Hear advice and perspectives from Bucknell alumni who will examine career paths that utilize the mathematics degree while discussing their work and available opportunities. The conversation will include a question and answer period and an opportunity to meet (and network with!) the alumni panelists. Pizza and calzones will be provided. This event is sponsored by the Mathematics Department and the Center for Career Advancement.
Allison Gibson ‘13, Consultant, Boston Consulting Group; MBA Graduate 2019, Kellogg School of Management, Northwestern University
Rachel Guen ‘19, Associate Analyst, Moody’s Investors Service
Zach Moon, ASA ‘16, Actuarial Advisor, Cigna
Jin On ’12, Manager, Data Science & Provider Analytics, Evariant
Student Colloquium talk by Professor Kari Lock Morgan, Penn State University
Title: Understanding Statistical Significance
Abstract: You may or may not have heard of results being “statistically significant,” and you may or may not know that results qualify as statistically significant if the p-value falls below a given threshold. Regardless of whether these phrases currently hold any meaning for you, the goal of this talk will be to shed light on the actual meaning of a p-value and statistical significance (beyond just “p < 0.05”). This will be accomplished by covering a modern and computationally intensive way of computing a p-value that will be illustrated both by hands-on and online activities (so bring a laptop or tablet if you want to play along!). This simulation-based approach will be both accessible to those who have never taken any statistics, and valuable to those who have taken statistics but want a deeper understanding or a more modern approach.
Student Colloquium talk by Professor J.T. Fry, Bucknell University
Title: Interacting with the Shadow of Data
Abstract: In our first statistics class, we learn how to build a scatterplot to visualize two variables at once. But what happens when we have many variables? Projection methods such as Principal Component Analysis (PCA) can create 2D pictures of higher-dimensional data, much like how the sun projects our 3D body into a 2D shadow. However, exploring these high-dimensional datasets can be complicated. In this talk, we present a visual analytics model that allows the user to combine their personal knowledge with a projection method to find novel ways of exploring the data.