Data & Science with Glen Wright Colopy

Philosophy of Data Science Series 

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Who makes it into the highlight reel of data science? Adler Perotte, because he's a clear thinker on why his data needs a specific type of analysis. In this case, it's the need to draw causal inferences from observational data. Go, GANS! Go!

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Career Q&A: 10 Questions From a Beginner Data Scientist

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This week's episode is likely of interest to early career data scientists or those interested in joining the field. Richard Franzese (Certara) & Glen Wright Colopy (Pod of Asclepius) team up to answer 10 questions from Ujjwal Oli, an MSc student at George Washington University MSc Program.

The questions range from technical requirements, to desirable soft skills and domain knowledge, to "how can I get an internship if they require prior experience?"

Please forward to any early-career statisticians or data scientists who would be interested.

Thank you for your support of the series!

#datascience #career #job #jobadvice

Philosophy of Data Science | Keynote 1 Presentation | Philosophy of Science & Statistics

Philosophy of Data Science Series 

In the first keynote of the Philosophy of Data Science Series we have a 2-part interview with Deborah Mayo (Virginia Tech).

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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. 

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Philosophy of Data Science Series 

In the first keynote of the Philosophy of Data Science Series we have a 2-part interview with Deborah Mayo (Virginia Tech).

You can join our mail list at: https://www.podofasclepius.com/mail-list

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! 

Topics:

0:00 - Preface to First Keynote Interview

Philosophy of Data Science Series

Session 1: Scientific Reasoning for Practical Data Science

Episode 4: Values and Subjectivity in Data Science

The Value-Free Ideal is a central tenant of objective science. But how do values, value judgements, and subjectivity leak into the practice of data science and statistics. To what extent is it desirable for science to be informed by values? Kevin Zollman (Carnegie Mellon University) covers the range of key ideas, from Heather E. Douglas to W.E.B. du Bois.

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YouTube: https://youtu.be/9USkWtX-ydc

Podbean: https://podofasclepius.podbean.com/e/philosophy-of-data-science-s01-e04-values-and-subjectivity-in-data-science/

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0:00 Intro

0:03 Welcome Kevin Zollman (Carnegie Mellon University)!

1:44 Is Science Value-Free?

6:08 How might values affect science?

9:00 Choice of Research Problem

10:45 Loss Functions

18:34 Choice of Variables

24:10 Choice of Statistical Model

29:30 Minimizing the Values in Science (W.E.B. du Bois)

35:20 Philosopher in Science

41:20 Statements on Generalizability

47:45 Clarifying Subjective Choices

52:45 Conflicts between Scientific Disciplines

61:18 Scientific Value Judgments & Self Correcting Science

67:50 Choice in Metrics and Research Focus

70:30 Concluding Ideas

Philosophy of Data Science Series 

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The third and final of our (planned) short tutorials on key modes of critical reasoning. Abduction is common called "inference to the best explanation"...so it's easy to see why this concept is important for data scientists. 

Huub Brouwer (Utrecht University) walks us through a brief tutorial on how even a world-famous infer-er can get this wrong and how data scientists can avoid the same mistake.

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0:00 Intro

Philosophy of Data Science Series 

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New episodes of the Philosophy of Data Science Series will now be published on Mondays!

Today's episode is a short introduction to a fundamental concept. Definitely worth your time!

Inductive reasoning is the fundamental challenge to scientific rigor. Induction is baked into methods like K-fold cross validation or generalizing from a sample to a population. However, many statisticians and data scientists are unfamiliar with the term and its implications. Joseph Wu (Brown University) gets us up-to-speed with a 10-minute presentation on the fundamental role of induction in scientific reasoning.

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Outline

Philosophy of Data Science Series 

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Deductive reasoning pervades statistics and data science...but how far can it get us to the right conclusion from data? Elina Vessonen (Finnish Institute of Health) gives a great 20-minute presentation reviewing the role of deduction in scientific reasoning. Elina begins with a common statistical example and then covers common deductive fallacies and their role in science.

It's a short and gentle introduction to a fundamental concept. Definitely worth your time!

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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. 

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0:00 Intro

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.

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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/

You can join our mail list at: https://www.podofasclepius.com/mail-list

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