{"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 Distributed Computing for Massive Datasets","slug":"how-data-scientists-use-distributed-computing-for-massive-datasets","published_at":"2026-06-13T21:07:20+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-distributed-computing-for-massive-datasets","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-0049.mp3","audio_url":"https://audio.fexingo.com/business/the-data-science-podcast/episode-0049.mp3","summary":"When your dataset outgrows a single machine, what do you do? In this episode, Lucas and Luna explore how data scientists use distributed computing frameworks like Apache Spark and Dask to process terabytes of data without crashing their laptops. They break down the key concept of data partitioning, explain why MapReduce is still relevant, and walk through a real example of how a mid-sized e-commerce company reorganized its log-processing pipeline to cut runtime from 14 hours to 47 minutes. Lucas shares a cautionary tale about shuffling bottlenecks that can ruin a cluster's performance, and Luna asks the practical question every team faces: when does it make sense to move from a single-node pandas workflow to a distributed system? They also discuss managed services like Databricks and AWS EMR versus rolling your own cluster. No prior distributed systems experience required — just a curiosity about what happens when data gets too big for a spreadsheet. #DataScience #DistributedComputing #ApacheSpark #Dask #MapReduce #BigData #DataEngineering #DataPartitioning #Shuffling #Databricks #AWSEmr #Pandas #Tech #Technology #FexingoBusiness #BusinessPodcast #Podcast #DataPodcast Keep every episode free: buymeacoffee.com/fexingo","meta_description":"When your dataset outgrows a single machine, what do you do? In this episode, Lucas and Luna explore how data scientists use distributed computing framewo…","key_points":[],"chapters":[],"topics":[],"duration_seconds":481,"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-distributed-computing-for-massive-datasets/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-distributed-computing-for-massive-datasets.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}