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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

By: 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. Economics
Episodes
  • How Data Scientists Use Reinforcement Learning for Dynamic Pricing

    How Data Scientists Use Reinforcement Learning for Dynamic Pricing
    Jun 15 2026
    In this episode of The Data Science Podcast, Lucas and Luna explore how reinforcement learning (RL) is transforming dynamic pricing strategies. Using the example of a major ride-hailing company, they break down how RL algorithms learn to set prices in real time by balancing exploration (testing new price points) and exploitation (using known optimal prices). Lucas explains the core RL concepts of state, action, reward, and the epsilon-greedy algorithm. Luna digs into the practical trade-offs: how often should a model explore versus exploit, and why the reward function must account for long-term customer retention, not just immediate revenue. The conversation also touches on how RL differs from A/B testing in dynamic pricing, the role of simulation environments for training, and ethical considerations around price discrimination. Listeners will walk away with a concrete understanding of RL-based pricing mechanics and a mental model to evaluate pricing algorithms they encounter daily. #ReinforcementLearning #DynamicPricing #DataScience #MachineLearning #RL #PricingStrategy #RideHailing #ExplorationExploitation #EpsilonGreedy #RewardFunction #AIBusiness #Technology #TechPodcast #DataDriven #FexingoBusiness #BusinessPodcast #DataSciencePodcast #Fexingo Keep every episode free: buymeacoffee.com/fexingo
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    9 mins
  • How Data Scientists Build Churn Prediction Models That Actually Work

    How Data Scientists Build Churn Prediction Models That Actually Work
    Jun 14 2026
    Churn prediction is one of the most common — and most frustrating — problems data scientists face. In this episode, Lucas and Luna dig into why many churn models fail in production and what separates the ones that actually reduce customer loss. They walk through a concrete example from a mid-size telecom company that cut churn by 14 percent in six months by focusing on the right features and the right deployment strategy. Along the way, they discuss the trap of over-relying on recency features, why boosting models often beat neural nets on tabular churn data, and how to build a simple intervention framework that turns predictions into action. If you've ever built a churn model that looked great in the notebook but went nowhere in production, this one's for you. #ChurnPrediction #DataScience #MachineLearning #CustomerAnalytics #FeatureEngineering #XGBoost #Telecom #ModelDeployment #PrecisionRecall #CustomerRetention #LifetimeValue #TinyML #ProductionML #BusinessAnalytics #Technology #FexingoBusiness #BusinessPodcast #DataDriven Keep every episode free: buymeacoffee.com/fexingo
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    6 mins
  • How Data Scientists Use Active Learning to Label Smarter

    How Data Scientists Use Active Learning to Label Smarter
    Jun 14 2026
    In this milestone 50th episode, Lucas and Luna explore active learning — a machine learning paradigm where the model itself chooses which data points to label, dramatically reducing manual annotation costs. They break down the core idea using a concrete example: training a fraud detection model for a payment processor processing 10 million transactions per day. Lucas explains uncertainty sampling, query-by-committee, and the 'exploration vs. exploitation' trade-off. Luna raises the practical challenge of label noise and how to handle it. They also discuss when active learning fails — like when the unlabeled pool doesn't represent real-world distribution. The conversation ties back to the broader theme: getting more value from fewer labels, a critical skill for any data scientist working with limited annotation budgets. #ActiveLearning #MachineLearning #DataScience #UncertaintySampling #QueryByCommittee #FraudDetection #Labeling #Annotation #SemiSupervisedLearning #ExplorationVsExploitation #ModelTraining #DataEfficiency #MLStrategy #Technology #Podcast #FexingoBusiness #BusinessPodcast #DataSciencePodcast Keep every episode free: buymeacoffee.com/fexingo
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    10 mins
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