『The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations』のカバーアート

The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations

The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations

著者： Fexingo
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Lucas and Luna sit at a data-science workstation, two thin laptops open to scatter plots and clustering visualizations, and ask: what can we actually learn from the numbers? Each episode of The Data Science Podcast with Fexingo is a grounded, specific conversation about a single analytics problem or machine-learning method — from regularization in regression to the bias-variance trade-off in random forests. Lucas leads with a journalistic eye for how models are built and tested in the real world, citing actual case studies like how Netflix used matrix factorization for recommendations or how healthcare researchers apply survival analysis to clinical trials. Luna keeps the discussion honest, asking about data quality, feature engineering pitfalls, and whether a model’s accuracy actually translates to business value. They never resort to buzzwords: instead, they walk through the workflow from data collection to deployment, discussing trade-offs like interpretability versus performance. The show serves data scientists, analysts, and engineers who want to stay sharp on methods without the hype. Listeners walk away with a clearer understanding of why one algorithm beats another on a given dataset, and what that means for their own projects. Can a neural network ever be truly explainable? And if not, should we trust it anyway? #DataScience #MachineLearning #Analytics #DataEngineering #Statistics #Python #RStats #DeepLearning #AI #BigData #DataVisualization #PredictiveModeling #CausalInference #DataQuality #FeatureEngineering #Business #FexingoBusiness #BusinessPodcast #Technology Keep every episode free: buymeacoffee.com/fexingo© 2026 Fexingo. All rights reserved. 経済学
エピソード
  • How Data Teams Build Causal AI Models

    How Data Teams Build Causal AI Models
    2026/09/10
    Most data teams are stuck in correlation. They build models that predict what happens but can’t explain why, leading to costly mistakes when interventions change the underlying environment. In this episode, Lucas and Luna explore how forward-thinking organizations are shifting from predictive machine learning to causal inference. We look at a specific case where a major logistics provider used do-calculus and structural equation modeling to distinguish between weather-driven demand spikes and genuine marketing effectiveness. You’ll learn why standard A/B testing fails for complex business systems, how to build causal graphs before touching any code, and why understanding confounders is the only way to trust your model when the world shifts. This is Episode 181 of The Data Science Podcast with Fexingo. #CausalInference #DataScience #MachineLearning #BusinessAnalytics #FexingoBusiness #BusinessPodcast #DecisionMaking #AIModels #Confounders #PredictiveAnalytics #TechTrends2026 #DataStrategy #LogisticsOptimization #StructuralEquations #DoCalculus #Counterfactuals #DataTeams #ROIMeasurement Keep every episode free: buymeacoffee.com/fexingo
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    13 分
  • How Data Teams Build Model Cards for Transparency

    How Data Teams Build Model Cards for Transparency
    2026/09/09
    In this episode of The Data Science Podcast with Fexingo, Lucas and Luna explore the emerging practice of model cards. They examine how leading technology teams are using standardized documentation to disclose a machine learning system’s intended use, performance metrics across demographic groups, and known limitations. Rather than treating models as black boxes, data science teams are adopting transparency frameworks similar to nutrition labels to build trust with regulators and end users. The discussion covers the structural components of a model card, how to handle edge cases in deployment, and why documenting failure modes is just as critical as reporting accuracy scores. This practical guide helps data scientists and engineering leaders prepare their AI systems for responsible production environments. #ModelCards #AITransparency #ResponsibleAI #MachineLearning #DataScience #TechEthics #MLOps #AIGovernance #FexingoBusiness #BusinessPodcast #TechnologyTrends #DataDriven #ModelDocumentation #BiasDetection #ProductionAI #TechLeadership #AlgorithmicFairness #DataEngineering Keep every episode free: buymeacoffee.com/fexingo
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    13 分
  • How Data Teams Build Explainable AI Systems

    How Data Teams Build Explainable AI Systems
    2026/09/08
    We explore why black box models are failing enterprise trust and how data teams are shifting toward inherently interpretable architectures. Using a specific case from a major fintech lender, we look at the trade-off between raw predictive power and regulatory compliance. The episode breaks down three practical techniques for building models that explain themselves without sacrificing accuracy. #ExplainableAI #XAI #ModelInterpretability #DataScience #MachineLearning #FexingoBusiness #BusinessPodcast #TechTrends2026 #RegulatoryCompliance #FinTech #AlgorithmicBias #TrustInAI #DataEthics #LucasAndLuna #AIGovernance #ModelTransparency #EnterpriseAI #DataDriven Keep every episode free: buymeacoffee.com/fexingo
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    10 分
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