Master’s Degree in Big Data for Commercial Routes and Fleet Optimization
Why this master’s programme?
The Master’s in Big Data for Commercial Routes and Fleet Optimization
This program provides you with the analytical and technological skills to transform data into strategic decisions. Learn to optimize routes, predict demand, and reduce costs in transportation and logistics. Master Big Data, Machine Learning, and Data Visualization tools, applying them to real-world industry cases. This program prepares you to lead the digital revolution in fleet management and global commerce.
Differentiating Advantages
- Predictive Analytics: anticipates market fluctuations and optimizes inventory management.
- Real-Time Route Optimization: minimizes costs and delivery times with advanced algorithms.
- Efficient Fleet Management: maximizes vehicle lifespan and reduces fuel consumption.
- Interactive Data Visualization: presents complex information clearly and concisely.
- Case Studies and Real-World Projects: apply what you’ve learned to concrete challenges in the logistics sector.
- Modality: Online
- Level: Masters
- Hours: 1600 H
- Start date:
Availability: 1 in stock
Who is it aimed at?
- Data analysts and fleet managers looking to optimize routes, reduce costs, and improve operational efficiency through Big Data analytics.
- Logistics and maritime transport professionals interested in applying advanced predictive analytics techniques for strategic decision-making.
- Business consultants and advisors who want to specialize in implementing Big Data solutions for optimizing trade routes.
- Operations directors and supply chain managers who need to visualize and analyze large volumes of data to identify patterns and trends.
- Graduates in statistics, computer science, engineering, or economics seeking specialization in data analytics applied to the maritime sector and Logistics.
Flexibility and applicability
Adapted to the needs of today’s professional: online methodology, practical projects and focus on real-world case studies in the sector.
Objectives and skills
Maximizing efficiency in fleet management:
Implement a predictive maintenance system based on telematics data and vehicle performance analysis to reduce downtime and optimize operating costs.
Implement predictive analytics to optimize routes:
“Develop machine learning models to predict transit times, fuel consumption and weather risks, integrating historical data, current conditions and forecasts, validating their accuracy and adaptability in different operational scenarios.”
Develop cost optimization strategies in the supply chain:
“Implement ABC and Pareto analysis to focus efforts on the products/suppliers with the greatest impact on costs, actively negotiating contracts and conditions.”
Managing and analyzing large volumes of data to improve strategic decision-making:
Implement predictive analytics and machine learning techniques to identify hidden trends and patterns in data, allowing for more accurate anticipation of business risks and opportunities.
Create predictive models to anticipate demand and optimize inventory:
“Implement Machine Learning algorithms (time series, regression, etc.) and adjust parameters to minimize forecast error and reduce storage costs.”
Design data-driven customer segmentation strategies to personalize offers and improve profitability:
“Identify actionable micro-segments, prioritizing those with the greatest value potential and adapting communication to maximize ROI.”
Study plan – Modules
- Fundamentals of predictive analytics applied to commercial routes: definition, importance, and evolution in modern logistics
- Statistical models and machine learning for demand forecasting and resource optimization in commercial fleets
- Processing and cleaning large volumes of data (Big Data): tools and techniques to guarantee the quality and reliability of information
- Route optimization algorithms: from classic methods (Dijkstra, Bellman-Ford) to advanced approaches based on artificial intelligence
- Integration of real-time data: telemetry, IoT sensors, and their impact on dynamic decision-making
- Modeling and simulation of logistics scenarios to validate optimization strategies and anticipate operational contingencies
- Implementation of intelligent fleet management systems (FMS) with predictive and adaptive capabilities
- Optimization Multi-objective: balancing cost reduction, delivery times, energy consumption, and regulatory compliance.
Use of Big Data platforms for advanced geospatial analysis: GIS, clustering, and critical route segments.
Continuous evaluation and improvement: performance metrics, KPIs, and dashboards for monitoring and adjusting logistics operations.
