A Layman's Glimpse Behind the Data Science Curtain

With the explosive growth of big data and data science capabilities, data professionals will find themselves increasingly interacting with data scientists and the models they produce. For this reason, it is helpful for any data professional to have a high-level understanding of the data science cycle. The objective of this session is to deliver an overview of that cycle, from the perspective of a non-data scientist who tackled a real-world data science application in the field of bibliometrics. Lessons learned from pre-processing through publication will be shared, in order to further the following learning objectives for participants:

An understanding of the data science cycle
Discussion of the importance of metadata and data quality for the data pre-processing phase, and common pitfalls
An introduction to types of models and their business applications
An overview of Weka, an open source data science tool, and its usefulness in facilitating data science exploration for the non-data scientist (no math degree required!)

Lori Hurley is a Mastery level Certified Data Management Professional. As an Information Architect for Allstate Insurance, she focuses on metadata management, reference data, and data governance, and is a member of the Model Center of Excellence. In her role, Lori enjoys the many opportunities to explore new technologies, as well as the occasional mind-bending taxonomy challenge.

She holds a Master's degree in Library and Information Science (specialization in Data Curation) from the University of Illinois at Urbana-Champaign and was awarded the 2014 Anne M. Boyd award for excellence. Lori co-authored "Deconstructing the Collaborative Impact: Article and Author Characteristics that Influence Citation Count" (ASIS&T, 2013) and "Catching Up to Corporate: A Shift towards Academic Data Governance" (SLA, 2014).

Before landing on a career in all things data, Lori was a certified Spanish teacher, a mainframe developer, and a part-time homeschooler.