This story was originally published on HackerNoon at: https://hackernoon.com/why-data-quality-is-becoming-a-core-developer-experience-metric.
Bad data secretly slows development. Learn why data quality APIs are becoming core DX infrastructure in API-first systems and how they accelerate teams.
Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-quality, #developer-experience, #software-architecture, #engineering-productivity, #data-quality-apis, #api-first-architecture, #distributed-systems, #good-company, and more.

This story was written by: @melissaindia. Learn more about this writer by checking @melissaindia's about page, and for more stories, please visit hackernoon.com.

In API-first systems, poor data quality (invalid emails, duplicate records, etc.) creates unpredictable bugs, forces defensive coding, and makes releases feel risky. This "hidden tax" consumes time and mental energy that should go to building features. The fix? Treat data quality as core infrastructure. By using real-time validation APIs at the point of ingestion, you create predictable systems, simplify business logic, and build developer confidence. This turns a vicious cycle of complexity into a virtuous cycle of velocity and better architecture. Bottom line: Investing in data quality isn't just operational hygiene—it's a direct investment in your team's ability to ship faster and with more confidence.

This story was originally published on HackerNoon at: https://hackernoon.com/why-accuracy-fails-for-uplift-models-and-what-to-use-instead.
When it comes to uplift modeling, traditional performance metrics commonly used for other machine learning tasks may fall short.
Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-science, #uplift-modeling, #data-analysis, #machine-learning, #uplift-models, #area-under-uplift, #uplift@k, #cg-and-qini, and more.

This story was written by: @eltsefon. Learn more about this writer by checking @eltsefon's about page, and for more stories, please visit hackernoon.com.

When it comes to uplift modeling, traditional performance metrics commonly used for other machine learning tasks may fall short.

This story was originally published on HackerNoon at: https://hackernoon.com/turning-your-data-swamp-into-gold-a-developers-guide-to-nlp-on-legacy-logs.
A practical NLP pipeline for cleaning legacy maintenance logs using normalization, TF-IDF, and cosine similarity to detect fraud and improve data quality.
Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-analysis, #atypical-data, #maintenance-log-analysis, #nlp-cleaning-pipeline, #python-text-normalization, #enterprise-data-quality, #tf-idf-vectorization, #data-cleaning-automation, and more.

This story was written by: @dippusingh. Learn more about this writer by checking @dippusingh's about page, and for more stories, please visit hackernoon.com.

The NLP Cleaning Pipeline is a tool to clean, vectorize, and analyze unstructured "free-text" logs. It uses Python 3.9+ and Scikit-Learn for vectorization and similarity metrics. The pipeline uses Unicode normalization, the Thesaurus, and case folding to remove noise.