Data Science Tech Brief By HackerNoon

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Learn the latest data science updates in the tech world.

  1. 18 hr ago

    83 Blog Posts To Learn About Metrics

    This story was originally published on HackerNoon at: https://hackernoon.com/83-blog-posts-to-learn-about-metrics. Learn everything you need to know about Metrics via these 83 free HackerNoon blog posts. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #metrics, #learn, #learn-metrics, and more. This story was written by: @learn. Learn more about this writer by checking @learn's about page, and for more stories, please visit hackernoon.com.

    83 Blog Posts To Learn About Metrics

  2. 18 hr ago

    Why Your Python Cache Does the Same Work Twice (P.S: GIL Won’t Save You)

    This story was originally published on HackerNoon at: https://hackernoon.com/why-your-python-cache-does-the-same-work-twice-ps-gil-wont-save-you. Your Python cache can run the same work twice, even with the GIL. See the check-then-act race, the lru_cache gotcha, and how locks fix it. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #python-programming, #python-threading, #race-conditions, #python-gil, #thread-safety, #python-concurrency, #hackernoon-top-story, and more. This story was written by: @sohelalam79. Learn more about this writer by checking @sohelalam79's about page, and for more stories, please visit hackernoon.com. A cache that checks "is this key missing?" and then computes the value can run the same work many times when threads interleave, even with the GIL, because the GIL protects the interpreter, not your two-step logic. Hold one lock around the check and the work, ideally one lock per key.

    Why Your Python Cache Does the Same Work Twice (P.S: GIL Won’t Save You)

  3. 2 days ago

    AI-Assisted Reconciliation of Enterprise Data: Detecting and Explaining Cross-Platform Discrepancies

    This story was originally published on HackerNoon at: https://hackernoon.com/ai-assisted-reconciliation-of-enterprise-data-detecting-and-explaining-cross-platform-discrepancies. Learn how deterministic checks and AI work together to detect data discrepancies, explain their causes, and simplify financial reconciliation. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-engineering, #data-quality, #ai, #data-reconciliation, #lakehouse, #data-science, #data-regulations, #ai-assisted-reconciliation, and more. This story was written by: @ajaysikharam. Learn more about this writer by checking @ajaysikharam's about page, and for more stories, please visit hackernoon.com. Learn how deterministic checks and AI work together to detect data discrepancies, explain their causes, and simplify financial reconciliation.

    AI-Assisted Reconciliation of Enterprise Data: Detecting and Explaining Cross-Platform Discrepancies

  4. 3 days ago

    REPL While It Works: Inspect a Running Python Program Without Pausing It

    This story was originally published on HackerNoon at: https://hackernoon.com/repl-while-it-works-inspect-a-running-python-program-without-pausing-it. Use the python REPL or debugger console while a program is running, without interrupting or pausing execution. Exploit threading multitasking. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #python-programming, #python-developers, #repl, #python-pdb, #python, #python-inspection, #debugging, #coding, and more. This story was written by: @dakra. Learn more about this writer by checking @dakra's about page, and for more stories, please visit hackernoon.com. Usually, without interrupting it, you cannot interactively debug, call functions in, or change the values of variables in a running python program. This article’s technique removes the limitation.

    REPL While It Works: Inspect a Running Python Program Without Pausing It

  5. 3 days ago

    When a Dashboard Looks Perfect but the Data Is Wrong

    This story was originally published on HackerNoon at: https://hackernoon.com/when-a-dashboard-looks-perfect-but-the-data-is-wrong. Data definitions are crucial for accurate metrics, and their absence can lead to believable but wrong numbers that nobody questions. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-science, #data-engineering, #data-analytics, #data-visualization, #data-governance, #data-testing, #automated-data-testing, #business-intelligence, and more. This story was written by: @kkj-data. Learn more about this writer by checking @kkj-data's about page, and for more stories, please visit hackernoon.com. Data definitions are crucial for accurate metrics, and their absence can lead to believable but wrong numbers that nobody questions. Centralizing definitions and putting business logic in versioned, tested models can prevent metric disputes and ensure trust in data-driven decisions.

