Master’s Degree in Big Data and Ocean Route Analytics
Why this master’s programme?
The Master’s in Big Data and Ocean Route Analytics
Prepares you to lead the digital revolution in the maritime industry. Learn to extract value from the data generated by ships, ports, and navigation systems to optimize routes, reduce costs, and improve safety. Master the tools of Big Data, Machine Learning, and Data Visualization applied to efficient fleet management and predictive analysis of ocean behavior.
Key Advantages
- Practical Application: Development of real-world projects with maritime datasets.
- Specialized Software: Management of route analysis and simulation platforms.
- Predictive Modeling: Anticipate risks and optimize fuel consumption.
- Strategic Vision: Make data-driven decisions for more efficient navigation.
- Networking: Connect with industry experts and leading maritime technology companies.
- Modality: Online
- Level: Masters
- Hours: 1600 H
- Start date:
Availability: 1 in stock
Who is it aimed at?
- Logistics and maritime transport professionals seeking to optimize routes and reduce costs through data analysis.
- Data analysts and data scientists interested in specializing in the maritime sector and the analysis of large volumes of oceanographic information.
- Naval engineers and merchant marine officers wishing to acquire advanced skills in the use of Big Data tools for strategic decision-making.
- Shipping companies and port operators seeking to improve the efficiency of their operations through predictive analytics and route optimization.
- Consultants and technology solution providers for the maritime sector seeking to expand their expertise in the area of Big Data and Analytics.
Flexibility and applicability
Designed for working professionals: online methodology, real-world case studies, and immediate application of the knowledge acquired.
Objectives and skills
Optimizing the efficiency of maritime transport:
“Plan the route, considering currents, tides, weather and port regulations, to minimize transit time and fuel consumption.”
Predicting and mitigating risks in navigation:
Interpret meteorological and oceanographic information to adjust navigation planning and execution, minimizing exposure to adverse conditions and optimizing routes in real time.
Develop predictive models to optimize resources and reduce costs:
“Implement machine learning algorithms to forecast demand, optimize inventories, and predict machinery failures.”
Extracting valuable information from maritime data for strategic decision-making:
“Analyze AIS, weather, and traffic data to optimize routes, assess risks, and improve operational efficiency, communicating findings to management and relevant teams.”
Design fleet management strategies based on advanced data analysis:
“Identify key KPIs, build predictive models, and optimize routes/maintenance to reduce costs and improve operational efficiency.”
Implement logistics optimization solutions to improve the maritime supply chain:
“Analyze bottlenecks in port terminals and propose decongestion strategies, considering the optimization of resources (cranes, personnel) and coordination with customs agents and shipping companies.”
Study plan – Modules
- Introduction to predictive models in ocean navigation: theoretical foundations and practical applications
- Advanced machine learning algorithms applied to maritime Big Data: regression, classification, neural networks, and ensemble models
- Route optimization with artificial intelligence techniques: genetic algorithms, particle swarm optimization, and heuristic methods
- Time series analysis and prediction of ocean conditions: phenology, climate patterns, swell, and visibility
- Integration of multisensor data: AIS, radar, satellites, meteorological and oceanographic sensors for robust predictive modeling
- Implementation of early warning systems using anomaly detection algorithms and probabilistic models in navigation
- Risk assessment and management using predictive techniques: collisions, groundings, and extreme weather events
- Use
- Explainable Artificial Intelligence (XAI) for the interpretation and validation of results in navigation decisions
- Simulation and modeling of dynamic scenarios for real-time decision-making: frameworks and specialized tools
- Optimization of energy consumption and minimization of the environmental footprint through predictive models and adaptive algorithms
- Cybersecurity applied to predictive and navigation systems: intrusion detection, encryption, and resilience protocols
- Practical implementation: development of data pipelines, model training, and deployment in real-time maritime environments
- Real-world case studies and benchmarking of algorithms in route optimization and maritime safety under different oceanographic conditions
- Critical evaluation and performance metrics for predictive models in ocean Big Data: accuracy, recall, F1 score, and ROC curves