- IoT Fundamentals Applied to Fleet Management: Sensors, Actuators, Communication Protocols, and IoT Network Architecture for Vehicular and Commercial Environments
- Platforms and Frameworks for Integrating IoT Devices with Big Data Systems: Comparative Analysis and Strategic Selection for Real-Time Monitoring
- Design and Implementation of Distributed Architectures for Massive Data Ingestion in Commercial Fleets: Edge Computing, Fog Computing, and Cloud Computing
- Advanced IoT Communication Protocols (MQTT, CoAP, LwM2M) and Their Optimization for Efficient and Secure Fleet Data Transmission
- Data Models and Schemas for Efficient and Scalable Storage of IoT Information on Big Data Platforms: NoSQL Databases, Time-Series Databases, and Data Lakes
- Real-Time Processing Techniques: Using Apache Kafka, Apache Flink, and Spark Streaming for Dynamic Monitoring and Instant Alerts in Fleets Distribution
Integration of machine learning and deep learning algorithms for predictive maintenance: early detection of mechanical failures and route optimization based on IoT pattern analysis
Implementation of digital twin systems for fleets: simulation, monitoring, and prediction of vehicle behavior and operating conditions
Advanced methodologies for IoT and Big Data visualization: customized dashboards and geospatial analysis systems for real-time decision-making
Cybersecurity standards for IoT environments in commercial fleets: authentication, encryption, identity management, intrusion detection, and resilience to attacks
Regulations and legal compliance for the collection and use of real-time data in fleets: privacy, data protection, and specific regulations for the transportation sector
Case studies and analysis of successful implementations of IoT and Big Data solutions in large commercial fleets for operational optimization and continuous improvement
Final comprehensive project: development of a Prototype system for real-time monitoring and predictive maintenance with IoT devices, a Big Data platform, and advanced analytical models.
- Fundamentals and architecture of Big Data applied to the management of commercial routes and fleets: storage infrastructures, distributed processing, and massive data ingestion systems
- Advanced predictive analytics models: multivariate time series, supervised and unsupervised learning to anticipate demand, traffic, and consumer behavior
- Route optimization algorithms: heuristics, metaheuristics (genetic, particle swarm), combinatorial optimization, and mathematical programming applied to minimizing costs and times
- Integration of heterogeneous data sources: IoT in vehicles, telematics sensors, geospatial data, weather, and socioeconomic data to enrich analysis and decision-making
- Application of machine learning and deep learning techniques in fleet data analysis for proactive failure prediction, predictive maintenance, and operational efficiency
- Implementation of Decision Support Systems (DSS)
- Big Data-based solutions for dynamic planning and real-time re-optimization of commercial routes
- Development and use of dashboards and advanced data visualization for continuous monitoring, KPI analysis, and alert generation in fleet management
- Continuous improvement methodologies: deviation analysis, feedback, and adjustment of predictive models to optimize operational processes and reduce uncertainty
- Real-world use cases and scalable solutions that integrate Big Data with ERP, CRM, and TMS systems to transform the supply chain and optimize fleet management
- Ethical, regulatory, and security aspects of handling large volumes of data and information privacy in commercial and logistics environments
- Fundamentals of scalable architectures for Big Data: distribution, parallelism, and fault tolerance in commercial environments
- Design and deployment of data pipelines for massive ingestion: integration of heterogeneous sources in real time and batch processing
- Streaming processing frameworks: Apache Kafka, Apache Flink, and Apache Spark Streaming applied to commercial fleets
- Machine learning models for dynamic route optimization: graph-based algorithms, reinforcement learning, and convex optimization
- Implementation of IoT telemetry systems: smart sensors, MQTT/CoAP protocols, and edge-to-cloud architecture
- Real-time management and analytics platforms: integration with dashboards, predictive alerts, and operational dashboards
- Predictive maintenance based on time series analysis: early detection of mechanical failures using supervised and unsupervised learning
- Horizontal scalability and vertical: container orchestration (Kubernetes, Docker) and design for high availability
Security and privacy in Big Data architectures: data encryption, IoT device authentication, and GDPR and CCPA compliance
Real-world case studies: optimization of pharmaceutical fleets, retail distribution, and urban logistics with Big Data and streaming ML
- Fundamentals and architecture of advanced Machine Learning models for predictive analytics in dynamic business environments
- Design and construction of integrated datasets: data acquisition, cleaning, normalization, and labeling for demand forecasting on trade routes
- Supervised and unsupervised learning techniques applied to identifying consumer behavior patterns and logistics optimization
- Advanced time series models (ARIMA, SARIMA, Prophet, LSTM) for accurate and continuous demand forecasting based on seasonal and external variables
- Application of deep neural networks (DNN) and Recurrent Neural Networks (RNN) architectures to capture temporal dependencies and nonlinear relationships in business data
- Implementation of reinforcement learning algorithms for optimal decision-making in Real-time analysis of fleet allocation and rerouting
Advanced mathematical optimization: integration of predictive models with linear, integer, and metaheuristic programming algorithms for the efficient allocation of logistics resources and optimal commercial routes
Evaluation and validation of models using robust cross-validation techniques, error analysis, ROC curve, and AUC to ensure predictive reliability under changing business conditions
Deployment of machine learning solutions in production environments: use of frameworks such as TensorFlow, PyTorch, and MLOps tools for continuous model maintenance and updates
Practical cases and real-world application in the intelligent optimization of commercial fleets: demonstrations with real datasets and simulations in highly complex operational scenarios
- Advanced Machine Learning fundamentals applied to logistics: supervised, unsupervised, and reinforcement learning for route optimization.
