Data Science Tutorials

Kernel density estimation is a nonparametric method for estimating the shape of a data distribution without assuming a fixed model. Learn the formula, bandwidth selection, and hands-on implementation in Python and R.

A practical walkthrough of the assumptions behind logistic regression, the diagnostics that catch violations in Python and R, and the alternatives to reach for when the assumptions don't hold.

A practical guide to spline regression, covering how piecewise polynomials and knots model nonlinear relationships, the main spline types, and how to fit them in Python and R.

A practical guide to GLMs - what they are, how their three components work together, and how to fit and interpret them in Python and R.

A detailed walkthrough of overfitting and underfitting in machine learning, including how to identify each failure mode, why it happens, and how to fix it through the bias-variance tradeoff.

See how an end-to-end MLOps pipeline predicts World Cup 2026 results, from automated retraining and DVC to a 10,000-run Monte Carlo simulation of the bracket.

A guide to Markov Chain Monte Carlo - covering how it works, why it’s used, the most common algorithms, and how to apply it in Python for Bayesian inference.

Gradient clipping is a one-line training fix that prevents exploding gradients from ruining deep neural network training. This guide covers how it works, the two main clipping methods, threshold selection, and implementation in PyTorch and TensorFlow.

Support Vector Regression is a margin-based regression method that ignores small errors intentionally, handles nonlinear relationships through kernels, and holds up on noisy real-world data where standard regression comes up short.

Singular Value Decomposition (SVD) is a matrix factorization method that breaks any matrix into three simpler components, revealing its underlying structure.

A conceptual guide to the kernel trick - what it is, how it enables SVMs and other kernel-based models, and when to use it over other approaches to nonlinear modeling.

A practical guide to the Kruskal-Wallis test - what it is, how it works, when to use it over ANOVA, and how to run and interpret it in Python and R.