Data & Science with Glen Wright Colopy

Glen Wright Colopy

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”

Subscribe Listen in podnod RSS feed Show website

89 episodes
Updated Aug 2, 2022

Episodes89

Newest first

Aug 17, 2021 · 1 hr 17 min
David Dunson | Advancing Statistical Science | Philosophy of Data Science
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* #datascience #science #statistics
Aug 3, 2021 · 1 hr 8 min
Martin Kuldorff | Spatiotemporal Models of Disease Outbreaks
Note: This conversation was recorded June 25, 2021. Martin Kuldorff | Spatiotemporal Models of Outbreaks Martin Kuldorff (Harvard Medical School) talks about the integration of biological & demographic information (and general reality) in the spatiotemporal models used to detect disease outbreaks. He also discusses how these methods can be applied to non-infectious diseases like cancer. 0:00 - Spatio-temporal modeling of outbreaks 6:02 - Important features of spatio-temporal outbreak models 12:20 - Which diseases wouldn't you track for modeling? 19:02 - Multiple comparison adjustments of alarms 25:15 - Domain knowledge of outbreak features 29:30 Competing hazards & risks 34:30 Comparing hemispheres 37:00 - Bridging the gap for infectious diseases to cancer 45:10 - Retrospective data correction / changing monitoring 57:00 - Competing risks & statistics 1:01:30 - Deducing risks & affects through knowledge of immunological mechanisms 1:09:00 - Future scientific convos #datascience #science
Jul 19, 2021 · 1 hr 8 min
Jason Costello | Data Science vs Software, Academia vs Industry
Interested in Data Science? Learn Data Science and Statistics from experts as they cover key topics in the field. The Data & Science podcast focusses on teaching data scientists how to think critically in order to solve data analysis problems across various scientific domains. Jason Costello | Data Science vs Software, Academia vs Industry Jason Costello (Hypervector) describes his (non-trivial) transition from academic research into big tech and then the healthcare industry. He outlines a strategy to find the cool research problems that you get in academia while still delivering value to your company. We then talk about the interface of data science / machine learning and software. 0:00 Deploying Data Science into the Real World 8:24 Transitioning from Academic to Industrial Data Science 16:56 First step to delivering value to industry 21:38 Toy example of high value data science 25:28 Deep technical challenges are real and useful too! 29:59 Formalized logic in machine learning solutions 32:54 Data Science & Machine Learning Projects can fail. 38:50 Getting to the cool data science projects 47:21 Putting Machine Learning Models into Software 56:21 Software and Deduction, Machine Learning and Induction 1:06:06 Is Software A Deductive Complex System?
Jun 14, 2021 · 1 hr 12 min
Eric Daza | N-of-1 Science & Causal Inference | Philosophy of Data Science
Interesting in Data Science? Learn Data Science and Statistics from experts as they cover key topics in the field. The Data & Science podcast focusses on teaching data scientists how to think critically in order to solve data analysis problems across various scientific domains. Eric Daza | N-of-1 Science & Causal Inference | Philosophy of Data Science Much of our scientific inference revolves around the identification and replication of patterns in data. So what can be done when N=1? Eric Daza gives us a statistician's perspective on the ideas behind N-of-1 studies, its best examples, and strongest critiques. 0:00 - The purpose of N-of-1 & generalizability 3:30 - Successes and challenges in N-of-1 9:30 - A lightbulb moment 18:00 – Anomalies, Compliance, & Recurring Patterns 23:00 – Best Critiques of N-of-1, Safety, Efficacy 41:20 - Causal Inference 54:30 – Increasing the number of data scientists 1:03:30 – Biostatistics’ changing place in data science / statistical thinking
Jun 1, 2021 · 1 hr 2 min
Edward McFowland III | Anomalous Pattern Detection & Model Building
#datascience #statistics Edward McFowland III | Anomalous Pattern Detection & Model Building Edward McFowland III (Harvard Business School) describes the differences between "anomalies" and "anomalous patterns". Edward describes how this informs modeling strategies, in particular, when to use an off-the-shelf model versus building a bespoke model from scratch. He then covers how to draw inspiration from different scientific and technical fields. 0:00 Edward: Live in Conference 2:00 Outliers vs Anomalies vs Anomalous Patterns 9:30 Strategy to Identify Anomalous Data Patterns 19:15 Adding Complexity to Models 25:00 Building Blocks vs Comprehensive Models 39:05 New Pieces of Evidence 40:40 Deciding Data Science Strategies 52:30 Connecting the Technical Dots 58:40 Interdisciplinary Interests
May 26, 2021 · 1 hr 30 min
Data Science Job Search | Advice + Q&A
