This story was originally published on HackerNoon at: https://hackernoon.com/principal-components-analysis-in-typescript-part-4-turning-pca-into-interpretable-factor-analysis.
Remember how PCA collapses data with 100 dimensions into a single dimension, wouldn't it be cool if this dimension were interpretable. Factor Analysis does that
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Now remember how PCA collapses data with 100 dimensions into a single dimension, wouldn't it be cool if this dimension was interpretable. For example, let's say the 100 columns were like stress, smoking frequency, alcohol ml etc etc.. you see where I am going with this, the final dimension would be something like cardiac arrest or premature demise. On that cheery note, let's figure out how PCA can actually be used to label this reduced dimension.

This story was originally published on HackerNoon at: https://hackernoon.com/the-llm-veneer-when-ai-sounds-smart-but-has-nothing-real-to-reason-over.
When AI sounds smart but has nothing real to reason over. A pet-tech case study in reference frames, longitudinal modeling, and missing data.
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Most AI products add a fluent interface before fixing the data model. The result: confident answers over the wrong structure. This is the LLM Veneer. A pet-tech case study in why data architecture matters more than conversational fluency.

This story was originally published on HackerNoon at: https://hackernoon.com/data-engineering-teams-need-a-different-version-of-agile.
This article explores which Agile practices actually help data engineering teams and which ceremonies often become operational overhead.
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Agile is useful for data engineering teams when it creates visibility, reduces context switching, and helps teams manage uncertainty. A visible backlog, regular delivery rhythm, and meaningful retrospectives usually help. Story point velocity tracking and status-report standups often become ceremony. The goal is not to “do Agile.” The goal is to create enough structure to prevent shortcuts, surface blockers early, and deliver reliable data work.