Data science tools can help us both better understand underlying phenomenon and better identify future courses of action. However, selecting the right tool for the job requires a further challenge and an art. The present analysis compares the well known TensorFlow package, in Python, to the lesser utilized but more statistically friendly package H2O, in R, to predict the SP500 stock market index as a multi-parameter economic time series. Results, innovations, ease or difficulty of use, and future applications of each data science package are reported. Local machine and virtual machine instances in Amazon AWS AMIs are evaluated. Overall fit of economic parameters are examined. TensorFlow and H2O offer unique and powerful neural network and recurrent neural network/LSTM solution pathways that are well worth consideration.