- Emerging trends and future perspectives in AI and analytics applied to navigation: computing quantum computing, edge computing, and maritime digital twins
- Big Data Fundamentals Applied to Maritime Navigation: Characteristics, Volume, Speed, and Variety of Ocean Data
- Distributed Processing Architectures: Hadoop, Spark, and Flink for Efficient Large-Scale Maritime Data Management
- Integration of Heterogeneous Data Sources: AIS, Satellites, Oceanographic and Meteorological Sensors on Big Data Platforms
- Advanced Preprocessing of Maritime Data: Cleaning, Normalization, and Enrichment to Improve Analysis Quality
- Advanced Supervised and Unsupervised Machine Learning Techniques for Predicting Route Patterns and Maritime Events
- Hybrid Predictive Models Applied to Ocean Route Optimization: Random Forest, Neural Networks, and Gradient Boosting
- Application of Deep Learning for the Early Detection of Maritime Traffic Anomalies and Adverse Oceanographic Conditions
- Development of dynamic route management systems: real-time optimization algorithms and data-driven decision-making
- Implementation of data pipelines for real-time analytics: ingestion, processing, and visualization on navigation platforms
- Risk assessment and control using artificial intelligence: collision prediction, congestion zones, and extreme weather conditions
- Advanced visualization tools for maritime Big Data: geospatial dashboards and interactive maps applied to route management
- Ethical and cybersecurity considerations in the predictive analysis of sensitive data in international navigation
- Case studies and market research: application of Big Data and Machine Learning for shipowners and maritime logistics operators
- Implementation of scalable and resilient systems for continuous ocean route management using cloud technologies
- Future perspectives and emerging trends in Big Data and applied predictive analytics to global maritime navigation and logistics
- Mathematical and statistical foundations in predictive models applied to ocean routes: regression, classification, and time series
- Advanced machine learning algorithms: deep neural networks, gradient boosting, and support vector machines for predicting maritime conditions
- Integration of maritime big data: collection, cleaning, and analysis of data from sensors, AIS, meteorology, and oceanography
- Route optimization using evolutionary and heuristic algorithms: genetic algorithms, particle swarming, and simulated annealing
- Modeling and simulation of navigational risks: identification and quantification of operational and environmental threats
- Application of AI-based early warning systems for proactive navigational safety management
- Use of reinforcement learning techniques for autonomous decision-making in route direction and critical maneuvers
- Evaluation of energy efficiency and emissions reduction through algorithmic optimization of maritime routes
Implementation of operational intelligence platforms and visualization dashboards for real-time monitoring of ocean fleets
Interoperability standards and protocols for integrating predictive models into Vessel Traffic Systems (VTS) and Electronic Navigational Dispatch Systems (ECDIS)
International regulatory and normative considerations applicable to artificial intelligence and data analysis in the maritime sector
Real-world case studies and practical application of predictive models in ocean route optimization and safety
- Fundamentals of Advanced Simulation: Mathematical Models, Numerical Methods, and Algorithms Applied to Ocean Navigation
- Concept and Architecture of Digital Twins: Creation, Synchronization, and Real-Time Updating
- Integration of Big Data in Digital Twins: AIS Sources, Meteorology, Oceanography, and Onboard Sensors
- Development of Scenarios for Operational Planning: Predictive Route Analysis, Speed Optimization, and Energy Consumption
- Simulation of Dynamic Oceanic Conditions: Tides, Currents, Winds, Waves, and Their Impact on Trajectories
- Application of Digital Twins in Risk Management and Incident Response: Early Detection, Mitigation, and Automated Response Protocols
- Real-Time Tactical Visualization: Interactive Platforms, Augmented Reality, and Virtual Reality for Decision-Making on the Bridge
- Advanced Analysis and Reporting Tools Post-simulation: KPI extraction, deviation reports, and operational lessons learned
Automation and decision support using artificial intelligence: integration with simulation and digital twins for adaptive responses
Case studies and integrated exercises in simulated environments: route planning, environmental emergencies, and resource optimization in complex ocean scenarios
- Fundamentals of maritime sensors: types, operating principles, and technical specifications
- Integration of geolocation systems: GNSS, DGPS, GLONASS, and Galileo in ocean environments
- Architecture of multisensor platforms: design, assembly, and real-time data synchronization
- Signal processing: filtering, calibration, and validation of data from inertial and acoustic sensors