- Design and selection of scalable architectures in Big Data environments: Hadoop, Spark, and distributed systems for massive processing of logistics data.
- Predictive models for optimizing commercial routes: multiple regression, decision trees, Random Forest, and XGBoost models.
- Implementation of deep neural networks for dynamic analysis of traffic and environmental conditions in real time.
- Use of clustering and segmentation techniques for the efficient grouping of vehicles and customers according to demand and geolocation.
- Integration of IoT data and telemetry in fleets: real-time capture, processing, and analysis for predictive maintenance and reduction of operating costs.
- Multi-objective optimization strategies: balancing time, costs, and CO2 emissions using genetic algorithms and optimization convex.
- Implementation of data pipelines in Lambda and Kappa architectures for real-time data ingestion, processing, and analysis in business environments.
- Cloud platforms for scalable Big Data management in logistics: AWS, Google Cloud, and Azure with specialized services in machine learning and fleet analytics.
- Frameworks and tools for real-time monitoring: Apache Kafka, Apache Flink, and alerting systems based on data streaming.
- Development of predictive maintenance models with Deep Learning techniques and time series analysis for anticipating mechanical failures in vehicles.
- Implementation of intelligent dashboards and automated reports for decision-making based on real-time data and critical KPIs.
- Evaluation and validation of models: performance metrics, hyperparameter tuning, and cross-validation strategies to ensure accuracy and robustness.
- Case studies of Real-world studies on route and fleet optimization using Big Data: results analysis, lessons learned, and continuous improvement.
Ethical, privacy, and compliance considerations in the processing of large volumes of commercial and personal data in logistics applications.
[…]
- Fundamentals of advanced predictive analytics: supervised and unsupervised machine learning techniques applied to the optimization of commercial routes
- Modeling and validation of predictive algorithms for demand and consumer behavior in dynamic logistics environments
- Integration of IoT sensors in vehicles and logistics assets: protocols, standards, and data architecture for real-time capture
- Implementation of IoT-based predictive maintenance systems: diagnosing, predicting, and optimizing interventions in operational fleets
- Design and deployment of scalable cloud architectures for massive processing of geospatial data and vehicle telemetry
- Advanced use of Big Data platforms (Apache Spark, Kafka, Hadoop) for the ingestion, storage, and distributed analysis of logistics information
- Comprehensive route optimization using genetic algorithms, stochastic optimization, and combinatorial analytics
- Integration of artificial intelligence for the
- Real-time decision-making, considering external variables such as traffic conditions, weather, and operational constraints
- Implementation of digital twin simulation models for commercial fleets: scenario anticipation and proactive route and maintenance adjustments
- Advanced visualization and real-time dashboards for continuous monitoring and proactive management of logistics operations using BI technologies
- Strategies for efficient asset lifecycle management in commercial fleets, supported by predictive analytics and IoT data
- Data security and governance protocols to ensure integrity, privacy, and regulatory compliance in logistics Big Data environments
- Practical industrial application cases: improvement of logistics KPIs through the synergy between predictive analytics, IoT, and scalable architectures
- Agile methodologies for the implementation and scaling of Big Data projects focused on route optimization and predictive maintenance
- Return on investment assessment (ROI) and quantifiable benefits of adopting advanced technologies for commercial logistics
- Big Data Fundamentals in Fleet Management: Distributed Architecture, Massive Storage, and Cluster Processing
- Machine Learning Models Applied to Commercial Routes: Regression, Classification, Clustering, and Combinatorial Optimization Algorithms
- Integration of Heterogeneous Data: Telemetry, IoT Sensors, ERP, CRM, and External Sources (Weather, Real-Time Traffic)
- Design of Real-Time Analytics Systems: Apache Kafka and Apache Flink Frameworks, and Streaming Processing for Instant Decision Making
