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Hosted by Fexingo · 🇺🇸 US · EN · 154 episodes
Established thought leaders with verified media credentials.
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. T
Fexingo hosts The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations, a business show with 154 episodes published.
In this episode of The Data Science Podcast, Lucas and Luna explore transfer learning—a technique that lets data scientists reuse pre-trained models instead of starting from scratch. They anchor the discussion with a con
Feature stores have quietly become the backbone of modern machine learning, but they're not just a place to stash tables. In this episode, Lucas and Luna dig into why feature stores are the difference between a model tha
Labeling data is one of the most expensive bottlenecks in machine learning, but active learning offers a smarter path. In this episode, Lucas and Luna break down how data scientists use active learning to train high-perf
In this episode, Lucas and Luna unpack conformal prediction, a method that gives machine learning models a rigorous way to state their own uncertainty. They walk through a real-world example—a hospital risk score that mu
In this episode, Lucas and Luna explore the multi-armed bandit problem, a classic dilemma in decision-making under uncertainty that has found new life in data science. They break down how companies like Netflix and Amazo
In this episode, Lucas and Luna explore manifold learning, a family of techniques that lets data scientists see the true shape of high-dimensional data by projecting it into lower dimensions. Using the classic example of
Synthetic data is quietly becoming one of the most practical tools in a data scientist's kit — letting teams train models on realistic datasets without exposing sensitive records. In this episode, Lucas and Luna dig into
In this episode, Lucas and Luna explore how data scientists are using causal forests to estimate personalized treatment effects—moving beyond average outcomes to understand which customers, patients, or users respond bes
Episode 152 of The Data Science Podcast dives into quantile regression, a technique that gives data scientists a fuller picture of uncertainty than standard mean-focused models. Lucas and Luna explore how predicting the
In this episode, Lucas and Luna dive into gradient boosting, the machine learning technique behind countless winning models on Kaggle and in production systems. They explore how it works, why it's so effective, and the p
In this milestone 150th episode, Lucas and Luna dive into probability calibration — the practice that makes a model's predicted confidence match real-world outcomes. They anchor the discussion with a concrete example: a
When a model's predictions start drifting from reality, the first question every data scientist asks is: where did the data go wrong? In this episode, Lucas and Luna explore a subtle but powerful debugging technique: dat
In this episode of The Data Science Podcast, Lucas and Luna explore how knowledge graphs are transforming recommendation engines beyond collaborative filtering. They dive into a real example: how a music streaming servic
Lucas and Luna explore Shapley values, the game-theory concept behind SHAP, which data scientists use to fairly distribute model predictions among features. They break down the math in plain English, show why it's more p
On this episode, Lucas and Luna drill into anomaly detection in manufacturing, using the semiconductor industry as a case study. They explore how a fab uses sensor data from etching tools to spot subtle process drifts be
Lucas and Luna explore how data scientists are using embeddings to transform search. From semantic understanding to handling typos and synonyms, they unpack the shift from keyword matching to meaning-based retrieval. Wit
Bias in machine learning isn't just a fairness problem — it's a business risk. In this episode, Lucas and Luna unpack how data scientists are using fairness metrics like demographic parity and equalized odds to audit mod
Recommender systems quietly power everything from your streaming queue to your shopping feed, but most teams still retrain them on a fixed schedule. In this episode, Lucas and Luna dig into online learning — the techniqu
In this episode, Lucas and Luna unpack the quiet killer of machine learning systems: data drift. They trace how models trained on 2024 data start stumbling by 2026, using a concrete example from retail demand forecasting
In episode 141, Lucas and Luna dig into counterfactual reasoning in data science — the art of asking what would have happened if a marketing campaign never ran. They open with a concrete example: a national grocery chain
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The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations is hosted by Fexingo. The show is categorised under business and has published 154 episodes.
The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations has published 154 episodes.
The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations regularly covers business. It sits in the business category.
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Episodes of The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations average 10 minutes. a focused format where a clear narrative arc and tight preparation matter most.
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