{"podcast":{"title":"The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations","slug":"the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831","podcast_index_feed_id":7871831,"rss_url":"https://feeds.fexingo.com/business/the-data-science-podcast.xml","website_url":"https://www.fexingo.com/","image_url":"https://audio.fexingo.com/business/the-data-science-podcast/cover.png","author":"Fexingo","episode_count":118,"summary":"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 gi…","last_synced_at":"2026-07-19T08:17:23.323447+00:00","page_url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831"},"episode":{"title":"How Data Scientists Use Temporal Fusion Transformers for Time Series Forecasting","slug":"how-data-scientists-use-temporal-fusion-transformers-for-time-series-forecasting","published_at":"2026-07-09T08:40:15+00:00","page_url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-use-temporal-fusion-transformers-for-time-series-forecasting","show_page_url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831","url":"https://audio.fexingo.com/business/the-data-science-podcast/episode-0099.mp3","audio_url":"https://audio.fexingo.com/business/the-data-science-podcast/episode-0099.mp3","summary":"In this episode, Lucas and Luna dive into Temporal Fusion Transformers (TFT), a deep learning architecture that has changed how data scientists approach time series forecasting. They walk through a concrete case from a major European electricity utility that used TFT to predict hourly load across 20,000 substations with unprecedented accuracy. You'll learn how TFT handles multiple time series simultaneously, incorporates static metadata, and produces interpretable attention weights that let analysts trust the model's predictions. Lucas explains the key architectural innovations — variable selection networks, gated residual connections, and quantile outputs — and Luna presses on the practical tradeoffs versus simpler models like Prophet or Gradient Boosting. If you're a data scientist looking to level up your forecasting toolkit, this conversation gives you the why, the how, and the gotchas. #TemporalFusionTransformers #TimeSeriesForecasting #DeepLearning #InterpretableML #EnergyForecasting #DataScience #MachineLearning #PredictiveModeling #AttentionMechanism #QuantileForecasting #LucasAndLuna #FexingoBusiness #BusinessPodcast #Technology #DataAnalytics #ModelDeployment #FeatureEngineering #UtilityIndustry Keep every episode free: buymeacoffee.com/fexingo","meta_description":"In this episode, Lucas and Luna dive into Temporal Fusion Transformers (TFT), a deep learning architecture that has changed how data scientists approach t…","key_points":[],"chapters":[],"topics":[],"duration_seconds":546,"processing_state":"not_requested","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/episodes/how-data-scientists-use-temporal-fusion-transformers-for-time-series-forecasting/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-use-temporal-fusion-transformers-for-time-series-forecasting.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}