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Course · 300 h · 5 modules
Course in Ocean Forecasting with Machine Learning
The Course in Ocean Forecasting with Machine Learning Prepares you to build machine learning models that forecast ocean variables such as waves, sea surface temperature and sea level. You will learn to prepare time series and spat
Duration300 h
FormatOnline
Starts21 December
Places20 / 20
Prior knowledge
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Programme overview
What is the Course in Ocean Forecasting with Machine Learning?
The Course in Ocean Forecasting with Machine Learning
Prepares you to build machine learning models that forecast ocean variables such as waves, sea surface temperature and sea level. You will learn to prepare time series and spatial data, train tree-based, gradient boosting and neural network models, compare them with benchmark numerical forecasts and validate them rigorously, gaining a clear understanding of both their strengths and their limits compared with physics-based models used by operational services today.
Key advantages
- Practical focus: Models trained on open ocean data.
- Honest comparison: Results benchmarked against physics-based forecasts.
- Rigorous validation: Metrics and validation schemes suited to time series.
- Open tools: Python, scikit-learn and deep learning libraries.
- Case study: A forecasting model for a real variable and location.
Programme type
Course
Study mode
Online
Duration
300 hours
Modules
5
Programme highlights
- Prepare the data: Build ocean training datasets.
- Train models: Apply machine learning and neural networks.
- Forecast the sea: Predict waves, temperature and sea level.
- Validate rigorously: Evaluate with suitable metrics and benchmarks.
- Boost your career: Open doors to ocean services and data companies.
- Train to combine data and artificial intelligence in ocean forecasting.
Curriculum
Programme Structure
The programme is structured in 5 modules, listed below in order of study.
01
Foundations of Ocean Forecasting
- Ocean variables that are forecast
- Benchmark numerical forecasting
- Classical statistical approaches
- Machine learning for forecasting
- Forecast horizons and scales
02
Preparing Data for Forecasting
- Buoy and satellite data sources
- Time series and sliding windows
- Feature engineering
- Time-based data splitting
- Normalisation and missing values
03
Machine Learning Models
- Linear regression and regularisation
- Decision trees and random forests
- Gradient boosting
- Hyperparameter tuning
- Feature importance
04
Neural Networks for the Ocean
- Dense neural networks
- Recurrent networks and LSTMs
- Convolutional networks for spatial fields
- Correcting numerical model outputs
- Probabilistic forecasting
05
Validation and Application
- Error and skill metrics
- Comparison with benchmark forecasts
- Extreme events and model limits
- Deploying a simple model
- Case study: forecasting waves on a stretch of coast
Workload
Study Time and Workload
300 hours of study, online and at your own pace. Move the slider to the hours a week you can really put in.
Hours a week8
Weeks to finish—
You finish around—
Who it is for
Graduates in marine science, physics, mathematics or engineering: who want to apply machine learning to the ocean Data scientists: interested in environmental and marine problems Ocean and weather forecasters: who want to explore new techniques Marine energy and port professionals: who need local forecasts Researchers: working with ocean time series
Faculty
Academic direction and faculty
The teaching team at Navalis: working professionals who deliver and oversee the institute's programmes.
Eng. Diego Cifuentes
Cap. Javier Abaroa (MCA)
Dr. Pau Ferrer
Dr. Nuria Llobregat
Eng. Javier Bañuls
Eng. Sofía Marquina
Eng. Tomás Riera
Verified reviews
Student Reviews
Reviews collected and verified by Opynio, not written by us.
Outcomes
Learning Outcomes
- 01Understand ocean forecasting
- 02Prepare data for forecasting
- 03Train classical models
- 04Apply neural networks
- 05Validate forecasts
- 06Interpret the limits
Careers
Career Opportunities
01Ocean data scientist: Forecasting models at companies and research centres.
02Ocean forecasting technician: New techniques in operational services.
03Marine energy data analyst: Forecasts for offshore operations.
04Research assistant: Projects applying AI to the ocean.
Questions
Entry Requirements
Yes, a basic level is recommended; the course does not start from zero in programming.
No. The course shows that they are usually complementary and examines the limits of each approach.
Mainly waves, sea surface temperature and sea level, using open data.
No. The exercises can be done in free cloud environments.
Building and validating a wave forecasting model for a real coastal location.
100% online, with a virtual campus available 24/7, live sessions that are recorded, and tutorials with the teaching staff.
The programme involves 300 hours of study. You can work at your own pace within the timetable we give you when you enrol.
Each programme has its own start date, shown on this page and confirmed by our admissions team when you enquire.
On completing the programme you receive the Course in Ocean Forecasting with Machine Learning qualification awarded by Navalis Magna Institute.
Online admissions
Admissions Process
No waiting for a phone call. If your documents are complete, you can pay in the same session.
Entry requirements
Entry profile: Students or graduates in marine science, physics, mathematics, statistics, computer science, engineering or related fields, with basic Python skills. Language: A good working level of English to follow classes and materials. Technical requirements: A computer with a stable internet connection, an up-to-date browser and a webcam for live sessions and project presentations. Admissions process: Online application, profile review and a short interview with the academic team. We confirm your start date when you enquire.
Questions?
Frequently Asked Questions
Leave your details and admissions will write back. If you already know this is the one, go straight to the application and pay in the same session.