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Data Science Tech Brief By HackerNoon

HackerNoon

Learn the latest data science updates in the tech world.

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Episodios

100 episodios

  1. Sampling Ad Exchange Events at Scale: What We Kept, What We Dropped, and Why 10:19
  2. 83 Blog Posts To Learn About Metrics 21:49
  3. Why Your Python Cache Does the Same Work Twice (P.S: GIL Won’t Save You) 8:26
  4. AI-Assisted Reconciliation of Enterprise Data: Detecting and Explaining Cross-Platform Discrepancies 7:39
  5. REPL While It Works: Inspect a Running Python Program Without Pausing It 3:02
  6. When a Dashboard Looks Perfect but the Data Is Wrong 7:32
  7. No One Holds the Key: Threshold ECDSA, Part 1 29:52
  8. OData at Scale: When to Use CDS Views, RAP, or API Business Hub in Hybrid SAP 31:04
  9. Your Drift Dashboard Can Be Green While Your Model Is Failing 15:22
  10. Why Faster Algorithms Aren't Enough to Build High-Performance Data Platforms 19:08
  11. Why Modern Data Lakehouses Fail at Scale: The Metadata Problem Nobody Talks About 9:32
  12. The Hidden Cost of Real-Time Data: Why Faster Pipelines Don't Always Deliver Better Decisions 9:44
  13. On November 1, Your 24/7 Dashboard Will Report a 25-Hour Day 5:53
  14. How to Solve Blind Spots in Your Architecture by Building a Production-Grade Uptime SaaS 3:37
  15. One Rogue pip install Away from a Breach 4:43
  16. Stop Using For-Loops for Everything: Advanced Python Iteration Patterns You Should Know. 3:35
  17. Why Knowledge Graphs and Ontologies Are Making a Comeback in the Age of AI 37:41
  18. Building an Auditable Pipeline for Cross-Language News Coverage Analysis 28:31
  19. How Shallow and Deep Copies Work in Python 8:31
  20. How I Fixed a Data Ingestion Bottleneck With Date Partitioning 9:12
  21. 224 Blog Posts To Learn About Web Scraping 55:14
  22. Mastering Databricks: A Developer’s Guide to Lakehouse Architecture & PySpark Pipelines 4:41
  23. 1.89 Seasons: A Baseball Experiment About Hiring and Human Judgment 11:59
  24. Knowledge Graphs: What They Are, Where They Live, and How to Build One 8:07
  25. I Built an AI Job Tracker While Job Hunting: The AI Was the Easy Part 9:02
  26. AI Agents Are Opening Up, but What About the Data? 8:39
  27. Scaling a Kafka Consumer From 4K to 25K Events per Second While Preserving Ordering 5:26
  28. Modeling Valve-Closure Pressure Spikes in Miniature Fluid Systems 10:34
  29. Fivetran vs. Apache SeaTunnel: Managed ELT or Open-Source Control? 17:19
  30. The Narrative Wars: Tracking Public Web Influence Operations Across the Globe 6:47
  31. Teaching Funnels To Understand Non-Linear Customer Journeys 17:41
  32. Building an AI-Powered A2P SMS Fraud Detection Platform Using XGBoost: A Machine Learning Approach 10:51
  33. Measuring Decision Confidence in Business Intelligence and Analytics 9:29
  34. Your Healthcare Integration Is Only as Good as Its Data Mapping Decisions 5:52
  35. Choosing a Python Sentence Boundary Detection Library 13:53
  36. This Is How Observability Starts - With Modeling Pipeline Runs 6:20
  37. Why AI-Assisted Data Engineering Needs Executable Specifications 12:54
  38. Dashboard Trust Is a Data Governance Problem, Not a BI Tool Problem 6:57
  39. I Had 1,011 SaaS Users, but Only 3 Core Actions and $0 MRR 11:59
  40. From Python Script Hell to a Modern Data Integration Framework 19:35
  41. Why Data Pipelines Keep Breaking—and How Data Contracts Fix Them 7:55
  42. Your Dashboards Are Production Systems. Start Monitoring Them Like One. 12:19
  43. The Hidden Work Behind Every Dashboard: Why Enterprise Data Validation Takes Longer Than You Think 6:27
  44. 67 Blog Posts To Learn About Ab Testing 17:02
  45. I Tried Every Way to Scrape Amazon in 2026. Here is What Actually Works 10:14
  46. How We Built a Per-Plant CO2 Dataset for 4,551 Power Stations Worldwide 4:58
  47. Eliminating Data Latency with Event-Driven Pipelines at Enterprise Scale 19:44
  48. Scaling Self-Service Analytics in Regulated Banking With Metadata-Driven Design 6:38
  49. How to Rotate Proxies Without Breaking Login Sessions 8:17
  50. I Built an Open-Source Firebase Analytics Alternative Because I Hit 1M Events/Day Once Too Many 10:03