Data & Science with Glen Wright Colopy

Philosophy of Data Science | S3 E1 | NeuralNets, GANs, Causality, and Medicine

Philosophy of Data Science Series Session 3: Data Science Highlight Reel Episode 1: Adler Perotte on NeuralNets, GANs, Causality, and Medicine Watch it on... YouTube: https://www.youtube.com/watch?v=DOf2lVHzZS4 Podbean: https://podofasclepius.podbean.com/e/philosophy-of-data-science-s3-e1-neuralnets-gans-causality-and-medicine/ 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! 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!

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

Career Q&A: 10 Questions From a Beginner Data Scientist Watch it on... YouTube: https://youtu.be/ftikMj7MoYM Podbean: https://podofasclepius.podbean.com/e/career-qa-10-questions-from-a-beginner-data-scientist/ 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! You can join the mail list here: https://www.podofasclepius.com/mail-list #datascience #career #job #jobadvice

Philosophy of Data Science | Deborah Mayo | Philosophy of Science & Statistics

Philosophy of Data Science | Keynote 1 Presentation | Philosophy of Science & Statistics Philosophy of Data Science Series Keynote with Deborah Mayo Episode 2: The Philosophy of Science & Statistics In the first keynote of the Philosophy of Data Science Series we have a 2-part interview with Deborah Mayo (Virginia Tech). In the second part of our keynote, Deborah Mayo covers the interplay between scientific and statistical philosophy. Deborah highlights some common scientific fallacies, along with suggestions of where statistical thinking can be made more rigorous. Watch it on... YouTube: https://youtu.be/9GGAXZ6htrA Podbean: https://podofasclepius.podbean.com/e/philosophy-of-data-science-keynote-1-presentation-philosophy-of-science-statistics/ 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!

Philosophy of Data Science | S01 E04 | Values and Subjectivity in Data Science

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. Watch it on... YouTube: https://youtu.be/9USkWtX-ydc Podbean: https://podofasclepius.podbean.com/e/philosophy-of-data-science-s01-e04-values-and-subjectivity-in-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 0:03 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 | S02 E04 | Intro to Abductive Reasoning for Data Scientists

Philosophy of Data Science Series Session 2: Essential Reasoning Skills for Data Science Episode 4: Intro to Abductive Reasoning for Data Scientists Watch it on... YouTube: https://youtu.be/SzQn9SPVhRU Podbean: https://podofasclepius.podbean.com/e/philosophy-of-data-science-s02-e04-intro-to-abductive-reasoning-for-data-scientists/ 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. 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 0:18 Example of Abduction in Action 4:55 Definition of Abduction 6:21 Applying Abductive Reasoning 8:35 Why is Abduction Not Deduction? 14:55 Abduction in Data Sciences 17:40 Conclusion

Philosophy of Data Science | S02 E03 | Intro to Inductive Reasoning for Data Scientists

Philosophy of Data Science Series Session 2: Essential Reasoning Skills for Data Science Episode 3: Intro to Inductive Reasoning for Data Scientists Watch it on... YouTube: https://youtu.be/lNOUvOUE_KE Podbean: https://podofasclepius.podbean.com/e/philosophy-of-data-science-s02-e03-intro-to-inductive-reasoning-for-data-scientists/ 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. 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! Outline 0:00 Intro 0:18 Inductive vs Deductive Reasoning 2:35 Overview of Induction, Deduction, and Abduction 3:23 Types of Induction: Everyday Life vs Statistical Generalizations 5:25 Sample to Population Induction 6:48 Population to Individual Induction 9:35 The Problem of Induction 11:52 Induction: Fallible but Powerful

Philosophy of Data Science | S02 E02 | Intro to Deductive Reasoning for Data Scientists

Philosophy of Data Science Series Session 2: Essential Reasoning Skills for Data Science Episode 2: Intro to Deductive Reasoning for Data Scientists Watch it on... YouTube: https://youtu.be/y93D-55wgX8 Podbean: 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! 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 0:18 Deduction Example in Statistics 4:05 Deductive Reasoning: Basic Concepts 6:42 Deductive Reasoning in Science 11:00 Falsification 16:05 Deductive Reasoning: A Summary

Philosophy of Data Science | S02 E01 | Round Table on Essential Reasoning Skills for Data Science

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/ 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

Philosophy of Data Science | S01 E03 | Communicating the Science in Data Science

Philosophy of Data Science Series Session 1: Scientific Reasoning for Practical Data Science Episode 3: Communicating the Science in Data Science One of the biggest challenges in data scientists is to communicate why your work matters. Kathy Ensor (ASA 2022 President and Rice University’s Noah Harding Professor of Statistics) covers how to distinguish yourself as a professional by communicating both your scientific and technical value. (Hint: The same scientific reasoning that helps you do good work in data science will also help you critically assess “how” and “what” to communicate.) Watch it on... YouTube: https://youtu.be/Vtasc0GGKDs Podbean: 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!

Philosophy of Data Science | S01 E02 | Scientific Reasoning for Practical Data Science

Philosophy of Data Science Series Session 1: Scientific Reasoning for Practical Data Science Episode 2: Scientific Reasoning for Practical Data Science Scientific reasoning plays an essential role in data science and statistics, both for developing new methods and applying our methods to real-world problems. In Session 1's titular episode, Andrew Gelman talks through the role of scientific thinking in his approach to data analysis. He also highlights the good ideas that have been generated by the wider statistical community. Watch it on... YouTube: https://youtu.be/R6mq5Esjzfw Coming up next week: Communicating the Science in Data Science with Kathy Ensor (Rice University & 2022 ASA President) 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! You can join our mail list at: https://www.podofasclepius.com/mail-list #datascience #statistics #machinelearning #ai #science #stem

Philosophy of Data Science | S01 E01 | Critical Reasoning in Medical Machine Learning

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!

Philosophy of Data Science | S01E00 | Welcome to the Series!

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...)

Innovative Trial Design & Master Protocols: Lisa Lavange | Pod of Asclepius

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

NC ASA Chapter: Plenty of Online Activities! @Pod of Asclepius

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

RelationalAI: Building a Knowledge Graph Database with Julia | Nathan Daly and Molham Aref@POd of Asclepius

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

Are Challenge Trials Ethical for COVID-19? with Richard Yetter Chappell @Pod of Asclepius

How do you forecast the spread of COVID-19? with Lily Wang @Pod of Asclepius

JuliaCon 2020 with Jane Herriman - Pod of Asclepius

S01 Episode 17 with Xinyi Li Part 2: Big Data Squared - Combining Brain Imaging and Genomics for Alzheimer’s Studies

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.

S01 Episode 17 with Xinyi Li Part 1: Big Data Squared - Combining Brain Imaging and Genomics for Alzheimer’s Studies

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.