    When a Dashboard Looks Perfect but the Data Is Wrong

  6. 4 days ago

    No One Holds the Key: Threshold ECDSA, Part 1

    This story was originally published on HackerNoon at: https://hackernoon.com/no-one-holds-the-key-threshold-ecdsa-part-1. A deep dive to threshold elliptic curve digital signature algorithm (ECDSA) and its building blocks: oblivious transfer, MtA, OT-based protocol. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #multi-party-computation, #ecdsa, #oblivious-transfer, #cryptography, #distributed-key-generation, #elliptic-curve-cryptography, #crypto, #hackernoon-top-story, and more. This story was written by: @privatekeypirate. Learn more about this writer by checking @privatekeypirate's about page, and for more stories, please visit hackernoon.com. Threshold ECDSA lets t-of-n parties produce a standard ECDSA signature without anyone ever holding the full private key. Part 1 covers ECDSA basics and nonce pitfalls, commitment-based DKG that blocks rogue-key attacks, the history of threshold ECDSA, the HLNR18 protocol’s trade-offs, and OT-based multiplication.

    No One Holds the Key: Threshold ECDSA, Part 1

  7. 4 days ago

    OData at Scale: When to Use CDS Views, RAP, or API Business Hub in Hybrid SAP

    This story was originally published on HackerNoon at: https://hackernoon.com/odata-at-scale-when-to-use-cds-views-rap-or-api-business-hub-in-hybrid-sap. Choose between CDS-view OData, custom RAP services, and API Business Hub in hybrid SAP landscapes, with Cloud Connector, security, and performance trade-offs. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data, #api-development, #hybrid-cloud, #enterprise-technology, #rap, #cds, #odata, #odata-at-scale, and more. This story was written by: @anupbharti. Learn more about this writer by checking @anupbharti's about page, and for more stories, please visit hackernoon.com. Hybrid SAP OData is three decisions, not one: CDS views for read-heavy Fiori/analytics, RAP for anything beyond CRUD, and API Business Hub when an external party is involved — used together. Across the Cloud Connector, tuning changes: cache metadata, watch $expand size, prefer $select calls over deep expands. Propagate identity per user with short-lived certificates, not a shared technical user. A read view rarely stays internal — revisit governance and write questions as it gains consumers.

    OData at Scale: When to Use CDS Views, RAP, or API Business Hub in Hybrid SAP

  8. 5 days ago

    Your Drift Dashboard Can Be Green While Your Model Is Failing

    This story was originally published on HackerNoon at: https://hackernoon.com/your-drift-dashboard-can-be-green-while-your-model-is-failing. Build a Python drift monitor with SciPy and Evidently, then test five scenarios showing why data drift doesn't always mean model performance is degrading. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-drift-detection, #ai-data-documentation, #model-degradation, #python-scipy, #concept-drift, #credit-risk-modelling, #ml-model-calibration, #mlops, and more. This story was written by: @davidtaiwobalogun. Learn more about this writer by checking @davidtaiwobalogun's about page, and for more stories, please visit hackernoon.com. Using a synthetic credit-risk model, the author builds a drift-monitoring pipeline with SciPy, pandas, and Evidently and tests it across five controlled scenarios. The results demonstrate that harmless distribution changes can trigger alerts, genuine risk shifts may not require retraining, and concept drift can degrade performance without changing input distributions. The article proposes a monitoring approach combining statistical drift, prediction changes, data-quality checks, and delayed outcome evaluation.

    Your Drift Dashboard Can Be Green While Your Model Is Failing
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About

Learn the latest data science updates in the tech world.

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  • Creator
    HackerNoon
  • Years Active
    2023 - 2026
  • Episodes
    100
  • Rating
    Clean
  • Copyright
    © 2026 HackerNoon

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