{"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 Build Recommendation Engines from Scratch","slug":"how-data-scientists-build-recommendation-engines-from-scratch","published_at":"2026-06-10T08:08:25+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-build-recommendation-engines-from-scratch","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-0042.mp3","audio_url":"https://audio.fexingo.com/business/the-data-science-podcast/episode-0042.mp3","summary":"Lucas and Luna walk through the real-world process of building a recommendation engine, using the open-source MovieLens dataset as their running case. They cover collaborative filtering, matrix factorization, the cold-start problem, and the engineering trade-offs between offline accuracy and online performance. Lucas explains why the Netflix Prize algorithm never made it into production, and Luna challenges the assumption that more data always helps. The episode ends with a practical checklist for any data scientist starting their first recommender system. #RecommendationEngine #CollaborativeFiltering #MatrixFactorization #MovieLens #NetflixPrize #ColdStart #DataScience #MachineLearning #Tech #Technology #FexingoBusiness #BusinessPodcast #DataEngineering #OfflineMetrics #OnlineAbtesting #Sparsity #ImplicitFeedback #ProductionML Keep every episode free: buymeacoffee.com/fexingo","meta_description":"Lucas and Luna walk through the real-world process of building a recommendation engine, using the open-source MovieLens dataset as their running case. The…","key_points":[],"chapters":[],"topics":[],"duration_seconds":577,"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-build-recommendation-engines-from-scratch/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-build-recommendation-engines-from-scratch.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}