- Route Optimization Algorithms: Vehicle Routing Problem (VRP), Metaheuristic Techniques (Genetic, Ant Colony, Simulated Annealing), and Their Practical Application
- Multi-Layer Logistics Optimization: Coordination of Warehouses, Distribution Centers, and Last-Mile Transportation Using Big Data Techniques
- Advanced Fleet Monitoring: Predictive Sensorization, Prescriptive Maintenance Based on Advanced Analytics and failure models
- Intelligent visualization and key KPIs: dynamic dashboards with integrated BI tools, operational performance analysis, and SLA tracking
- Enterprise use cases: design and implementation of pilot projects in the retail, distribution, and freight transport sectors with quantifiable results
- Security, privacy, and regulatory compliance aspects in Big Data projects for fleets: management of sensitive data and GDPR in industrial environments
- Principles and fundamentals of technological innovation applied to Big Data, IoT, and Machine Learning in the context of route optimization and fleet management.
- Advanced IoT systems architecture: design, deployment, and scalability of sensor networks for the massive, real-time collection of vehicle and environmental data.
- Big Data integration strategies in route logistics: capture, distributed storage, and processing of large volumes of data using Hadoop, Spark, and hybrid cloud platforms.
- Development of predictive models based on Machine Learning for the dynamic optimization of business routes: supervised and unsupervised algorithms and reinforcement learning techniques applied to real-time decision-making.
- Implementation of real-time monitoring and supervision systems for commercial fleets: use of intelligent dashboards, telemetry analysis, and proactive alerts for continuous operational improvement.
- Advanced predictive maintenance techniques: vibration analysis, thermography, and lifecycle modeling with artificial intelligence to reduce downtime and operating costs in fleets.
- Comprehensive route optimization using combinatorial optimization algorithms and mathematical logistics: linear programming, heuristic, and metaheuristic models adapted to real business constraints.
- Real-world case studies and benchmarking of disruptive technologies in Big Data, IoT, and Machine Learning applied to logistics efficiency and environmental sustainability.
- Security, privacy, and cybersecurity in connected fleet systems: encryption, authentication, and risk management protocols in IoT networks.
- Future trends and technological evolution: prospective analysis of the convergence between Big Data, IoT, and Machine Learning for the digital transformation of fleet and commercial route management.
- Introduction and Conceptual Framework: Big Data Fundamentals Applied to Logistics and Commercial Fleet Management
- Design and Architecture of Intelligent Systems: Integration of IoT Platforms, Sensors, and Telemetry in Fleets for Real-Time Data Collection
- Advanced Data Preprocessing: Techniques for Cleaning, Normalizing, and Enriching Massive and Heterogeneous Datasets from Fleets and Commercial Routes
- Predictive Models and Advanced Analytics: Application of Supervised and Unsupervised Machine Learning Algorithms for Demand Estimation and Route Optimization
- Combinatorial Optimization and Metaheuristic Algorithms: Development and Implementation of Solutions for Complex Vehicle Routing Problems (VRPs) with Specific Logistics Chain Constraints
- Technological Platforms and Tools: Use of Big Data Frameworks (Hadoop, Spark), NoSQL Databases, and Interactive Visualization Technologies for Fleet Control and Performance Analysis
- AI-based decision support systems: creating dynamic dashboards and predictive alerts to anticipate incidents on commercial routes
- Integration of predictive models with logistics management systems (WMS, TMS): automation and continuous improvement in route planning and resource allocation
- Evaluation, validation, and performance metrics: establishing specific KPIs such as cost per kilometer, delivery time, and emissions reduction, with rigorous statistical analysis
- Practical case studies: design and development of a functional prototype for the predictive optimization of a real company’s logistics chain, applying the concepts integrated throughout the master’s program
Career prospects
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- Data Analyst in Logistics and Transportation: Route optimization, fleet management, and cost analysis.
