Data Science Tutorials

Learn how the probability mass function defines discrete probability distributions. Explore its properties, examples, and differences from probability density functions.

Understand the role of the Hessian matrix in multivariable calculus and optimization. Learn how it’s used to analyze curvature, locate critical points, and guide algorithms in machine learning.

Learn about orthogonal matrices with practical examples and real-world applications in linear algebra and data science.

Use this method to make better predictions from real-world data. Learn how to minimize errors and find the most reliable trend line.

Understand how the t-distribution helps when sample sizes are small or population variance is unknown. Compare it to the normal and Z-distributions to learn when each is appropriate.

Understand how the law of large numbers supports statistical reasoning. See how it ensures consistency in averages over time and powers predictions in fields like sports, finance, and science.

In this tutorial, you will be introduced to filtering rows in SQL using the WHERE clause.

Explore the significance of the sigmoid function in neural networks and logistic regression, with practical insights for data science applications.

The triangle inequality is more than just a geometric principle - it's a cornerstone of distance measures in data science. Discover how this elegant mathematical concept powers algorithms across computational geometry, optimization, and machine learning.

Learn what R-squared means in regression analysis, how to calculate it, and when to use it to evaluate model performance. Compare it to related metrics with examples in R and Python.

Learn how to break down time series data into meaningful components like trend, seasonality, and residuals using additive and multiplicative models. Learn about both classical and STL methods. Explore seasonal adjustments and practice forecasting with decomposition models.

Learn the Gaussian elimination algorithm through step-by-step examples, code implementations, and practical applications in data science.