Validated case study · Data Analytics

Humach Retail: Sales Analytics

Turn a compact sales table into defensible business evidence with aggregation, grain checks and visual reasoning.

Download mini_sales.csvUse Python, SQL, Excel or your preferred workflow unless a task specifies otherwise.

Project dataset

Inspect the CSV before you answer the tasks

Scroll horizontally or vertically to examine the exact miniature data used by the validator.

date	region	channel	revenue	orders
2026-01-01	East	Online	120	3
2026-01-01	West	Store	95	2
2026-02-01	East	Store	150	4
2026-02-01	West	Online	110	3
2026-03-01	North	Online	135	3
2026-03-01	East	Online	90	2
2026-04-01	North	Store	160	4
2026-04-01	West	Online	125	3

8 rows5 columnsmini_sales.csv

Project workspace

Analyse → validate → submit.

Numeric and decision tasks are auto-checked. Written reasoning is checked for completeness and key ideas, then included in your local submission report.

0/5 validated

01

How many rows are in the sales table?

02

What is total revenue across all rows?

03

What is revenue per order across the whole table?

04

Before joining another table, what should you confirm about this table?

The row grain and key uniqueness The logo colour The browser theme

05

Write a two-sentence recommendation that cites at least one numerical result.

Submit your project locally

HumachLearn stores no server-side data. Submission creates a portable JSON report containing your answers, validation state and timestamp.