Working with Messy Data
Abstract
Data, no matter how prepared, usually needs some refinement before it’s ready to use in your analysis. During this talk, you’ll hear about some approaches to exploring data, recognizing common data quality issues, and what can be done to address those issues. You’ll hear about some practical approaches you can use in every project, and about some cautionary tales about what to avoid or how your decisions can start to influence the results of a project.
Location
In Person
Authors
Christopher Teixeira
(he/him)
Data Scientist
Christopher Teixeira is a Data Scientist with extensive experience applying statistics, applied probability,
and operations research to solve complex organizational challenges. Throughout his career, he has partnered with
diverse stakeholders to drive data-informed decision-making, helping organizations navigate the nuances of various
analytical techniques to find optimal solutions. Christopher has a proven track record of delivering code in multiple languages,
leading large-scale technical efforts, and responding to technical proposals and developing relationships with customers.
He holds an M.S. in Operations Research from George Mason University and a B.S. in Mathematics from Worcester Polytechnic Institute.