#datascience #jobs #career #jobsearch #statistics The Statistical Consulting Section of the ASA invited me to give a presentation on the data science job search followed by a Q&A. They were kind enough to let me post it here (with minor edits). My drawing of "cumulative cost" is wrong. It should intercept the "current cost" line at time = 0. 0:00 – Humility, Goals, & Human Data Points 5:00 – Play the Numbers Game 12:40 – Job vs Career 18:18 – Nonsensical Data Science Job Descriptions 25:40 – Technical Review & Presentation 30:00 – The Advantages of Early Career 37:25 – Save Job Descriptions / Industry vs Academia 46:10 – Career vs Job Clarification 53:10 – Bachelor’s vs Master’s vs Doctorate? 56:10 – Delivering Value Over Time 1:08:10 – Product vs Service 1:11:10 – Comments From an Academic Perspective 1:116:43 – Get Your Foot in the Door / Doing What You Love 1:25:50 – Future Q&A’s
May 19, 2021 · 1 hr 9 min
Mike Evans | Statistical Reasoning & Evidence | Philosophy of Data Science Series
Mike Evans | Statistical Reasoning & Evidence | Philosophy of Data Science Series Mike Evans (University of Toronto) describes his approach to statistical reasoning. Mike outlines how to recognize and address problems that are statistical in nature and why these approaches should be grounded in our ability to measure statistical evidence. Watch it on YouTube at: https://youtu.be/Q7JpGZxHxXU 0:00 Statistical Reasoning 2:30 The Basic Problem: Reasoning on Statistical Problems 13:00 Rules of Statistical Inference 19:30 Bias (The Controversial Bit?!?!) 24:10 Steps of Statistical Reasoning 25:50 Connection to Philosophy of Science 27:35 Measuring Evidence (Frequentist vs Bayesian vs Loss Function) 29:49 Problems with the p-values 32:00 Choosing & Checking Priors 49:25 Idealism, Good Plans, Bad Plans 54:45 Describing Your Reasoning 59:20 Critiques of the Principle of Evidence 1:04:00 Data-Driven Science vs Hypothesis Driven Science
May 13, 2021 · 1 hr 35 min
Deborah Mayo | Statistics & Severe Testing vs Pseudoscience
Deborah Mayo | Statistics & Severe Testing vs Pseudoscience Watch it on… YouTube Podbean In our fourth episode of the “science vs pseudoscience” mini-series, Deborah Mayo (Virginia Tech) specifies several necessary criteria to be scientifically rigorous. She gives several examples of how statistical thinking is essential to scientific thinking and why she believes that the “I’ll know it when I see it” approach to delineating science from pseudoscience is not a good approach. Looking to catch up with the earlier “Science vs Pseudoscience” episode? You can watch them here: Intro Episode 1 Episode 2 Episode 3
May 10, 2021 · 1 hr 10 min
Kristin Morgan | The Data Science of Sports Injury
Description: In the world of biomechanics, engineers continuously aim to innovate and create new models for better understanding of their research. In this episode, Kristin Morgan (University of Connecticut) returns to the show as she explains how they use gait as a form of diagnostic tool in maximizing human performance. Having experiences on sports herself, Morgan presents how they use gait to measure recovery from physical impairment, specifically for ACL-related injuries. Aside from this, however, she also explains how they use the same tool to measure recovery from cognitive impairment. An insightful episode for all! Keywords: biomechanics, models, metrics, gait, engineering, statistics, cognitive impairment, physical impairment 0:00 - Intro 03:01 - Creating models for performance optimization 07:23 - Why gait is an effective diagnostic tool 11:38 - Maximizing gait in creating models for post-ACLR 17:35 - Manifestation of different injuries & models 22:01 - Modeling motor control 26:28 - Applying other models in biomechanics 30:50 - Using asymmetric walking for recovery 39:30 - Understanding cognitive impairment recovery 44:19 - Moving forward with gait as diagnostic tool 45:40 - Taking inspiration from other fields / Statistics in Engineering 47:45 - Engineering and statistics hand in hand 52:50 - Limitations of modeling in biomechanics 54:20 - Starting a career in biomechanics 58:20 - Including cognitive impairment 1:00:20 - Tailoring models to specific cases 1:05:33 - Applying the models to injuries other than ACL
May 5, 2021 · 1 hr 14 min
Michael McRoberts | Football Analytics and Data-Driven Decisions
Michael McRoberts | Football Analytics and Data-Driven Decisions Michael McRoberts (Championship Analytics Inc.) uses Monte Carlo simulations to provide strategy analytics to college and NFL football teams. Topics include communicating data-driven recommendations, the need to create counterfactual data, and asymmetric decision rewards. 0:00 The challenge of sports analytics 5:00 Analytics recommendations 16:00 Communicating data-driven recommendations 24:35 Vegas Odds & Ancillary Data 30:00 Football is way behind / Data science projects with a "runway" 41:25 Creating experiments and counterfactuals 49:30 Implementing data science insights 56:15 Asymmetric decision rewards 58:50 How to start in sports analytics 1:10:00 Data science vs analytics vs statistics
