Creating Production Level Data Science Code by Megan Robertson Visit https://rstats.ai/nyr/ to learn more. Abstract: A data scientist writes code throughout every stage of a project from exploratory data analysis to evaluating models and summarizing results. Once you've developed a proof of concept or minimally viable product it can be a daunting task to put it into production. How do you organize and adapt all the code that you created? What can you do to make sure the code catches errors and alerts you to them? Do you feel overwhelmed by everything you need to do? By attending this presentation you will learn tips and strategies to organize your own code during a project to make creating production code easier. You will also learn how to optimize your code to catch errors and create effective documentation. Bio: Megan Robertson is a Senior Data Scientist at Nike. She has multiple years of experience applying data science in retail settings. She first became interested in math and statistics through sports analytics, and wrote her Master's thesis in collaboration with an NBA team. Her background includes training in Bayesian methodology, statistical modeling, machine learning and more. Megan has delivered multiple talks on various aspects of data science from building a career and project management to writing code and more. Twitter: https://twitter.com/leggomymeggo4 Presented at the 2021 New York R Conference (September 9, 2021)