Episodes

  • How Data Scientists Use Embedded Analytics for Product-Led Growth

    How Data Scientists Use Embedded Analytics for Product-Led Growth
    Jul 5 2026
    Episode 92 of The Data Science Podcast with Fexingo dives into embedded analytics—the practice of integrating dashboards, reports, and AI insights directly into customer-facing products. Lucas and Luna explore how companies like Notion and Canva use embedded analytics to drive product-led growth, reduce churn, and monetize data. They walk through a real case: a fictional B2B SaaS platform that cut customer onboarding time by 40% by embedding usage analytics inside its app. The hosts discuss the technical stack (frontend SDKs, API-first BI tools, semantic layers), the UX pitfalls to avoid (like overwhelming users with charts), and the business model shift from selling reports to embedding them. If you're a data scientist or product manager wondering how to turn dashboards into a growth engine, this episode gives you a concrete playbook. #EmbeddedAnalytics #ProductLedGrowth #DataScience #BusinessIntelligence #Analytics #Notion #Canva #B2BSaaS #CustomerRetention #Monetization #SemanticLayer #APIFirst #UX #ProductAnalytics #Technology #FexingoBusiness #BusinessPodcast #DataDriven Keep every episode free: buymeacoffee.com/fexingo
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    8 mins
  • How Data Scientists Use Causal Inference for Marketing Attribution

    How Data Scientists Use Causal Inference for Marketing Attribution
    Jul 5 2026
    Most marketing attribution models are correlational — they tell you what happened, not why. In this episode, Lucas and Luna break down how data scientists are using causal inference techniques, specifically double machine learning and instrumental variables, to measure the true incremental impact of ad spend. Using a real 2025 case from a mid-market e-commerce brand that ran geo-lift tests across 50 DMAs, they show how naive last-click attribution overestimated Facebook ROI by 60 percent while underestimating podcast ads by 40 percent. The hosts explain why off-the-shelf attribution is broken, how double ML handles high-dimensional confounders like seasonality and competitor activity, and why the field is shifting from 'more data' to 'better questions.' Specific metrics, concrete numbers, no vague theory. #CausalInference #MarketingAttribution #DataScience #DoubleMachineLearning #InstrumentalVariables #GeoLift #IncrementalMeasurement #ROI #DigitalMarketing #Tech #BusinessPodcast #DataDriven #Analytics #Econometrics #CausalEffect #Confounders #AdTech #FexingoBusiness Keep every episode free: buymeacoffee.com/fexingo
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    10 mins
  • How Data Scientists Use Knowledge Graphs for RAG

    How Data Scientists Use Knowledge Graphs for RAG
    Jul 4 2026
    In this episode, Lucas and Luna explore how knowledge graphs are supercharging retrieval-augmented generation (RAG) systems. They break down a concrete example: how a financial services firm used a knowledge graph built from SEC filings and earnings call transcripts to reduce hallucination in their Q&A chatbot by 40 percent. The hosts explain why flat vector search alone often fails, how graph traversal adds context, and what it takes to maintain a dynamic knowledge graph. They also touch on trade-offs like latency and engineering complexity. If you've wondered when RAG needs more than a vector database, this episode gives you the practical answer. #KnowledgeGraphs #RAG #RetrievalAugmentedGeneration #Hallucination #VectorSearch #GraphTraversal #Neo4j #SECFilings #EarningsCalls #NLP #LLMOps #DataScience #Technology #FexingoBusiness #BusinessPodcast #MachineLearning #GraphDB #EntityResolution Keep every episode free: buymeacoffee.com/fexingo
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    8 mins
  • How Data Scientists Use Graph Neural Networks for Recommendation

    How Data Scientists Use Graph Neural Networks for Recommendation
    Jul 4 2026
    Lucas and Luna explore how graph neural networks are transforming recommendation systems, using the example of Pinterest's PinSage model. They break down how GNNs capture relational data like user-item interactions to generate high-quality recommendations, discuss the challenges of scaling to billions of nodes, and compare GNN-based approaches to traditional collaborative filtering. The episode includes a concrete explanation of message-passing in graphs and real-world performance metrics from Pinterest's deployment. #GraphNeuralNetworks #RecommendationSystems #Pinterest #PinSage #MachineLearning #DataScience #Technology #CollaborativeFiltering #MessagePassing #NodeEmbeddings #GraphConvolution #Scaling #UserItemGraph #IndustrialML #Personalization #FexingoBusiness #BusinessPodcast #DataDriven Keep every episode free: buymeacoffee.com/fexingo
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    11 mins
  • How Data Scientists Use Dimensionality Reduction for Visualization

