The specific challenges in aquaculture today reveal needs and problems that must be addressed appropriately and in sync with the most recent optimization methods. It is now the time to bring the techniques of aquaculture to a new level of development and understanding. In that, one must consider the state of the art methods of statistics and data mining that permit a deeper insight into the aquaculture reality through the collected datasets, either from daily data or from sampling to sampling data. This must also be tuned to the expert knowledge of the fish farmers, their procedures and technology in use today. In this paper we review the state of the art of data analytics methodology in aquaculture, the data available deriving from the procedures characteristic to this business, and propose mathematical models that permit a deeper insight on the data. We also address the data unknowns and strategies developed that will contribute to the success of the business, leading to discover valuable information from the data that can be made usable, relevant and actionable.