Applications of Graph Theoretic Techniques in Data Driven Merchandising
In recent years there has been a growing appreciation for the utility of classical graph theoretic techniques in data science and business intelligence. While some still perceive little value for these techniques outside of social network analysis, at Secret Sauce Partners we have found a variety of use cases. In particular, methods using graphical connectivity have helped us elucidate data quality issues, gain insight into sparsity, and inform decisions about machine learning strategies for recommendation tasks. In this talk, we will provide a gentle introduction to graphical connectivity, introduce relevant Python libraries, and apply these to concrete examples in the domain of data driven merchandising.
Senior Data Scientist, Secret Sauce Partners
Currently, Alex serves as a Senior Data Scientist at Secret Sauce Partners, where she works on topics related to Fit Predictor, a service which allows retailers in the fashion industry to provide size recommendations to their online shoppers. Prior to this, Alex worked in data science roles in Los Angeles-based startups in advertising technology and consulting. Alex holds an undergraduate degree in Mathematics and a PhD in Quantitative Psychology.