Data Science Tutorials

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.

Explore the fundamentals, applications, and comparisons of Hamming distance in various fields.

Explore the fundamentals of the Pareto distribution and its applications. Learn how this statistical concept can be applied in various fields. Discover practical examples and visual aids to enhance your understanding.

Discover why Euler’s number is everywhere—from banking and biology to machine learning and meteorology—and how this constant powers continuous growth and change.

Visualize inequality at a glance using the Lorenz curve. Discover how to create and interpret Lorenz curves using Python to analyze inequality across income, crime, and other real-world datasets.