in naval asset management.
8.8 Data collection and cleaning for analysis in the naval sector.
8.3 Key performance indicators (KPIs) in naval asset management.
8.4 Data analysis tools and techniques applied to EAM.
8.5 Identifying patterns and trends in naval asset data.
8.6 Practical examples of asset optimization using data analysis.
8.7 Introduction to artificial intelligence (AI) and machine learning (ML) in EAM.
8.8 Case study: Applying data analysis to improve efficiency and reduce costs.
8.8 Data visualization and report creation for decision-making.
8.80 Risk and opportunity assessment in asset management.
8.8 Introduction to EAM strategies in the naval sector.
8.8 Selection and implementation of maintenance strategies. 8.3 Application of Data Analytics for Strategic Decision Making
8.4 Optimization of the Naval Asset Lifecycle
8.5 Asset Profitability Analysis
8.6 Use of Data for Maintenance Planning and Scheduling
8.7 Continuous Improvement and Change Management in EAM
8.8 Data Analytics Tools and Software Applied to EAM
8.8 Predictive Modeling and Risk Analysis
8.80 Case Studies: Implementation of EAM and Data Analytics Strategies
3.8 Introduction to EAM System Implementation
3.8 Selection and Evaluation of EAM Systems
3.3 Planning the Implementation of EAM in Maritime Environments
3.4 Design and Configuration of EAM Systems
3.5 Integration of Data from Different Sources into the EAM System
3.6 Training and Development of Personnel in the Use of the EAM System 3.7 Change Management and Adaptation to New Technologies
