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This primer introduces Gaussian Processes (GPs) as a practical tool for data science and machine learning. We focus on GPs' popular use as a flexible tool for regression, with a fully nonparametric relation between covariates and response --- as well as a coherent mechanism for reporting uncertainty. We will discuss how GPs are an example of "Bayesian nonparametrics" (BNP) and able to learn more nuance from data as data set size increases. We will cover the standard GP model; the covariance (or kernel) function that specifies the GP; GP inference; benefits and limitations of GPs; and uses of GPs as a tool or module in data analyses beyond GP regression.