    How Data Scientists Use Dimensionality Reduction for Visualization
    Jul 3 2026
    Episode 88 of The Data Science Podcast dives into dimensionality reduction — but not for preprocessing. Lucas and Luna explore how data scientists use t-SNE and UMAP to visualize high-dimensional data, from customer segmentation to single-cell genomics. They discuss the trade-offs between global and local structure preservation, the risk of over-interpreting clusters, and why a 2D plot is never the whole truth. With concrete examples from retail analytics and biology, this episode gives you a practical framework for when to use t-SNE versus UMAP and how to avoid common pitfalls. If you've ever stared at a scatter plot and wondered if the patterns are real, this one's for you. #DimensionalityReduction #tSNE #UMAP #DataVisualization #MachineLearning #DataScience #Clustering #HighDimensionalData #SingleCellGenomics #CustomerSegmentation #PCA #Interpretability #Visual Analytics #FeatureEngineering #UnsupervisedLearning #Technology #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo
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    10 mins
  • How Data Scientists Use Manifold Learning for Dimensionality Reduction

    How Data Scientists Use Manifold Learning for Dimensionality Reduction
    Jul 3 2026
    In episode 87 of The Data Science Podcast, Lucas and Luna explore manifold learning—a powerful technique for dimensionality reduction that goes beyond PCA. They focus on t-SNE and UMAP, explaining how these algorithms preserve local and global structure in high-dimensional data. Using concrete examples from genomics and image datasets, they discuss when to use each method and common pitfalls like misleading visualizations. Lucas shares a cautionary tale about a team that over-interpreted a t-SNE plot, while Luna explains how UMAP scales to millions of points. They also touch on recent developments like parametric UMAP and its integration with deep learning. Perfect for data scientists who want to understand the trade-offs between linear and nonlinear dimensionality reduction. #ManifoldLearning #DimensionalityReduction #tSNE #UMAP #DataScience #MachineLearning #PCA #DataVisualization #TopologicalDataAnalysis #Genomics #ImageData #KLDivergence #CrossEntropy #ParametricUMAP #FexingoBusiness #BusinessPodcast #Technology #DataSciencePodcast Keep every episode free: buymeacoffee.com/fexingo
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    9 mins
  • How Data Scientists Use Pareto Frontiers for Multi-Objective Optimization

    How Data Scientists Use Pareto Frontiers for Multi-Objective Optimization
    Jul 2 2026
    In this episode, Lucas and Luna explore the concept of the Pareto frontier, a powerful framework for multi-objective optimization in data science. Starting with a concrete example—a ride-hailing company balancing driver wait times against passenger fares—they illustrate how Pareto optimality helps teams make trade-offs in model tuning, resource allocation, and product decisions. They discuss real-world applications in portfolio optimization, A/B testing, and reinforcement learning, where multiple conflicting objectives (e.g., profit vs. fairness, accuracy vs. latency) must be balanced. The hosts explain how to compute Pareto frontiers efficiently, why they're essential for interpretability, and how data scientists present these trade-offs to stakeholders. Tune in for a practical, example-driven conversation that will change how you think about optimization. #ParetoFrontier #MultiObjectiveOptimization #DataScience #MachineLearning #TradeOffs #Optimization #Tech #AIBusiness #ModelSelection #ReinforcementLearning #PortfolioOptimization #ABTesting #Fairness #Latency #Interpretability #FexingoBusiness #BusinessPodcast #DataDriven Keep every episode free: buymeacoffee.com/fexingo
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    8 mins
  • How Data Scientists Use Neural Radiance Fields for 3D Reconstruction

    How Data Scientists Use Neural Radiance Fields for 3D Reconstruction
    Jul 1 2026
    Lucas and Luna dive into Neural Radiance Fields (NeRFs), a technique that has reshaped 3D reconstruction from 2D images. They walk through how NeRFs work at a high level—converting sparse photographs into continuous volumetric scene representations—and why this matters for industries like autonomous driving, cultural heritage preservation, and virtual production. The episode anchors on a concrete example: how the Google Research team originally trained a NeRF on 100 images of a single scene to synthesize novel views with photorealistic quality, and how recent advances like Instant NGP have cut training time from hours to seconds. Lucas explains the key algorithmic steps: ray marching through a neural network that outputs color and density per point, then volumetric rendering to produce a pixel value. Luna questions where the bottleneck remains (data capture, not compute) and probes the real-world trade-off between quality and speed. The conversation stays grounded in tools and techniques data scientists actually use—no math beyond a brief mention of positional encoding—and closes by asking what happens when NeRFs meet generative AI for full scene editing. #NeuralRadianceFields #NeRF #3DReconstruction #ComputerVision #DeepLearning #InstantNGP #VolumetricRendering #RayMarching #GoogleResearch #PositionalEncoding #AutonomousDriving #VirtualProduction #CulturalHeritage #DataScience #Technology #FexingoBusiness #BusinessPodcast #MachineLearning Keep every episode free: buymeacoffee.com/fexingo
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    11 mins