This talk is targeted at data analysts and data scientists familiar with the PyData stack but with little or no formal training in advanced math or graph analytics. In this talk the audience will be given an overview of some of the basics of graph theory, be taught how to state a data science problem graphically as well as how to identify a potential graph problem, and will be introduced to some of the tools available in Python to use graph analytics on the job. After being introduced to the "ball and stick" model of a graph as well as some of the basic ideas of graph theory such as nodes and edges, paths and cycles, indegrees vs outdegrees for directed graphs, and edge weighting, the audience will then be shown some common use cases of graph analytics in the real world. Examples such as using path analysis to analyze online checkout funnels or optimize car routing, cluster analysis to find the most important nodes in a graph as applied to social network analysis, and graph coloring for scheduling will be used to illustrate the value of thinking graphically. Finally, NetworkX and igraph will be introduced as Python libraries that make graph analytics readily available. The talk will be a mix of theory and application, with the goal of using the wide applications and ready-to-use tools as a method of inspiring the audience to further study theory. However, the examples shown will demonstrate the material contained in this talk can be used on its own to solve problems with no further study.