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With pervasive sensors continuously collecting and storing massive amounts of information, there is no doubt this is an era of data deluge. Learning from these large volumes of data is expected to bring significant science and engineering advances along with improvements in quality of life. However, with such a big blessing come big challenges. Running analytics on voluminous data sets by central processors and storage units seems infeasible, and with the advent of streaming data sources, learning must often be performed in real time, typically without a chance to revisit past entries. Workhorse signal processing (SP) and statistical learning tools have to be re-examined in todays high-dimensional data regimes. This article contributes to the ongoing cross-disciplinary efforts in data science by putting forth encompassing models capturing a wide range of SP-relevant data analytic tasks, such as principal …