The GAIN project is committed to the ecological intensification of aquaculture by using data to guide correct decision that balances the need for protein to feed a growing population with the need to protect our natural environments. Central to this development is using data for improved management of aquaculture operations. This course provides a deep dive on data analysis techniques and methodologies for aquaculture. We specifically deal with some of the key aspects of aquaculture datasets such as time series data and implications, handling data quality issues, and applying statistical and machine learning techniques to data.

In the course we give an overview of a precision aquaculture framework. This necessitates a system for ingestion, curation, processing, and analysis of data coming from farms (and other pertinent external data streams such as weather, satellite, and open ocean). We briefly cover aspects related to the ingestion and curation of data streams - here we also give a description of aquaculture farms and what data might be generated there.

We focus on data processing and analysis methodologies and how these can contribute to the more effective management of aquaculture farms. Borrowing from Marc Andreessen's - now decade-old - essay on Why Software Is Eating the World, the course focuses on how innovative statistical and machine learning approaches can address aquaculture management pain points