David Dunson | Advancing Statistical Science | Philosophy of Data Science Series
A fundamental question in the philosophy of science is "what does it mean to make scientific progress?" We will have a series of episodes centered around this question for statistics and data science. In our first episode in the series, David Dunson (Duke University) discusses important advances in Bayesian analysis, big data,  uncertainty, and scientific discovery. 
Topic Timestamps
0:00 Intro to David Dunson
1:54 What does it mean to advance data science and statistics? 
6:14 Industry & Optimization, Science & Uncertainty
8:14 Prediction & Discovery / Bayesian Modeling 
14:13 What is “complex” data?
22:49 Big Data, Bayes, and Nonparametrics
33:50 Ad hoc approaches vs principled methods
37:08 Should Machine Learning Publications Refocus on Scientific Discovery?
39:50 Mathematically principled data science & statistics
51:40 Do Bayesians just use priors as regularizers?
55:16 Bayesian Priors and Tuning Inference Methods
1:00:00 Prioritize the Most Important Work in Data Science 
1:07:07 Good Practices of Star Grad Students
1:13:17 The Science in Statistical *Science*
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