In this session, we will examine the opportunities for decision making while building a data analysis pipeline and follow the consequences of those decisions for the interpretability of the results. In addition, we will dive into examples of various types of bias, as well as examples of assumptions made in data collection, in implementation and in statistical modeling. Throughout the session we will be discussing what to consider when choosing your quality control measures to maximize the trust that you can put in your data. Please note that this is a high-level seminar, suitable for students and researchers who are just starting out with data analysis, switching fields, or interested in incorporating a data-mindset into projects that may not have traditionally relied on data.
Monday, March 19, 2018 | 12:00-1:00 PM
McArthur Engineering Annex, MEA 202
Speaker: Athina Hadjixenofontos, PhD | CCS Director of Engagement
Dr. Hadjixenofontos joined the University of Miami Center for Computational Science in 2016. As the Center’s Director of Engagement, she leads a number of programs that aim to support the development of computational skills and adoption of computational mindsets in various populations. She’s particularly excited by the science part of data science, as it relates to assumptions, bias and their relationship with asking questions that make sense. She holds a PhD in computational genetics from the University of Miami John P. Hussman Institute for Human Genomics.
All experience levels are welcomed. Join us! Bring a laptop to maximize participation. Light Lunch will be provided.