The 54th International Liège Colloquium has focused on the topic “machine learning and data analysis in oceanography”. Data-driven approaches to understand and predict the ocean dynamics have gained significant traction in recent years thanks to the increased number, coverage and quality of ocean observations; improved numerical ocean modeling; the availability of massively parallel computing devices (like graphical processing units); and the advancement of optimization schemes and machine learning (ML) models able to constrain high-dimensional and non-linear systems. Many potential applications of such statistical or ML models (sometimes in combination with dynamical modeling derived from first principles) have been developed. These applications include identifying patterns and features as well as regions with common dynamics and deriving related quantities from easily observed ones (such as estimation of subsurface dynamics from partial observations or chlorophyll concentration from radiances), infilling missing data in satellite observations and adaptive sampling methods guided by machine learning. The spatial and temporal scales from different observation systems have become more and more diverse, which requires new techniques and approaches to combine them in an optimal way for estimating the state of the ocean. Statistical or ML models can also be used to identify bad or questionable observations and can help in quality control and quality assessment of observational data products. Data-driven techniques also have a high potential to complement classical numerical modeling as they allow new ways to represent subgrid parameterization, surrogate models, optimal ensemble predictions, and new and efficient approaches for stochastic modeling and data assimilation to be proposed. While many recent advancements are within machine learning applications, progress in more traditional data-driven analysis techniques such as optimal interpolation, variational analysis and Kalman Filtering, among others, is also within the scope of this special issue. We welcome all submissions that fit this description.