- Advanced algorithms for sensor fusion: Kalman, particle filter, and machine learning techniques applied to maritime navigation
- Dynamic geospatial visualization: implementation of GIS systems for ocean data representation and analysis
- Predictive route modeling: statistical analysis and Big Data-based simulation for trajectory optimization
- Development of early warning systems: parametric and AI-based for real-time decision-making real
- Maritime communication protocols: NMEA 0183/2000 standards, integration with ECDIS and marine IoT networks
- Cybersecurity in the transmission and storage of sensor data: strategies and regulations applicable in maritime environments
- Uncertainty analysis and error management in maritime positioning systems
- Big Data tools for advanced analytics: Hadoop, Spark, and cloud platforms geared towards ocean data
- Practical implementation: integration workshops and simulation of real-time navigation scenarios
- Case studies: application of integrated systems on ocean routes to improve safety and operational efficiency
- Regulatory and legal aspects: compliance with SOLAS, IMO, and international guidelines for advanced navigation systems
- Fundamentals of predictive modeling applied to ocean routes: statistical theory, supervised and unsupervised learning, and advanced regression methods
- Design and development of machine learning algorithms for predicting adverse maritime conditions and critical events at sea
- Integration and processing of multisensor data: acquisition, cleaning, and normalization of information from radar, AIS, satellite meteorology, and in-situ meteorological sensors
- Digital simulation models for navigation: Monte Carlo techniques, agent-based simulation, and deep neural networks to anticipate operational scenarios and risks
- Route optimization through predictive analytics: use of reinforcement learning for dynamic trajectory selection based on safety and fuel efficiency criteria
- Implementation of early detection and real-time warning systems using advanced analytics and online learning of oceanographic and meteorological data
- Evaluation and mitigation of operational risks using predictive models Integrated with Maritime Safety Management Systems (MSMS) and advanced ECDIS platforms
Studies of correlation and causality between environmental variables and navigation performance using multivariate analysis techniques and big data
Development of intelligent dashboards with advanced visualization for decision-making based on artificial intelligence and cloud processing
Case studies and practical applications: implementation of predictive solutions in commercial fleets and scientific expeditions, with analysis of results and ROI (return on investment)
- Advanced Big Data Fundamentals in Ocean Navigation: Data Structures, Distributed Storage, and Real-Time Processing
- Predictive Models Applied to Ocean Routes: Multivariate Regression, Deep Neural Networks, and Support Vector Machines for Predicting Maritime Conditions
- Digital Simulation of Maritime Scenarios: Monte Carlo Techniques, Agent-Based Modeling, and Discrete Event Simulations for Trajectory Optimization
- Integration of Multi-Source Data: AIS Sensors, Satellites, Oceanographic Buoys, and Meteorological Data for Holistic Voyage Analysis
- Route Optimization Using Evolutionary Algorithms and Metaheuristics: Genetic Algorithms, Particle Swarms, and Simulated Annealing Applied to Efficient Planning
- Risk and Safety Analysis in Navigation: Probabilistic Modeling, Failure Analysis, and Early Warning Systems Based on Machine Learning
- Advanced Data Visualization and Simulations: GIS tools, interactive dashboards, and augmented reality for real-time decision-making
Environmental impact and energy consumption assessment: predictive modeling of fuel consumption based on oceanic and meteorological variables
Implementation of Big Data Analytics in operational management: data pipeline, ETL, data lake storage, and use of cloud platforms for scalability
Practical case studies and integrated projects: development of predictive models and simulations for safe and efficient navigation in complex real-world scenarios
- Fundamentals of Big Data Visualization: Key Concepts, Scalability, and Performance in Maritime Environments
- IoT System Architectures for Ocean Monitoring: Distributed Sensors, Communication Networks, and Specific Protocols (MQTT, CoAP, LwM2M)
- Integration and Real-Time Processing of Multi-Source Data: AIS, Radar, Weather Sensors, GNSS Positioning Systems, and Onboard Devices
- Advanced Big Data Storage and Management Models: NoSQL Databases, Data Lakes, and Edge Computing Solutions in a Maritime Context
- Dynamic and Adaptive Visualization: Development of Tactical and Strategic Dashboards for Decision-Making in Ocean Navigation
- Predictive Analytics Algorithms Applied to Maritime Routes: Anomaly Detection, Oceanographic Pattern Prediction, and Route Optimization
