Short Course on Marine Data Literacy

Sunday, 3rd December 2023

Monday, 4th December 2023

Tuesday, 5th December 2023

Wednesday, 6th December 2023

Thursday, 7th December 2023

Friday, 8th December 2023

Saturday, 9th December 2023

• Why do we need marine data?
• How do we measure and model the sea?
• Different types of marine data: physical, biogeochemical, ecological; models; observations (in situ + remote)
• What is operational oceanography?
• Technologies used to produce marine data
• Temporal and spatial constraints in data collection and interpretation; how to measure and interpret data avoiding pitfalls
• The value addition chain of data; downstream services and data product delivery

• Met-ocean data: types and characteristics
• Types of products PHYS CMEMS
• Types of products INSITU CMEMS
• "Data lexicon": Quality information document and product user manual

• What oceanographic data is freely available? How can this be accessed?
• Demonstration of professional online data interfaces to visualise in near-real-time ocean data products
• Portals from where numeric data derived from in-situ measurements, remote sensing, and forecasting models, can be downloaded
• Demonstration of visualisation software including Panoply, the Sentinel Application Toolbox (SNAP), and QGIS, that can be used to process downloaded data
• Simple data processing techniques to added-value products (such as colour composites, vegetation and water quality indices, etc…) 

• What is a model?
• Transport of a property: Eulerian vs Lagrangian
• Discretization in space and time
• Model limitations
• Initial and boundary conditions
• Downscaling
• Types of models
• Operational modelling cycle
• Data assimilation, analysis and reanalysis
• Model validation

• Data management tools, principles and technologies deployed by EMODnet
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• DCO Data Sharing, Decade's Data and Information Strategy and related initiatives

• Brief introduction to web scraping and API
• Brief introduction to data wrangling
• Interacting with data servers part 1 (openDAP)
• Introduction to cloud storage and cloud computing

• Statistical methods
• Time series analysis
• Spatial analysis of data fields
• Climatological assessments

• Introduction
• What is Artificial Intelligence? Demystifying the hype surrounding AI
• AI needs huge amounts of data to operate, and oceanographic data is readily available
• Overview of different AI applications used in oceanography

• Syntax
• Variables
• Data types
• Basic operations
• Loops
• Conditional statements

• IT tools and systems
• Relational Database Management Systems
  • Data model
  • Basic properties of transactions in RDMS
• Geo-Information systems
  • Spatial, temporal, and spatio-temporal data models
  • NoSQL Databases
  • Data models
  • Cap-theorem (consistency and availability)

• Data Preprocessing
• Overview on Distributed Data Processing
  • Hadoop, map-reduce
  • Spark
  • Flink

• What is unsupervised learning?
• Distinction between clustering and classification
• Cluster properties and basic approaches to cluster analysis
• Examples of some algorithm and applications
• What is supervised learning?
• Distinction between classification and regression problem
• Learning algorithms and why are the introduced
• Learning as an optimization problem. Gradient descent. Local optimum
• Linear separability and complexity of neural network architecture
• Simple (shallow) neural network implementation

• Functions
• Modules
• Libraries