The Stratum Tutorial · 25 lessons

Master Stratum in 25 lessons

A free, self-paced course. Each lesson pairs a real dataset with a feature of Stratum: what it's for, the exact clicks to reproduce it, and the statistics behind the result.

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Arc 1 · Data: import & wrangling

01
Lesson 01

Import & First Look

Get data into Stratum in seconds and read your first summary.

Housing dataset
02
Lesson 02

Computed Columns & the Formula Engine

Derive new variables with 100+ functions — no SQL required.

Housing dataset
03
Lesson 03

Filtering & Sorting

Slice data interactively with the dockable Filter Inspector.

Housing dataset
04
Lesson 04

Reshaping Data

Join, stack, aggregate, dedup and sample — all in-app.

Retail dataset
05
Lesson 05

Histograms & Distribution Shape

Read the shape of a variable with bins, KDE and normal overlays.

Housing dataset

Arc 2 · Distribution & exploratory charts

06
Lesson 06

Box-Whisker & Violin Plots

Compare groups and spot outliers.

Housing dataset
07
Lesson 07

Density, Ridgeline & ECDF

Smooth and cumulative distribution views.

Process dataset
08
Lesson 08

Q-Q Plots & Normality

Judge whether your data is normal.

Process dataset
09
Lesson 09

Dot/Strip & Pareto

Rank categories and find the vital few.

Process dataset
10
Lesson 10

Pie, Bar & Mosaic

Composition and categorical association.

Survey dataset

Arc 3 · Relationships & inference

11
Lesson 11

Scatter, Trend & Bubble

Visualize relationships between variables.

Housing dataset
12
Lesson 12

Correlation Analysis

Pearson/Spearman matrices and significance highlighting.

Housing dataset
13
Lesson 13

The Summary Report

Master descriptive statistics.

Housing dataset
14
Lesson 14

Two-Sample Comparison

Test whether two groups differ.

Clinical dataset
15
Lesson 15

One- & Two-Way ANOVA

Compare many groups and factors.

Process dataset
16
Lesson 16

MANOVA, Contingency & Differences

Multivariate and categorical inference.

Process + survey
17
Lesson 17

Linear Regression

Models with full diagnostics.

Housing dataset

Arc 4 · Time series & quality control

18
Lesson 18

Trend & Time-Series

Trend, seasonality, and moving-average smoothing.

Retail dataset
19
Lesson 19

Control Charts I — Shewhart

I-MR and X-bar R/S charts.

Process dataset
20
Lesson 20

Control Charts II — Attributes

p/np, c/u and Nelson rules.

Process dataset
21
Lesson 21

Control Charts III — CUSUM & EWMA

Catch small, slow process shifts.

Process dataset

Arc 5 · Data mining / machine learning

22
Lesson 22

PCA & Clustering

Reduce dimensions, find natural groups.

Wine dataset (public)
23
Lesson 23

Decision Trees

Interpretable predictive models.

Housing dataset
24
Lesson 24

Random Forest & Boosted Trees

Stronger predictions via ensembles.

Wine dataset (public)

Arc 6 · Scale, accessibility & output

25
Lesson 25

Stratum at Scale: 5 Million Rows

Performance, accessibility and publication-ready output.

IoT dataset (5M rows)