||A prominent 'grand challenge' for the 21st century is to effect a reversal of the paradigm by which new materials are developed and manufactured, especially to address the stringent demands of applications in advanced technologies. It is evident that the time and resource consumptive empiricism that has dominated materials development during the past century, must give way to a greater dependence on modeling and simulation and modern data science tools. Materials Science has not escaped being impacted by the challenges of Big Data. In fact, the unique combination of automated data collection on almost every instrument from microscopes to mechanical property characterization and the unusually well-developed physics-based state evolution models that we normally think of as Computational Materials Science, our field is expected to experience an explosion progress in the coming years. With the data available, on the one hand, from automated data collection and, on the other, from physics-based models, Materials Science is set to be a major player in the Big Data sphere. Data Analytics, the new field that is growing to provide the scientific basis of converting all of this data to information that can be used in critical decision making, is expected to be a key technology in the future. This symposium will bring together all facets of the nascent Data Analytics for Materials Science and Manufacturing field in order to facilitate communication and collaboration among the various camps. Papers will be sought on all subjects related to Data Analytics, a non-inclusive set of topics is: fusion of multimodal data involving different sensors characterizing the same sample, inverse methods for reconstruction of geometric, internal stress, chemistry and other features, forward modeling of microscope observations, uncertainty quantification of computed results and the effects of experimental uncertainties, graph theoretic methods for representing materials structures, ontologies for representing materials characteristics and properties, methods for parameter estimation in physics-based models, segmentation and subsequent analysis of microscope data, compressed sensing in data acquisition, data driven modeling, dimensionality reduction methods such as reduced order models or manifold learning methods, end member analysis of multi- and hyper-spectral data, prediction of rare events, anomaly detection, decision making under uncertainty, and data mining in Materials Science.