3.8 Testing and Validation of the Implemented EAM System
3.8 Monitoring and Optimization of EAM System Performance
3.80 Case Study: Successful Implementation of EAM Systems in the Maritime Sector
4.8 Overview of EAM Systems and Their Importance in Naval Management
4.8 Architecture and Key Components of EAM Systems
4.3 Introduction to Databases and Their Application in EAM
4.4 Integration of Data from Sensors and Monitoring Systems
4.5 Use of Data for Maintenance Scheduling and Planning
4.6 Generation of Reports and Dashboards for Decision-Making
4.7 Data Analysis for Resource and Cost Optimization
4.8 Cybersecurity in EAM Systems and Data Protection
4.8 Emerging Trends in EAM and Data Systems 4.80 Case Study: Transforming Naval Management with EAM and Data Systems
5.8 Introduction to Advanced Data Analysis in Naval EAM
5.8 Descriptive and Diagnostic Data Analysis Techniques
5.3 Predictive Analysis for Maintenance Optimization
5.4 Using Machine Learning Algorithms in EAM
5.5 Time Series Analysis and its Application in Asset Management
5.6 Risk Modeling and Simulation in EAM
5.7 Optimizing Inventory and Spare Parts Management
5.8 Integrating Data from Multiple Sources for More Comprehensive Analysis
5.8 Using Business Intelligence (BI) Tools in Data Analysis
5.80 Case Study: Applying Advanced Data Analysis to Naval Asset Excellence
6.8 Introduction to the Integration of EAM and Data Science
6.8 Data Collection and Preparation for Analysis 6.3 Application of Machine Learning algorithms in EAM.
6.4 Development of predictive models for asset management.
6.5 Optimization of predictive maintenance using Data Science.
6.6 Analysis and optimization of asset lifecycles.
6.7 Use of artificial intelligence in decision-making.
6.8 Implementation of Data Science solutions in EAM systems.
6.9 Evaluation of return on investment (ROI) in Data Science projects.
6.10 Case study: Successful integration of EAM and Data Science in the strategic management of naval assets.
7.8 Introduction to EAM management in the naval sector.
7.9 Collection and analysis of naval asset data.
7.3 Optimization of asset lifecycles.
7.4 Maintenance strategies and their application in EAM.
7.5 Use of data in maintenance planning and scheduling. 7.6 Inventory Optimization and Spare Parts Management
7.7 Risk Assessment and Data-Driven Decision Making
7.8 Tools and Software for EAM Management and Optimization
7.8 Continuous Improvement and Change Management in Asset Management
7.80 Case Studies: EAM Management, Data, and Optimization in the Naval Sector
8.8 Introduction to EAM System Modeling
8.8 Modeling Methodologies and Tools
8.3 Data Modeling for Asset Management
8.4 Database Design for EAM
8.5 Simulation and Scenario Analysis in EAM
8.6 Risk and Reliability Analysis in EAM Systems
8.7 Maintenance Planning Optimization
8.8 Use of Predictive Models in Asset Management
8.8 Integration of Modeling with Data Analysis Systems 8.80 Case Study: EAM Systems Modeling and Data Analysis in Naval Asset Management.
9.9 Introduction to Naval Asset Management and the Importance of Optimization
9.9 Overview of EAM Systems and Their Application in the Naval Sector
9.3 Fundamentals of Data Analysis and Their Relevance to Asset Management
9.4 Key Performance Indicators (KPIs) in Naval Asset Management
9.5 Tools and Technologies for Naval Asset Optimization
9.9 Developing EAM Strategies for the Naval Sector
9.9 Applying Data Analytics to Naval Asset Management
9.3 Data Analysis for Decision-Making in Asset Management
9.4 Optimizing the Lifespan of Naval Assets
9.5 Case Studies and Best Practices in the Naval Industry
3.9 Methodologies for Implementing EAM Systems in the Maritime Sector
3.9 Integrating Data from Different Sources for Asset Analysis 3.3 Use of dashboards and visualizations for intelligent asset management.
3.4 Identification and mitigation of risks in naval asset management.
3.5 Planning and execution of EAM implementation projects.
4.9 In-depth study of the most widely used EAM systems in the naval sector.
4.9 Application of Data Analytics for predictive maintenance.
4.3 Use of artificial intelligence and machine learning in asset management.
4.4 Transformation of naval asset management through technology.
4.5 Evaluation and selection of suitable EAM systems for naval management.
5.9 Advanced data analysis techniques for naval asset management.
5.9 Analysis of historical data for asset performance optimization.
5.3 Predictive modeling for maintenance and inventory management.
5.4 Cost optimization and risk reduction in asset management. 5.5 Implementation of data-driven continuous improvement strategies.
6.9 Integration of EAM systems with Data Science for strategic asset management.
6.9 Application of machine learning models for failure prediction and predictive maintenance.
6.3 Data analysis for resource planning optimization.
6.4 Strategies for data-driven decision-making in naval asset management.
6.5 Implementation of a data-driven asset management approach.
7.9 Comprehensive view of EAM management, data, and optimization in the naval sector.
7.9 Application of asset optimization tools and techniques.
7.3 Optimization of inventory and spare parts management.
7.4 Risk analysis and mitigation strategies in naval asset management.
7.5 Performance evaluation and continuous improvement in asset management.
8.9 Modeling of EAM systems for simulation and optimization of asset management. 8.9 Application of data analysis for identifying trends and patterns.
8.3 Development of predictive models for naval asset management.
8.4 Optimization of asset lifespan and cost reduction.
8.5 Implementation of a model- and data-driven asset management approach.
1. EAM Data Analysis for Naval Fleet Optimization: Introduction
2. EAM Data Collection and Preparation for Naval Analysis
3. Descriptive and Diagnostic Analysis of EAM Data in the Naval Environment
4. Predictive Analysis of EAM Data for Naval Maintenance
5. Prescriptive Analysis of EAM Data and Fleet Decision-Making
6. Visualization and Communication of Naval EAM Analysis Results
7. Risk Modeling and Simulation in Naval Asset Management
8. Optimization of Maintenance Planning Based on EAM Data
9. Implementation of EAM Key Performance Indicators (KPIs) in Fleets
10. Final Project: Practical Case Study of EAM Analysis in a Naval Fleet
- 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.
Proyectos tipo capstones
Admisiones, tasas y becas
- 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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