The mean is not always the right summary. The median is not always safe either. Today you learn when each measure of center and spread is appropriate, how to detect outliers, and why the same dataset can tell three different stories depending on which statistics you report.
By the end of this lesson you will compute mean, median, mode, variance, standard deviation, and IQR in Python, identify outliers with both z-scores and the IQR method, explain why mean and median diverge in skewed distributions, and choose the right summary for a given dataset.
mean is the foundation of Day 1. Every concept that follows builds on the mental model you establish here. The most effective approach is to understand the principle first, then apply it — skipping straight to implementation creates gaps that compound into confusion later.
Work through each example in this lesson sequentially. The concepts connect, and the order is deliberate. If something is unclear, slow down at that point rather than pushing past it — a ten-minute pause now saves hours of debugging later.
Understanding mean requires seeing it in motion. The code below is not a complete application — it is a minimal, working illustration of the key mechanism. Study the pattern, run it, break it deliberately, then fix it. That cycle builds real comprehension.
Once the basic pattern works, the logical next step is median. This is where the abstraction becomes useful — you move from understanding the mechanism to applying it to real problems. The transition is usually smaller than it feels. Most of the hard work happened in Section 1.
variance completes today's picture. It is where mean and median converge into a pattern you can apply to novel problems. This integration step is often where the day's learning consolidates — if the earlier sections felt abstract, this one typically makes them click.
Implementing mean alone handles the happy path. Real systems encounter edge cases, invalid input, and unexpected state. Missing median means missing those guards.
Combining mean with median gives you a complete, defensible implementation. The extra lines cost ten minutes; the robustness they add is worth hours of debugging time.
Several mistakes appear consistently when engineers encounter Descriptive Statistics — Summarizing Data You Can Trust for the first time. Recognizing them now costs nothing; encountering them in production costs hours.
Two intensive days (Thu–Fri) with an instructor who has taught thousands of engineers. Cohorts in 5 cities, June–June–October 2026 (Thu–Fri).
Reserve Your Seat — $1,490Before moving on, you should be able to answer these without looking: