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Artwork for 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

Fexingo

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
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  • Avg 10 min
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  • S4 · E190
    Yesterday · 11 min

    Why Your AI Models Fail in Production

    We drill into the hidden cost of data drift and why accuracy metrics lie. Lucas and Luna explore how a major logistics firm’s routing algorithm degraded by eighteen percent in six months, not because the code broke, but because the real world changed...

    • Transcript
  • S4 · E189
    Friday · 11 min

    Why Your AI Models Are Confidently Wrong

    Most data scientists measure model success by accuracy, but accuracy is a trap when the cost of error is asymmetric. This episode explores why precision and recall matter more than raw correctness in high-stakes environments like credit scoring and...

    • Transcript
  • S4 · E188
    Thursday · 8 min

    How Data Teams Handle Model Decay in Production

    Models degrade. This episode explores the hidden economics of model decay, using a real-world example from a major logistics firm that saw a twelve percent drop in routing efficiency over six months. Lucas and Luna break down why accuracy metrics lie...

    • Transcript
  • S4 · E187
    Wednesday · 9 min

    How Data Teams Use Counterfactual Explanations

    Most model explanations tell users what happened, but rarely what could have happened. In this episode, we look at how forward-thinking data teams are deploying counterfactual explanations to drive actual behavior change. We examine the specific...

    • Transcript
  • S4 · E186
    Tuesday · 8 min

    Why Your Data Models Fail in Production

    In this episode of The Data Science Podcast, Lucas and Luna dissect the critical gap between training environments and real-world deployment. They explore how data teams can implement effective feature stores to ensure consistency, using concrete...

    • Transcript
  • S4 · E185
    Monday · 9 min

    How Data Teams Master Causal Inference for Better Decisions

    Most data teams treat correlation as causation, leading to expensive marketing missteps and product features nobody uses. In this episode, Lucas and Luna drill into causal inference — specifically the use of propensity score matching and synthetic...

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  • S4 · E184
    September 13 · 12 min

    How Data Teams Master Active Learning for Smarter AI

    Most data teams drown in unlabeled data, wasting compute and time on information that adds little value. In this episode of The Data Science Podcast, Lucas and Luna explore active learning, a strategy where models pick their own homework. We look at how...

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  • S4 · E183
    September 12 · 7 min

    Why Your Data Models Fail at Scale

    Most data teams build models that work perfectly in the lab but collapse under real-world traffic. In this episode, we explore how Microsoft tackled massive latency issues by moving from batch processing to online learning systems. We break down the...

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  • S4 · E182
    September 11 · 10 min

    How Data Teams Measure AI Impact Beyond Accuracy

    Most data teams celebrate model accuracy while ignoring whether the model actually moves business needles. Lucas and Luna drill into why optimizing for F-score or RMSE often leads to production paralysis. They examine a specific case where a major retail...

    • Transcript
  • S4 · E181
    September 10 · 13 min

    How Data Teams Build Causal AI Models

    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...

    • Transcript
  • S4 · E180
    September 9 · 12 min

    How Data Teams Build Model Cards for Transparency

    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...

    • Transcript
  • S4 · E179
    September 8 · 10 min

    How Data Teams Build Explainable AI Systems

    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...

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  • S4 · E178
    September 7 · 10 min

    How Data Teams Measure ROI Beyond Accuracy

    We explore the hidden gap between model accuracy and actual business value, using a specific case where a retail giant’s high-precision inventory model failed to move product. Lucas and Luna break down why optimizing for F1 scores can lead to zero return...

    • Transcript
  • S4 · E177
    September 6 · 11 min

    How Data Teams Handle Concept Drift

    In this episode of The Data Science Podcast with Fexingo, Lucas and Luna dive into the subtle but critical issue of concept drift. While feature drift is well understood, concept drift represents a fundamental shift in the relationship between input data...

    • Transcript
  • S4 · E176
    September 5 · 13 min

    How Data Teams Use Synthetic Data to Train AI

    Real data is messy, biased, and expensive. Synthetic data offers a way to generate realistic training sets without touching private information. We look at how major firms are using generative models to create artificial datasets for healthcare and...

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  • S4 · E175
    September 4 · 9 min

    Why Your AI Models Are Failing in Production

    We explore the hidden gap between model accuracy and business value using a specific case study from a major fintech lender. Discover why optimizing for precision creates silent losses, how to measure true economic impact, and the practical framework...

    • Transcript
  • S4 · E174
    September 3 · 11 min

    How Data Teams Measure Model Impact Beyond Accuracy

    Most data teams celebrate high accuracy scores but fail to track whether those models actually move the business needle. In this episode, Lucas and Luna dissect the gap between technical performance and commercial value using a specific retail inventory...

    • Transcript
  • S4 · E173
    September 2 · 12 min

    How Data Teams Use Data Lineage to Trace Model Decisions

    We talk about the hidden cost of black-box models and why tracing data lineage is no longer optional for regulated industries. Using a recent compliance failure at a major fintech firm as our anchor, we break down how lineage graphs connect raw inputs to...

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  • S4 · E172
    September 1 · 11 min

    How Data Teams Use Feature Engineering to Build Better Models

    We talk about the often overlooked art of feature engineering and why raw data rarely wins on its own. Lucas and Luna break down how transforming variables, like creating interaction terms or encoding categorical noise, can double model performance...

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  • S4 · E171
    August 31 · 8 min

    How Data Teams Use Survival Analysis to Predict Churn

    In this episode of The Data Science Podcast, Lucas and Luna explore how data teams apply survival analysis to predict customer churn. They unpack the core concepts—hazard functions, Kaplan-Meier curves, and Cox proportional hazards—using a relatable...

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Showing 1–20 of 44 episodes