The Data Science Systems group at UH focuses on developing interoperable, scalable and parallel algorithms to analyze data sets mainly with machine learning (e.g. clustering, classification, regression, dimensionality reduction, variable/feature selection, time series and even deep neural networks), and graphs (paths, cliques, vertex neighborhood). Our approach is different from other data science groups in the sense that we build libraries, tools and systems in general, instead of using existing systems. In other words, we aim to understand how a specific DS algorithm works all the way from reading the data set from secondary storage, processing it in main memory exploiting modern CPUs and ample RAM, but without exceeding the computer capacity. On the other hand, we develop interoperable routines that can exchange and read data in diverse formats, to analyze them with flexibility and high speed. In the past most of our research worked on DBMSs. Today it works DS languages like Python and R, where the data sources are diverse files, which may not necesarily come from a database and which may flow continously like data streams.