Data Analysis Tutorials

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.

Learn how to use PI() in Excel so you have precision with mathematical formulas. Discover examples, syntax details, and tips for harnessing π in calculations.

Master Excel time formulas to calculate durations, handle time over 24 hours, and format time values for clear, accurate reporting.

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.

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.

Power BI users from Excel always find out that Power BI does not have a native COUNTIF() function. This article will show various ways to implement COUNTIF() in Power BI and its practical use cases.

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.

Understand the basic concepts of hierarchical clustering, how it works, and how to implement it in Python.

Learn how to remove extra rows quickly using sorting, formulas, and VBA, depending on what works best for you.