- Implementation of IoT Platforms for the Continuous monitoring: architecture, scalability, security, and maintenance of onboard systems
Secure communication protocols for real-time data transmission: encryption, authentication, and resilience against cyberattacks
Integration of geospatial analysis with GIS systems for visualizing maritime traffic and oceanographic conditions
Machine learning and deep learning tools for advanced interpretation of sensor data and intelligent decision-making on the bridge
Case studies: implementation of IoT and Big Data solutions for real-time fleet monitoring and their impact on reducing operating costs and risks
Impact assessment and performance metrics of visualization and monitoring systems in a real-world environment: key KPIs and continuous improvement
International regulations and standards applicable to maritime data transmission and visualization: compliance and best practices
Development of skills for the critical interpretation of dashboards and strategic reporting applied to navigation Oceanic
- Emerging challenges and future trends in big data visualization and IoT platforms geared towards smart maritime management
- Fundamentals of predictive models in ocean navigation: regression, decision trees, SVM, and deep neural networks applied to maritime routes
- Multi-objective optimization for route planning: genetic techniques, evolutionary algorithms, and stochastic optimization under dynamic constraints
- Digital simulation and agent-based modeling (ABM) for maritime traffic scenarios and emergency response
- Advanced integration of satellite, meteorological, and oceanographic data: preprocessing, cleaning, and fusion to improve prediction accuracy
- Implementation of interactive 3D visualization systems: marine GIS, real-time dashboards, and VR for route and risk management
- Dynamic risk management: predictive models of adverse events, probabilistic analysis, and automated alerts for navigational safety
- Application of machine learning for predictive ship maintenance and energy consumption optimization in Ocean voyages
Use of Digital Twins in the simulation and optimization of maritime operations: design, validation, and application under variable conditions
Advanced programming in Python and R for the development and implementation of analytical models in maritime Big Data
Continuous evaluation and validation of predictive models: metrics, overfitting, underfitting, and improvement strategies in real-world scenarios
- Theoretical and methodological foundations of Big Data applied to ocean routes: data architecture, distributed frameworks, and scalable storage systems
- Development and implementation of predictive models for maritime route optimization: advanced regression, deep neural networks, and supervised and unsupervised machine learning techniques
- Digital simulation in ocean environments: stochastic simulation methods, agent-based simulation, and the use of digital twins to replicate operational scenarios
- Integration of heterogeneous data sources: AIS, marine meteorology, ocean currents, onboard IoT sensors, and satellite data for real-time analysis
- Development of multi-objective optimization algorithms: minimizing fuel consumption, reducing travel times, and managing operational risks
- Advanced real-time data visualization systems: dynamic dashboards, interactive maps 3D and geospatial analysis applied to safety and efficiency in navigation
Real-time processing platforms: use of Apache Kafka, Spark Streaming, and edge computing technologies for immediate processing of large volumes of maritime data
Architecture and implementation of an integrated monitoring and early warning system based on artificial intelligence for incident prevention on ocean routes
Methodologies for system evaluation and validation: performance metrics, testing in simulated environments, and impact assessment on real maritime operations
Cybersecurity and data protection considerations in maritime Big Data platforms: protocols, authentication, integrity, and access management in critical systems
Technical documentation, reporting, and presentation of project results: comprehensive reports, technical visualizations, and executive presentations geared toward stakeholders in the naval sector
Master’s thesis planning: timeline, technological resources, multidisciplinary team, and goal setting Achievable
Review and critical analysis of real-world case studies on the optimization and safety of ocean routes using Big Data and advanced analytics
Value proposition and opportunities for technological innovation in the maritime industry through the use of integrated predictive systems
Future perspectives and emerging trends in Big Data and analytics applied to ocean navigation systems: automation, complete digitalization, and operational sustainability
Career prospects
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- Data Analyst in Shipping Companies and Maritime Transport Companies: route optimization, fuel consumption analysis, demand forecasting.
