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From EDA to features: encoding, scaling, and the leakage traps in between
A machine learning model can achieve a perfect validation score and still be completely useless in production.
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Dimensionality reduction for seeing: PCA, t-SNE, and UMAP compared honestly
A dataset with 64 features cannot be plotted directly on a two-dimensional screen. But a machine learning engineer can transform those 64 features into two coordinates and produce a surprisingly beaut
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Simpson’s paradox: when your aggregate answer is exactly wrong
Imagine you are a data analyst at an e-commerce company. Your team uses two shipping carriers, ExpressSwift and BudgetFlow, and management wants to know which one delivers packages on time more reliab
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Detecting data drift in production with the K–S test and PSI
A machine-learning model can be perfectly healthy from an infrastructure perspective and still become increasingly wrong.
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Chi-squared tests for categorical data: independence and goodness-of-fit
Many important questions in data analysis involve categories rather than numerical measurements:
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Bootstrap and permutation tests: confidence intervals and p-values without a formula
You have two versions of an API endpoint. The new version appears faster, but its latency distribution is skewed, a handful of requests take much longer than the rest, and the median matters more to y
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Nonparametric tests when your assumptions break: Mann–Whitney, Wilcoxon, Kruskal–Wallis
Imagine you’re analyzing an A/B experiment for an e-commerce platform.
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Is it really normal? QQ-plots and normality tests (Shapiro–Wilk, Anderson–Darling)
A histogram looks roughly bell-shaped. The sample mean and median are close. The skewness is small. Your statistical software reports a p-value of 0.0001.
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Kolmogorov–Smirnov and friends: goodness-of-fit and two-sample distribution tests
A service can have the same average latency, the same standard deviation, and the same request volume as yesterday—and still deliver a dramatically different user experience.
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Hypothesis testing for engineers: p-values, effect sizes, and A/B pitfalls
Imagine you’re responsible for the checkout experience of an e-commerce platform.