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Data Engineering: Reliable Data Systems
Data engineering is the design of reliable data systems: ingestion, storage, contracts, transformations, orchestration, lineage, quality, and serving.
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Value at Risk and Expected Shortfall: Quantiles and Tail Risk
Value at Risk is a quantile of a loss distribution. Expected Shortfall averages the tail beyond that quantile. Both are model-dependent and require careful h...
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Data Science for Carbon Reduction: Measurement Before Optimization
Data science can support decarbonization, but credible carbon analysis starts with system boundaries, attribution, baselines, life-cycle accounting, and unce...
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Renewable Energy Optimization Under Uncertainty
Renewable-energy optimization is a constrained stochastic control problem involving generation, storage, transmission, demand, uncertainty, and market rules.
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Managing Data Science Under Uncertainty
Data science and engineering both contain uncertainty. The useful management distinction is between discovery risk, implementation risk, and operational risk...
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Mathematics for Machine Learning: What Each Tool Is For
Machine learning rests on linear algebra, calculus, probability, statistics, and optimization, but each mathematical tool matters because of the structure it...
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Mann-Whitney U Test: What It Actually Tests
The Mann-Whitney U test compares pairwise ordering between two independent distributions. It is not automatically a test of medians or a fallback whenever no...
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Continuous and Binary Variables: Correlation and Regression
A binary variable and a continuous variable can be related through mean differences, point-biserial correlation, regression, or latent-threshold models. The ...
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Linear Probability Models vs Logistic Regression
The linear probability model and logistic regression target the same conditional event probability through different functional forms. The choice depends on ...
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Mann-Kendall Trend Test: Assumptions and Pitfalls
The Mann-Kendall test detects monotone association with time, but serial dependence, seasonality, ties, change points, and irregular sampling must be handled...
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