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Data & Science with Glen Wright Colopy
Data & Science with Glen Wright Colopy episodes
- Gualtiero Piccinini | What Are First-Person Data? | Philosophy of Data Science
Gualtiero Piccinini | What Are First-Person Data?
First-person methods (and its associated data) have been scientifically and philosophically contentious. Are they pseudoscientific? Or simply pushing the bounds of scientific methodology? Obviously, I have no idea… so Prof. Gualtiero Piccinini (University of Missouri – St. Louis) provides a helpful introduction to the topic covering the key points of its history and the philosophical/scientific debate.
0:00 Why cover first-person methods & data?
2:26 First-person methods vs first-person data?7:10 Are first-person data legitimate at all?11:50 Phenomenology13:26 First-person data is extracted from human behavior18:25 Skepticism & arguments against first-person data25:40 Psychophysics, introspectionists, behavioralists, cognitivists, and the origins of first-person data35:20 Using new instruments & methods in science46:00 Is this where the philosophers roam?#datascience #statistics #science
52 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 Dunson1:54 What does it mean to advance data science and statistics?6:14 Industry & Optimization, Science & Uncertainty8:14 Prediction & Discovery / Bayesian Modeling14:13 What is “complex” data?22:49 Big Data, Bayes, and Nonparametrics33:50 Ad hoc approaches vs principled methods37:08 Should Machine Learning Publications Refocus on Scientific Discovery?39:50 Mathematically principled data science & statistics51:40 Do Bayesians just use priors as regularizers?55:16 Bayesian Priors and Tuning Inference Methods1:00:00 Prioritize the Most Important Work in Data Science1:07:07 Good Practices of Star Grad Students1:13:17 The Science in Statistical *Science*#datascience #science #statistics
1 hr 18 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 models12:20 - Which diseases wouldn't you track for modeling?19:02 - Multiple comparison adjustments of alarms25:15 - Domain knowledge of outbreak features29:30 Competing hazards & risks34:30 Comparing hemispheres37:00 - Bridging the gap for infectious diseases to cancer45:10 - Retrospective data correction / changing monitoring57:00 - Competing risks & statistics1:01:30 - Deducing risks & affects through knowledge of immunological mechanisms1:09:00 - Future scientific convos#datascience #science
1 hr 9 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 Science16:56 First step to delivering value to industry21:38 Toy example of high value data science25:28 Deep technical challenges are real and useful too!29:59 Formalized logic in machine learning solutions32:54 Data Science & Machine Learning Projects can fail.38:50 Getting to the cool data science projects47:21 Putting Machine Learning Models into Software56:21 Software and Deduction, Machine Learning and Induction1:06:06 Is Software A Deductive Complex System?1 hr 9 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
1 hr 13 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
1 hr 3 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 Game12:40 – Job vs Career18:18 – Nonsensical Data Science Job Descriptions25:40 – Technical Review & Presentation30:00 – The Advantages of Early Career37:25 – Save Job Descriptions / Industry vs Academia46:10 – Career vs Job Clarification53:10 – Bachelor’s vs Master’s vs Doctorate?56:10 – Delivering Value Over Time1:08:10 – Product vs Service1:11:10 – Comments From an Academic Perspective1:116:43 – Get Your Foot in the Door / Doing What You Love1:25:50 – Future Q&A’s1 hr 31 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 Problems13:00 Rules of Statistical Inference19:30 Bias (The Controversial Bit?!?!)24:10 Steps of Statistical Reasoning25:50 Connection to Philosophy of Science27:35 Measuring Evidence (Frequentist vs Bayesian vs Loss Function)29:49 Problems with the p-values32:00 Choosing & Checking Priors49:25 Idealism, Good Plans, Bad Plans54:45 Describing Your Reasoning59:20 Critiques of the Principle of Evidence1:04:00 Data-Driven Science vs Hypothesis Driven Science1 hr 10 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
1 hr 36 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
1 hr 11 min
About Data & Science with Glen Wright Colopy
From the publisher's feed
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”