Data Science Tutorials

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.

The Mann-Whitney U test is a rank-based nonparametric test for comparing two independent groups when data doesn't meet the normality assumption required by the t-test.

GELU is a smooth, probabilistic activation function that outperforms simpler alternatives like ReLU in deep learning architectures, and has become the default choice in transformer models like BERT and GPT.

Newton's method is an iterative root-finding algorithm that uses tangent line approximations to close in on the solution of equations that have no closed-form answer.

The t statistic helps you decide whether a difference in your data is meaningful or just random variation. This guide explains how it works, how to calculate it, and how to use it in real testing scenarios with clear, step-by-step examples.

Master OpenAI's Codex CLI to automate data workflows. Learn to conduct EDA, build Python ETL pipelines, and generate tests directly from your local terminal.

A practical guide to geometric series covering the finite and infinite sum formulas, convergence conditions, and real-world applications across finance, physics, and computer science.

A practical guide to Maclaurin series covering the core formula, common expansions, convergence rules, and real-world applications in numerical methods, physics, and machine learning.

The F statistic is used to test whether a model explains variation in the data better than random chance. This guide explains what the F statistic means, how it is calculated, and how to interpret it.

Learn what an objective function is, how it works in optimization and machine learning, and how to define and interpret it with real examples.

Discover how the Pearson correlation coefficient quantifies the strength and direction of relationships in your data. Learn to calculate, interpret, and apply it using Python, R, and Excel.