Apr 30, 2021 · 1 hr 11 min
Andrew Gelman & Megan Higgs | Statistics’ Role in Science and Pseudoscience
Andrew Gelman & Megan Higgs | Statistics' Role in Science and Pseudoscience #datascience #statistics #science #pseudoscience Our science vs pseudoscience discussion continues with Andrew Gelman (Columbia) and Megan Higgs (Critical Inference LLC). Andrew and Megan describe two critical roles that statistics plays in science.... but also how statistics can add the air of scientific rigor to bad research or help statisticians fool themselves. From there the conversation goes on in a way that only a conversation with Andrew and Megan can! A very fun episode. 0:00 - Two roles of statistics in science 4:50 - Many models were intended for designed experiments 10:30 - The biggest scientific error of the past 20 years 15:00 - Feedback loop of over-confidence / Armstrong Principle 21:00 - Science is personal 25:00 - The value of different approaches / Don Rubin Story 34:40 - Statistics is the science of defaults / engineering new methods 45:00 - The value of writing what you did 52:27 - Math vs science backgrounds + a thought experiment 1:01:20 - Fooling ourselves
Apr 27, 2021 · 1 hr 2 min
Irina Gaynanova | Replicability, Reproducibility, Responsibility, and Optimism for the Future of Science
Irina Gaynanova (Texas A&M) describes why she thinks that replicability is a prerequisite for reproducibility in science and how scientists can (personally) start improving the replicability of research. We also discuss how the concepts of replicability/reproducibility can differ according to the domain-specific context and the methods used. Please forward to any students or colleagues who would find this of interest!
Apr 19, 2021 · 1 hr 15 min
Science vs Pseudoscience | Neil Manson | Philosophy of Data Science
#datascience #science #pseudoscience #criticalthinking #reasoning We each like to think of ourself as scientific. I'm yet to meet someone who would embrace being called "pseudoscientific". But what makes the difference? In this episode, Neil Manson talks about the fallout from Thomas Kuhn's 1962 book "The Structure of Scientific Revolutions" and how this created a playbook for many modern critiques/attacks on scientific activity. We have a new series that centers on the discussion of science vs. pseudoscience. Guests of different backgrounds share their insights on what really constitutes science and the highly-contested pseudoscience. The implications for data scientists and statisticians is very interesting, since many of the examples around this debate involved the conflicts between hypothesis-driven science vs data-driven science. 0:00 - Intro 0:43 - Science vs Pseudo/Bad/No Science 05:52 - Demarcation problem of science 12:07 - Incentives in science 13:00 - Glen forgets the word for "book" 13:40 - Luminiferous aether & lunch tables 18:19 - Keeping “good science” out of the science category 22:49 - Aiming to define science in relation to Kuhn’s theory 29:06 - Kuhn’s theory in action in various scenarios 32:53 - Logical fallacies in the world of science 46:17 - Intelligent design theory as science 51:14 - Distinction of different sciences
Apr 8, 2021 · 58 min
Science vs Pseudoscience | Dien Ho | Philosophy of Data Science
We have a new series that centers on the discussion of science vs. pseudoscience. Guests of different backgrounds share their insights on what really constitutes science and the highly-contested pseudoscience. In today’s episode, we talk to Professor Dien Ho, PhD, a Professor of Philosophy and Healthcare Ethics, of the Massachusetts College of Pharmacy & Health Science University. Discover how philosophical ideas and theories are applied in hopes of understanding what really counts as science and what pseudoscience really is. 00:03 - Introductions 5:33 - What is pseudoscience? 08:53 - Legitimacy of other sciences 12:11 - What qualifies as science? 19:00 - Inductivism and empirical falsifiability 26:22 - Positivism and the importance of assumptions 31:36 - Assumptions and observations for data scientists 42:34 - The pursuit of science 49:17 - Scientism and revolutionary scientists 54:43 - Pinning down what science is
Apr 7, 2021 · 5 min
New Science vs Pseudoscience Series (+ Renaming the Podcast)
We're launching a series on "Science vs Pseudoscience" tomorrow! Also we've rebranded to better reflect the focus of the podcast. The focus of the podcast isn't changing - it's still data science, critical scientistic reasoning, and figuring out how to figure stuff out! Some fun reading on pseudoscience: https://philpapers.org/archive/MONP-1...
Mar 11, 2021 · 1 hr 14 min
Environmental Data Science | Career Q&A
