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Exploring the frontiers of AI, software architecture, and engineering.
Missing Data: When to Drop, When to Impute, When to Worry
Missing values are not one problem — they are three, with different remedies. The pattern of missingness decides whether you can drop, impute, or must investigate before doing anything.
Read Article →Anomalies in Time Series: Spikes, Level Shifts, and Seasonal Surprises
A spike is not a level shift, and neither is a seasonal dip. Anomaly detection on time series fails when it treats all three the same. Here is the residual-based approach that tells them apart.
Read Article →Parsing Megabytes Without Freezing the UI
A 50 MB CSV should not freeze a browser tab. Chunked parsing, Web Workers and transferable buffers keep the interface alive — here is the engineering pattern.
Read Article →Shipping Your Analysis: Choosing the Right Export Format
Every export format lies in a different way. CSV loses types, JSON loses labels, HTML keeps everything human-readable. Match the format to the consumer and the decision.
Read Article →Choosing the Right Chart for the Metric: A Taxonomy for People Who Ship Dashboards
A chart-selection taxonomy: trends, distributions, compositions, comparisons — plus the chart crimes (dual axes, 3D pies, cumulative lines) to avoid.
Read Article →From Statistics to Sentences: Writing Executive Summaries People Actually Read
How to turn descriptive statistics into decision-ready narratives: what an automated executive summary should say, what it must never claim, and why.
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