Data Skeptic

Updated: 06 Jul 2025 • 580 episodes
dataskeptic.com

The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.

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In this episode, Professor Pål Grønås Drange from the University of Bergen, introduces the field of Parameterized Complexity - a powerful framework for tackling hard computational problems by focusing on specific structural aspects of the input. This framework allows researchers to solve NP-complete problems more effic

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In this episode, we learn why simply analyzing the structure of a network is not enough, and how the dynamics - the actual mechanisms of interaction between components - can drastically change how information or influence spreads.  Our guest, Professor Baruch Barzel of Bar-Ilan University, is a leading researcher in ne

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22 Jun 2025 • EN

Github Network Analysis

In this episode we'll discuss how to use Github data as a network to extract insights about teamwork. Our guest, Gabriel Ramirez, manager of the notifications team at GitHub, will show how to apply network analysis to better understand and improve collaboration within his engineering team by analyzing GitHub metadata -

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14 Jun 2025 • EN

Networks and Complexity

In this episode, Kyle does an overview of the intersection of graph theory and computational complexity theory.  In complexity theory, we are about the runtime of an algorithm based on its input size.  For many graph problems, the interesting questions we want to ask take longer and longer to answer!  This episode prov

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01 Jun 2025 • EN

Actantial Networks

In this episode, listeners will learn about Actantial Networks—graph-based representations of narratives where nodes are actors (such as people, institutions, or abstract entities) and edges represent the actions or relationships between them.  The one who will present these networks is our guest Armin Pournaki, a join

37 min
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24 May 2025 • EN

Graphs for Causal AI

How to build artificial intelligence systems that understand cause and effect, moving beyond simple correlations? As we all know, correlation is not causation. "Spurious correlations" can show, for example, how rising ice cream sales might statistically link to more drownings, not because one causes the other, but due

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