Wednesday, April 21 at 2pm EDT/11am PDT
Analyzing data using conventional statistical methods involves looking at tabular data where data points are independent of each other, e.g. a person’s age is independent of any other person’s age.
These approaches limit the insight that can be gained as there’s often knowledge hidden in how one data point relates to another. For example, two people can be deemed similar if they have entirely different purchase histories but each have purchase histories similar to a third user.
This talk will go over how graph analytics can gain these sorts of insights not easily achievable through conventional data analysis performed on tabular data.