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 Oct 21, 2020
Wednesday Oct 21, 2020
Philosophy of Data Science Series
Session 2: Essential Reasoning Skills for Data Science
Episode 1: Round Table on Essential Reasoning Skills for Data Science
Session 2 "Essential Reasoning Skills for Data Scientists" is kicking off with a roundtable discussion with Elina Vessonen (Finnish Institute for Health & Welfare), Joseph Wu (Brown University), and Huub Brouwer (Tilburg University & Utrecht University).
One of the major challenges in data science is that we use three different modes of critical reasoning (deduction, induction, and abduction) on a daily (or even hourly) basis. It's important to understand the strengths and weaknesses of each mode of reasoning so that we can apply them as appropriate. This round table will begin this conversation on the modes of reasoning and how it applies to & science and data science.
Watch it on...
YouTube: https://www.youtube.com/watch?v=5bOuy6VA8Hg
Podbean: https://podofasclepius.podbean.com/e/philosophy-of-data-science-s02-e01-round-table-on-essential-reasoning-skills-for-data-science/
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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!
0:00 Intro
0:10 Roundtable on Critical Reasoning Skills
2:50 Guest Introductions
5:48 Thesis: Data Science Use All Modes of Reasoning Daily
6:45 Taxonomy of Deduction, Induction, and Abduction
17:21 The Problem of Induction
32:18 The Problem of Induction Creeping into Deduction
36:45 Bayesian Applicability Indices and Signal Quality Indices
40:55 What is "The" Scientific Method?
44:08 What is Pseudo-Science?
47:35 Theory vs Data/Evidence
50:48 Final Remarks
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