As the volume of astronomical X-ray datasets increases with the advent of dedicated catalogs and new facilities, data science and machine learning methods are becoming a useful tool for discovery in high energy astrophysics, in particular in the context of time-domain anomalies. Due to the specific nature of X-ray datasets, which are lists of individual photon detections called event files, the pre-processing practices of the data for the purpose of classification, regression, or anomaly detection need to be carefully reviewed before any existing methods designed for optical or infrared datasets is used. Here I provide an overview of our experience pre-processing X-ray datasets for machine learning applications, and adapting methods of classification and anomaly detections for X-ray catalogs (in particular, the Chandra Source Catalog, CSC). I will discuss what CSC interfaces are most useful for the seamless use of machine learning for discovery, and provide recommendations as to what other data providers should do to make these methods easier to use. Finally, I will highlight some results of our investigations.