- Big Data Consultant for Commercial Routes: Advising companies on improving the efficiency and profitability of their logistics operations.
- Fleet Optimization Specialist: Development and implementation of strategies to improve fleet performance and reduce costs.
- Supply Chain Manager: Application of Big Data techniques for supply chain optimization and strategic decision-making.
- Big Data Project Manager in the Logistics Sector: Leading data analysis projects to improve efficiency and profitability in logistics and transportation companies.
- Researcher in the Area of Big Data Data and Logistics: Development of new methodologies and tools for the application of Big Data in the optimization of commercial routes and fleet management.
Route Optimization Software Developer: Creation of software solutions for fleet management and the optimization of commercial routes based on Big Data techniques.
Transportation Risk Analyst: Identification and assessment of risks in the transport of goods through data analysis and the application of predictive models.
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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 anticipate demand and optimize routes.
- Fleet Optimization: Learn to reduce costs and improve efficiency with advanced algorithms and real-time data.
- Advanced Geolocation: Use GIS and spatial analysis tools to identify opportunities and mitigate risks.
- Big Data in Logistics: Transform large volumes of data into effective business strategies and competitive advantages.
- Real-World Case Studies: Apply your knowledge to concrete projects with leading companies in the sector.
Testimonials
I implemented the knowledge gained in the Master’s program to develop a predictive demand model for a logistics company. This resulted in an 18% reduction in transportation costs and a 12% increase in fleet efficiency, exceeding the project’s initial expectations.
During my Master’s degree in Artificial Intelligence & Maritime Big Data, I developed a system for predicting optimal routes for merchant ships, taking into account variables such as weather conditions and real-time maritime traffic. This project, which used machine learning algorithms and processing large volumes of AIS data, achieved a 12% reduction in fuel consumption and travel time in the simulations performed, surpassing existing solutions.
I applied the knowledge I gained from my Master’s in Big Data to optimize our delivery fleet’s routes. The result was an 18% reduction in fuel costs and a 12% increase in delivery efficiency, exceeding the company’s expectations.
I implemented a machine learning model that predicted route demand with 95% accuracy, optimizing our fleet routes and reducing fuel costs by 18% and delivery times by 12%.
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.
- Introduction and Conceptual Framework: Big Data Fundamentals Applied to Logistics and Commercial Fleet Management
- Design and Architecture of Intelligent Systems: Integration of IoT Platforms, Sensors, and Telemetry in Fleets for Real-Time Data Collection
- Advanced Data Preprocessing: Techniques for Cleaning, Normalizing, and Enriching Massive and Heterogeneous Datasets from Fleets and Commercial Routes
- Predictive Models and Advanced Analytics: Application of Supervised and Unsupervised Machine Learning Algorithms for Demand Estimation and Route Optimization
- Combinatorial Optimization and Metaheuristic Algorithms: Development and Implementation of Solutions for Complex Vehicle Routing Problems (VRPs) with Specific Logistics Chain Constraints
- Technological Platforms and Tools: Use of Big Data Frameworks (Hadoop, Spark), NoSQL Databases, and Interactive Visualization Technologies for Fleet Control and Performance Analysis
- AI-based decision support systems: creating dynamic dashboards and predictive alerts to anticipate incidents on commercial routes
- Integration of predictive models with logistics management systems (WMS, TMS): automation and continuous improvement in route planning and resource allocation
- Evaluation, validation, and performance metrics: establishing specific KPIs such as cost per kilometer, delivery time, and emissions reduction, with rigorous statistical analysis
- Practical case studies: design and development of a functional prototype for the predictive optimization of a real company’s logistics chain, applying the concepts integrated throughout the master’s program
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.