We've received a lot of questions from early career data scientists interested in starting a career in environmental science and climate science. Elizabeth Mannshardt (EPA), Grant Weller (Optum Labs), and Megan Higgs (Critical Inference LLC) sit down to give you your answers! Thinking about a career change to Environmental Data Science? We invite you to listen to some career growth strategies and opportunities in environmental data science” podcast. Throughout the episode we discuss how to transition from other careers to an environmental data scientist. How to get quantitative skills in order to switch to environmental science. Ways someone can learn environmental science and get an entry job as an environmental scientist. Plus, “in career growth should one focus on a specific domain or to go broad?”. 00:00:00 Start 00:03:14 Introduction 00:09:03 Transition from other fields to Environmental Scientist. 00:22:27 How to get quantitative skills in order to switch to environmental science. 00:36:20 In career growth should one focus on a specific domain or be broad. 00:44:00 Ways someone can learn environmental science. 00:48:49 Ways people can get an entry job as an environmental scientist 01:07:54 Final comments
Feb 25, 2021 · 1 hr 42 min
Data Science Career Q&A for Undergrads
#datascience #career #job Data Science Career Q&A for Undergrads with Mallory LaRusso We continue to answer data science career questions. We've heard back from a lot of different groups about the world of data science. In this episode, we're talking about undergraduate DS job prospects. Mallory LaRusso is a senior at NCSU finishing her BS in Statistics, and Minor in Genetics. Watch/Listen as Glen and Richard answer questions from our guest Malory as she tries to understand ways of how to properly transition from being an undergrad student to becoming a data scientist. From questions about data scientists’ typical workday to their most challenging projects to date, we’ve got it all covered in this episode! Keywords: data analytics, data science, programming, coding, workday, work culture, educational background 0:00 - Introduction 02:20 - Series overview 06:44 - Educational and career path 09:16 - Typical work day 17:00 - The importance of writing in data science 20:39 - Work culture 23:30 - Type of data you work with 26:00 - Mathematical vs Statistical Models 31:45 - The harder DS jobs are what’s left 32:35 - Favorite project as data scientist 36:00 - Work on a real problem 39:55 - Data scientists’ degrees 44:15 - Difference of data analytics and data science 54:13 - Favorite programming language 1:01:29 - How data science jobs will change 1:07:42 - Largest data set to have worked with 1:17:02 - Advice for students to prepare for data science roles 1:36:30 - What advantage does an undergraduate have? 1:40:22 - Wrap-up
Feb 16, 2021 · 59 min
Philosophy of Data Science | Step-change and Anomaly Detection | Alex Bolton
#datascience #ai #earlycareer Philosophy of Data Science Series Session 3: Data Science Highlight Reel Episode 4: Alex Bolton on Step-change and Anomaly Detection Who makes it into the highlight reel of data science? Alex Bolton for doing the hard work of analyzing data to figure out exactly when things don't look "normal". We discuss the critical reasoning behind step-change detection and anomaly/novelty detection. Alex provides several real-world examples of the data and challenges. Watch it on... YouTube: https://www.youtube.com/watch?v=097FO1JDkhU Podbean: 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!
Feb 8, 2021 · 1 hr 12 min
Irina Gaynanova | Replicating Clinical Metrics & Innovating New Methods
Philosophy of Data Science Series Session 3: Data Science Highlight Reel Episode 2: Irina Gaynanova on Replicating Clinical Metrics & Innovating New Methods Who makes it into the highlight reel of data science? Irina Gaynanova for her work on replicating clinical metrics for deployment. She then goes into how her grasp of the scientific domain helps her innovate new methods and metrics. Regardless of whether you work in the clinical domain, this is an example of rigorous scientific thinking in data science. 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!
Jan 21, 2021 · 1 hr 8 min
Karel Moons | Validating Medical Predictive Models | Philosophy of Data Science
Philosophy of Data Science Series Session 3: Data Science Highlight Reel Episode 2: Karel Moons on Validating Medical Predictive Models Watch it on... YouTube: https://www.youtube.com/watch?v=Y6Qik_5hZog Podbean: Who makes it into the highlight reel of data science? Karel Moons and the classic BMJ Series on validating predictive/prognostic models for the clinic. You can start reading the BMJ Series for your self here: [1] https://www.bmj.com/content/338/bmj.b375 [2] https://www.bmj.com/content/338/bmj.b604 [3] https://www.bmj.com/content/338/bmj.b605 [4] https://www.bmj.com/content/338/bmj.b606 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!