Data and Science with Glen Wright Colopy is a podcast covering critical scientific reasoning, particularly from a data science / machine learning / statistics perspective. Episodes typically focus on understanding of how to be better scientists and critical thinkers for the practical purpose of being a better data scientists. Previously called: ”Pod of Asclepius”
Episodes
Wednesday Sep 23, 2020
Wednesday Sep 23, 2020
Philosophy of Data Science Series
Session 1: Scientific Reasoning for Practical Data Science
Episode 1: Critical Reasoning in Medical Machine Learning
Data science in medicine and healthcare requires not only algorithmic and statistical knowledge but also a strong appreciation of the clinical environment in which (i) the data is being collected and (ii) the algorithm will be used. I'll showcase a scenario where a machine learning system failed to perform a "simple" clinical task and how critical reasoning was used to resolve the problem.
Guest-host Kristin Morgan (University of Connecticut) joins us to lead the discussion in how this example is applicable to the broader field of biomedical data science.
This is...
Session 1: Scientific Reasoning for Practical Data Science
Episode 1: Critical Reasoning in Medical Machine Learning
Watch it on... YouTube: https://youtu.be/o5YmdoCiyug
Podbean:
Coming up next week: Applying Scientific Reasoning to Statistical Practice with Andrew Gelman (Columbia University)
We're always happy to hear your feedback and ideas - just post it in the YouTube comment section to start a conversation.
Thank you for your time and support of the series!
Wednesday Sep 16, 2020
Wednesday Sep 16, 2020
The Philosophy of Data Science Series
Session 1: Scientific Reasoning for Practical Data Science
Episode 0: Welcome to the Philosophy of Data Science Series!
This is our very first episode of "The Philosophy of Data Science" series on Pod of Asclepius!
We go over our plans for the series plus some thoughts on why data science is such a rich field for discussions on scientific reasoning. Your time is valuable and you deserve a good explanation of why the topics were chosen and how the series is structured to maximize learning.
Topic List
0:00 New intro jingle for the series!
0:10 Welcome to the Philosophy of Data Science Series!
1:07 Modes of reasoning
5:33 Session 1 Overview: Scientific Reasoning for Practical Data Science
10:15 Session 2 Overview: Essential Reasoning Skills for Data Science
11:32 Keynotes and Session 4
14:15 Future Sessions
Coming up next week: Critical Reasoning in Medical Machine Learning
Thank you for your time and support of the series! It only gets better from here! (Seriously, it really does only get better from here. We've got Andrew Gelman coming up, plus Cynthia Rudin, Mihaela van der Schaar...)
Wednesday Sep 09, 2020
Wednesday Sep 09, 2020
Lisa LaVange (Gillings School of Global Public Health at the University of North Carolina at Chapel Hill) was the 2018 American Statistical Association (ASA) president and the director of the Office of Biostatistics in the Center for Drug Evaluation and Research (CDER) at the FDA.
She give a high-level overview of issues surrounding Innovative Trial Design and Master Protocols. A great listen for anyone wanting to be introduced to the subject or (for those already familiar) interested in its growing breadth of applications.
#datascience #statistics #biopharm #pharma #FDA
Tuesday Aug 11, 2020
Tuesday Aug 11, 2020
Amy Shi (SAS), Emily Griffith (North Carolina State University), and Elizabeth Mannshardt (EPA) discuss the many activities of the North Carolina Chapter of the American Statistical Association, including a lot of online activities that can be enjoyed even if you aren't in NC. The recording was made on the cusp of COVID...so updated information is posted below. NC ASA Activities NC ASA YouTube Channel: https://www.youtube.com/channel/UCPMPV3vCOY2dZka5ELPBWpA NC ASA Website: https://community.amstat.org/northcarolina/home
Tuesday Jul 21, 2020
Tuesday Jul 21, 2020
Molham Aref and Nathan Daly describe their experience using Julia to build a next-generation knowledge graph database that combines reasoning and learning to solve problems that have historically been intractable. They explain how Julia's unique features enabled them to build a high-performance database with less time and effort. Both Nathan and Molham with be speaking at JuliaCon 2020 at the end of July. It's free and online, so there's no reason not to attend. You can register for JuliaCon 2020 here: https://juliacon.org/2020/
0:00 Intro
1:25 RelationalAI
3:25 Advantages of Julia as a foundation
4:21 "Full stack" data science
5:38 Advantages of Julia in the tech stack
6:30 Technical requirements of RelationalAI
7:45 Advantages of Julia (cont.)
10:00 Data munging, preprocessing, and transparency
14:30 Advantages of Julia (cont.)
18:35 RelationalAI's Innovation
22:00 Data Analysis and taking computational efficiency for granted
23:38 Who are the users of RelationalAI?
25:45 What are "knowledge graphs"?
28:30 Knowledge graphs for AI and Software 2.0
32:43 Julia as "executable math"
34:10 "Multiple dispatch" in a nutshell
36:20 Julia in the scientific community
38:53 See Nathan and Molham again at JuliaCon 2020
Monday Jul 20, 2020
Monday Jul 13, 2020
Thursday Jul 09, 2020
Monday Jun 22, 2020
Monday Jun 22, 2020
Working with brain imaging data, Xinyi has a lot of cool figures to show off in her technical presentation. She walks us through the image-on-scalar regression model and how it is used to infer a personalized “baseline” brain image along with the effects of different cognitive diagnoses.
Monday Jun 15, 2020
Monday Jun 15, 2020
Xinyi continues the conversation on precision medicine research at SAMSI. Xinyi describes the challenges of combining genomic data with imaging data for modelling Alzheimer’s with the goal to supplement subjective diagnosis criteria with the more objective biomarkers.