- Maritime Logistics Consultant: improving supply chain efficiency, optimizing fleet management, developing cost reduction strategies.
- Maritime Market Intelligence Specialist: analyzing market trends, identifying business opportunities, risk assessment.
- Software Developer for the Maritime Industry: creating data analysis tools, designing predictive models, developing data visualization systems.
- Maritime Researcher: modeling ocean routes, studying the environmental impact of maritime transport, developing new technologies for navigation.
- Risk Manager in Marine Insurance Companies: data analysis for risk assessment Risk assessment, development of loss prediction models, and optimization of insurance policies.
Data Analyst in Ports and Maritime Terminals: Optimization of maritime traffic management, improvement of port operations efficiency, and maritime safety analysis.
Maritime Sustainability Expert: Data analysis for emissions reduction, development of energy efficiency strategies, and implementation of sustainable practices in maritime transport.
“`
Entry requirements
Academic/professional profile:
Bachelor’s degree in Nautical Science/Maritime Transport, Naval/Marine Engineering or a related qualification; or proven professional experience on the bridge/in operations.
Language proficiency:
Functional Maritime English (SMCP) recommended for simulations and technical materials.
Documentation:
Updated CV, copy of qualification or seaman’s book, national ID/passport, motivation letter.
Technical requirements (for online):
Device with camera/microphone, stable internet connection, monitor ≥ 24” recommended for ECDIS/Radar-ARPA.
Admissions process and dates
Online
application
(form + documents).
Academic review and interview
Admissions decision
Admissions decision
(+ scholarship offer if applicable).
Place reservation
(deposit) and enrolment.
Induction
(access to the virtual campus, calendars, simulator guides).
Scholarships and financial support
- Predictive Analytics: Master machine learning techniques to optimize routes and reduce operating costs.
- Data Visualization: Learn to create interactive dashboards for agile and efficient decision-making.
- Big Data Applied to the Maritime Sector: Discover leading tools and platforms for processing large volumes of data.
- Route Modeling: Develop predictive models to minimize risks and maximize the efficiency of maritime transport.
- Professional Certification: Obtain a certification that will set you apart in the maritime sector job market.
Testimonials
I applied the knowledge I gained from my Master’s degree in Big Data and Ocean Route Analytics to optimize the routes of my company’s cargo fleet. Predictive analysis of ocean currents and weather patterns, combined with machine learning models for fuel management, resulted in a 12% reduction in transportation costs and an 8% decrease in CO2 emissions in the first quarter of implementation.
During my Master’s degree in Naval Technological Research and Innovation, I developed an optimization algorithm for propeller design that reduces fuel consumption by 12% and cavitation by 5%, validated through CFD simulations and towing tank tests. This project, which culminated in a scientific publication, allowed me to apply the knowledge I acquired and contribute to improving energy efficiency in the naval sector.
I applied the knowledge from the Master’s program to optimize the routes of my company’s cargo fleet, reducing delivery times by 12% and fuel consumption by 8%, which generated annual savings of $2 million.
I applied the knowledge I gained in my Master’s program to optimize the routes of my company’s cargo fleet. The new predictive model, based on analysis of ocean currents and real-time weather data, reduced travel times by 12% and fuel consumption by 8%, generating annual savings of $2.5 million.
