Data Science Tutorials

Learn what statistical arbitrage is, how quantitative traders identify temporary pricing relationships, and how strategies such as pairs trading and mean reversion work in practice.

Learn how state space models represent dynamic systems using hidden states and observations, including the state and observation equations, Kalman filtering, and time-series applications.

Learn what a residual plot is, how to interpret common patterns, and how residual plots help identify nonlinearity, heteroscedasticity, outliers, and other regression problems.

A practical, math-first look at saddle points - what makes them different from local minima and maxima, how to spot one using gradients and the Hessian, and why they show up so often in high-dimensional optimization and neural network training.

Learn what an augmented matrix is, how it represents systems of equations, and how to use row operations to solve them.

Learn what minima and maxima are, how local and global extrema differ, and how to find them using critical points, first and second derivative tests, and the closed-interval method.

Learn what an invertible matrix is, how to determine if a matrix is invertible, and why matrix inverses matter in linear algebra and data science.

Learn how SARIMA extends ARIMA to handle seasonality, understand its seven parameters, and build a working model in Python from data collection through forecasting.

A hands-on guide to the silhouette score, covering the formula, interpretation ranges, a scikit-learn example, how to use it for choosing the right number of clusters, and how it compares to other clustering metrics.

Knowing why spurious relationships happen, from confounders to sampling bias, is what separates a real finding from a statistical coincidence.

Learn what the lognormal distribution is, how it relates to the normal distribution, and where it applies across finance, biology, and machine learning.

Compare stock market data APIs for backtesting, dashboards, screeners, and AI agents, with Python examples covering splits, reliability, and fundamentals.