Professional Diploma in Data Science for Ports and Shipping Companies
This professional diploma prepares you to apply data science to the challenges of maritime transport and port operations. You will work with AIS and operational data, forecasting models and visualisation, with a focus on the decisions that data can improve.
Prior knowledge check
These are the skills this programme gives you. Tick the ones you already master and we will tell you which part of the syllabus is for you.
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What is the Professional Diploma in Data Science for Ports and Shipping Companies?
This professional diploma prepares you to apply data science to the challenges of maritime transport and port operations. Throughout the programme you work with AIS data, port-call records, fuel consumption reports, met-ocean data and terminal data. You learn to clean, analyse and model this information in order to improve routes and berth planning, estimate arrival times, control consumption and emissions, and build dashboards that support decision-making in ports, terminals and shipping companies.
What makes the programme different: real cases from the sector built on AIS, port calls, fuel consumption and terminal operations; an applied focus on the actual problems of ports, terminals and shipping companies; tools such as Python, SQL and visualisation software; consumption and emissions analysis using recognised indicators; and your own analytics project for a port or a fleet.
Programme highlights
- Command of maritime data: work with AIS and operational data from ships and ports.
- Optimise berthing, port calls and routes using data.
- Apply predictive models to forecast arrivals and demand.
- Measure fleet fuel consumption, energy efficiency and emissions.
- Career paths in port authorities, terminals and shipping companies.
- Training aimed at more efficient and sustainable ports and fleets.
Syllabus of the Professional Diploma in Data Science for Ports and Shipping Companies
8 modules. Select a module to see its content.
Data in the Maritime and Port Sector
- 1.1. Digitalisation of maritime transport
- 1.2. Stakeholders in the port ecosystem
- 1.3. Port community systems
- 1.4. Maritime single window
- 1.5. Ship and port call data
- 1.6. Terminal data
- 1.7. Metocean data
- 1.8. Data governance and sharing
Data Science Tools
- 2.1. Python for analysis
- 2.2. Pandas and table processing
- 2.3. SQL and databases
- 2.4. Geospatial analysis
- 2.5. Data visualisation
- 2.6. Reproducible notebooks
- 2.7. Business intelligence tools
- 2.8. Project best practices
AIS Data and Traffic Analysis
- 3.1. How the AIS system works
- 3.2. AIS messages and fields
- 3.3. Trajectory cleaning
- 3.4. Detection of port calls and anchorages
- 3.5. Traffic density
- 3.6. Route analysis
- 3.7. AIS limitations and data quality
- 3.8. Enrichment with ship registers
Port Operations Analytics
- 4.1. Port productivity indicators
- 4.2. Waiting and port call times
- 4.3. Berth planning
- 4.4. Congestion and queues
- 4.5. Container terminal productivity
- 4.6. Bulk operations
- 4.7. Land transport and connections
- 4.8. Benchmarking between ports
Predictive Models for Ports and Fleets
- 5.1. Estimated time of arrival prediction
- 5.2. Traffic demand forecasting
- 5.3. Terminal operation times
- 5.4. Predictive maintenance
- 5.5. Time series models
- 5.6. Machine learning models
- 5.7. Model evaluation
- 5.8. Deployment to production
Energy Efficiency and Emissions
- 6.1. Fuel consumption and performance
- 6.2. Effect of weather and loading
- 6.3. Speed optimisation
- 6.4. IMO carbon intensity indicators
- 6.5. Monitoring of emissions during port calls
- 6.6. Onshore power supply
- 6.7. Port emission inventories
- 6.8. Sustainability reports
Dashboards and Decision-Making
- 7.1. Design of key indicators
- 7.2. Operational dashboards
- 7.3. Strategic dashboards
- 7.4. Geospatial visualisation
- 7.5. Alerts and monitoring
- 7.6. Data storytelling
- 7.7. User adoption
- 7.8. Data ethics and privacy
Case Study: Analytics Project for a Port or a Fleet
- 8.1. Problem definition
- 8.2. Identification of data sources
- 8.3. Data preparation
- 8.4. Exploratory analysis
- 8.5. Model development
- 8.6. Evaluation of results
- 8.7. Dashboard
- 8.8. Presentation to management
Duration and format of the Professional Diploma in Data Science for Ports and Shipping Companies
It is 800 hours. Tell us how many you can study each week and we will tell you when you will really finish.
This is the pace at which most students finish. It fits well with the instalment plan.
Academic leadership and faculty
The Navalis teaching team: working professionals who teach and supervise the programmes.
Dra. Marina AlcoverAcademic Director and Head of Quality Assurance
Ing. Diego CifuentesSenior Lecturer in Naval Welding and Non-Destructive Testing; Head of QA/QC for Welded Joints
Cap. Javier Abaroa (MCA)Senior Lecturer in Ship Handling, Electronic Navigation (ECDIS/Radar/ARPA) and GMDSS; STCW Compliance and Bridge Resource Management Coordinator
Dr. Pau FerrerSenior Lecturer in Naval Architecture and Simulation; Director of the Hydrodynamics and CFD Laboratory
Dra. Nuria LlobregatSenior Lecturer in Marine Meteorology, SAR and Operational Safety; Navigation Risk Management Coordinator
Ing. Javier BañulsSenior Lecturer in Electrical Engineering, Automation and ETO Engineering; Electronic Integration and Engine Alarms Coordinator
Ing. Sofía MarquinaSenior Lecturer in Materials, Composites and Corrosion Protection; Coordinator of the Advanced Paints and Coatings Line
Ing. Tomás RieraSenior Lecturer in Propulsion, Alternative Energy and Hybrid Systems; Energy Performance and Vibration CoordinatorObjectives of the Professional Diploma in Data Science for Ports and Shipping Companies
Career opportunities after the Professional Diploma in Data Science for Ports and Shipping Companies
Questions about the Professional Diploma in Data Science for Ports and Shipping Companies
It is not essential; the diploma includes an introduction to Python and SQL applied to industry cases.
From open sources and sample datasets; the limitations and quality of this data are also explained.
Yes. Fuel consumption and carbon intensity indicators defined within the IMO framework are analysed.
Yes. The methods can be applied to fleets and ports of any size.
A complete analytics project for a port or a fleet, with a model and a dashboard.
100% online, with a virtual campus available 24/7, live sessions that are recorded, and tutorials with the teaching staff.
The programme has a workload of 110 hours. You can progress at your own pace within the timetable we give you when you enrol.
Each programme has its own start date, which is shown on this page and confirmed by the admissions team when you request information.
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Admission process
No waiting for a call back. If your documents are in order, you can enrol in the same session.
- Profile: a degree in Nautical Science, Engineering, Economics, Logistics, Statistics, Computer Science or a related field, or professional experience in the maritime and port sector.
- Language: sufficient command of the language of instruction to follow classes and materials. Basic technical English is recommended.
- Technical resources: a computer with a stable connection, an up-to-date browser and a camera for live sessions and project presentations.
- Admission process: online application, review of your profile and a short interview with the academic team.
- 01ApplicationYour details, background and documents.
- 02Instant reviewWe check that everything is complete and in order.
- 03EnrolmentChoose full payment or instalments and reserve your place.
Admission is provisional until the committee validates the documents provided. If you do not meet the requirements, the full amount is refunded.
Ask us before you enrol
Leave your details and admissions will write to you to answer any questions. If you are already sure, you can apply and pay in the same session.