Big Data Madison promotes the understanding and adoption of technologies used to acquire, store, and analyze data in all its forms. This spans everything from data engineering to data science.
Everyone is encouraged to attend, no level of experience is too basic to join and learn.
We will focus on some of the technologies used in the Big Data ecosystem (Hadoop, Spark, streaming data and data processing, etc), as well as topics in Data Science (machine learning, data visualization, analytics and more). We will try to balance the topics between technology talks, use cases, and demos.
Retailers have many methodologies for grouping their products together, some may use a merchant driven hierarchy, while others use a hierarchy dictated by Marketing Strategy. Either way, these product groupings influence a number of critical decisions that each retailer must make, such as how products are brought to market, how they are advertised, and how they are discounted. This talk will focus on an alternative method for developing a product hierarchy, a customer driven approach. For demonstration purposes, we will construct a toy data set to represent customer sales data, from which we will construct Random Intersection Graphs. These graphs relate products to one another via transactional history and their projections will be used to create graphs that provide alternative underlying structures for product relationships. The insights driven from uncovering these latent structures in product relationships can assist in driving strategies throughout the business. This talk will focus on how one can determine a customer’s level of brand loyalty when making their purchases.
Tipan Verella is a Data Scientist in Marketing’s Advanced Analytics organization at Kohl’s, where his coffee fueled days are spent doing data engineering/wrangling/analysis, as well as building models that serve as the foundation for executive strategy. Prior to his tenure at Kohl’s, Tipan worked in AdTech, for companies such as Millenial Media and AOL (both now Verizon), primarily focusing on the performance prediction of click-through and conversion rates of online advertisements.
Tipan is finishing his PhD work in Systems and Information Engineering at the University of Virginia, where he researched latent structures of complex behavioral systems using tools and techniques from probability and graph theory. Tipan is a proud Marquette University faculty spouse, and Highland Community School parent, with a penchant for mathematics and programming in Python.
I would like to thank American Family for the food and Cloudera for an after meetup round of drinks.