Frequently asked questions
Yes. The itinerary includes ECDIS/Radar-ARPA/BRM with harbor, ocean, fog, storm, and SAR scenarios.
Online with live sessions; hybrid option for simulator/practical placements through agreements.
Recommended functional SMCP. We offer support materials for standard phraseology.
Yes, with a relevant degree or experience in maritime/port operations. The admissions interview will confirm suitability.
Optional (3–6 months) through Companies & Collaborations and the Alumni Network.
Simulator practice (rubrics), defeat plans, SOPs, checklists, micro-tests and applied TFM.
A degree from Navalis Magna University + operational portfolio (tracks, SOPs, reports and KPIs) useful for audits and employment.
- Theoretical and methodological foundations of Big Data applied to ocean routes: data architecture, distributed frameworks, and scalable storage systems
- Development and implementation of predictive models for maritime route optimization: advanced regression, deep neural networks, and supervised and unsupervised machine learning techniques
- Digital simulation in ocean environments: stochastic simulation methods, agent-based simulation, and the use of digital twins to replicate operational scenarios
- Integration of heterogeneous data sources: AIS, marine meteorology, ocean currents, onboard IoT sensors, and satellite data for real-time analysis
- Development of multi-objective optimization algorithms: minimizing fuel consumption, reducing travel times, and managing operational risks
- Advanced real-time data visualization systems: dynamic dashboards, interactive maps 3D and geospatial analysis applied to safety and efficiency in navigation
Real-time processing platforms: use of Apache Kafka, Spark Streaming, and edge computing technologies for immediate processing of large volumes of maritime data
Architecture and implementation of an integrated monitoring and early warning system based on artificial intelligence for incident prevention on ocean routes
Methodologies for system evaluation and validation: performance metrics, testing in simulated environments, and impact assessment on real maritime operations
Cybersecurity and data protection considerations in maritime Big Data platforms: protocols, authentication, integrity, and access management in critical systems
Technical documentation, reporting, and presentation of project results: comprehensive reports, technical visualizations, and executive presentations geared toward stakeholders in the naval sector
Master’s thesis planning: timeline, technological resources, multidisciplinary team, and goal setting Achievable
Review and critical analysis of real-world case studies on the optimization and safety of ocean routes using Big Data and advanced analytics
Value proposition and opportunities for technological innovation in the maritime industry through the use of integrated predictive systems
Future perspectives and emerging trends in Big Data and analytics applied to ocean navigation systems: automation, complete digitalization, and operational sustainability
Request information
Complete the Application Form.
Attach your CV/degree certificate (if you have it to hand).
Indicate your preferred cohort (January/May/September) and whether you would like the hybrid option with simulator sessions.
An academic advisor will contact you within 24–48 hours to guide you through the admission process, scholarships, and compatibility with your professional schedule.
Faculty
Eng. Tomás Riera
Full Professor
Eng. Tomás Riera
Full Professor
Naval engineer specializing in the selection, integration, and optimization of propulsion systems for yachts, high-speed craft, and service vessels.
Eng. Sofía Marquina
Full Professor
Eng. Sofía Marquina
Full Professor
Materials engineer specializing in marine composites and corrosion protection systems for ships, yachts, and port structures.
Eng. Javier Bañuls
Full Professor
Eng. Javier Bañuls
Full Professor
Naval electrical engineer with over fifteen years of experience in the design, integration, and operation of power and automation systems.
Dr. Nuria Llobregat
Full Professor
Dr. Nuria Llobregat
Full Professor
Specialist in meteorological and operational decision-making for coastal and offshore navigation, integrating wind, wave, and current models.
Dr. Pau Ferrer
Full Professor
Dr. Pau Ferrer
Full Professor
Naval engineer with a PhD in applied hydrodynamics and over a decade of experience designing high-performance hulls and marine structures.
Cap. Javier Abaroa (MCA)
Full Professor
Cap. Javier Abaroa (MCA)
Full Professor
MCA-certified captain with command of ocean-going vessels, specializing in advanced maneuvering, navigation management, and bridge safety.