Polish lab
Polish the visual until the takeaway is clear
A polished data visual removes noise before it asks the viewer to decide.
Puzzle target
Filter to high-satisfaction rows, chart revenue, then sort high to low.
Working dataset
| City | Region | Visits | Signups | Revenue | Satisfaction |
|---|---|---|---|---|---|
| Vancouver | West | 1,200 | 156 | $18,400 | 86 |
| Calgary | West | 860 | 95 | $9,900 | 74 |
| Toronto | Central | 2,100 | 252 | $32,600 | 82 |
| Montreal | East | 1,580 | 181 | $21,200 | 79 |
| Halifax | East | 640 | 83 | $8,700 | 88 |
Revenue visual
Tiny model
Feature
Visits
Train
60%
Predicted revenue
$21,793
This is intentionally small: change one feature, keep a holdout split, and explain what changed before trusting the model.
Flash cards
Review the key moves
What is the main idea behind Data Science Practice: Polish a Chart?
Lesson checks
Practice each idea before moving on
Short Mimo-style checks built from this lesson's code, terms, and sequence.
Which statement best captures the main point of this lesson?
Put the learning moves in the order that makes the concept easiest to apply.
Before charting or modeling a dataset, which move should come first?
Work directly with the dataset lab below. The controls change the rows, derived columns, visual, and tiny model summary in place.
| Practice surface | What you manipulate |
|---|---|
| Dataset | Campaign rows with visits, signups, revenue, satisfaction, and promo cost |
| Transform | Filters, derived net revenue, metric choice, and sorting |
| Model | One-feature prediction with a train and holdout split |
| Goal | Remove noisy rows and choose the chart metric that answers the question. |
Practice Task
- Filter to high-satisfaction rows, then sort revenue so the takeaway is readable.
- Watch the checklist in the lab update as the dataset state changes.
- Use the table, visual, and model card together before deciding what the data says.