Diploma in EAM and Data Systems for Asset Management
About us 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
- Format: Online
- Duration: 8 months
- Hours: 900 H
- Language: ES / EN
- Credits: 60 ECTS
- Registration date: 04-07-2026
- Strat date: 14-08-2026
- Available places: 7
1.449 $
Competencias y resultados
Qué aprenderás
1. EAM Optimization and Data Analysis for Naval Asset Management
Para quien va dirigido nuestro:
Diploma in EAM and Data Systems for 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.
Proyectos tipo capstones
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