Data / Beginner

A daily-demand story from synthetic bike-rental data

Create a small synthetic dataset and tell a careful story about daily bike-rental demand. Separate observations from assumptions and limitations instead of presenting a causal or market claim.

Start with this brief Example submissions

First 30 minutes

Before you start

Basic spreadsheet skills are enough; coding is optional. Use the supplied synthetic 14-day bicycle-demand and rain scenario, and do not infer causation from it.

First 30 minutes

Open the example CSV, inspect its columns, and write one cleaning note. Then sketch the first chart you would make for daily demand and rain.

A sufficient small-scope submission

A CSV and synthetic-data note describe the dataset.An annotated synthetic CSV
Cleaning notes or code show how the data was checked.Cleaning notes; code is optional
Two readable charts communicate the pattern.Two clear charts
A one-page summary separates observation, assumption, and limitation.A summary of observations, assumptions, and limits

Extend it if you want

Add a comparison view by day type or a short note about a missing value, then explain how that choice could change the reading of the chart.

Example file layout

bike-demand.csv
cleaning-notes.md
charts.png
summary.md

Example presentation

This hypothetical student example explores a synthetic 14-day table of bicycle demand and rain indicators. The student labels the data as synthetic, describes one cleaning choice, and shows two simple charts: daily demand and demand alongside the rain indicator. The summary separates visible observations from assumptions and states that the small table cannot establish causality. No real transport study, forecast, or operational recommendation is claimed. It is a format example for explaining evidence and limits, not a completed real analysis.

Representative concept example; not real client work or your personal work. Learn the format and write your own decisions.

A starter template is not a completed submission. Produce your own evidence.

01Scenario

Imagine that a fictional bike-rental team wants to explore how daily demand and rain might appear together. Build your own synthetic 14-day dataset with date, demand, and rain columns; there are no real people or real market records in this exercise.

Goal

Turn a tidy dataset into two clear charts and a one-page summary. Explain what the data appears to show while making clear that synthetic values cannot establish causality or describe a real market.

02Scope and limits

Use only your own synthetic 14-day data with date, demand, and rain columns.
Do not claim causality, real customer behavior, or real market performance.
Keep observations, assumptions, and limitations visibly separate in the summary.

03Production steps

1
Create the 14 rows and write a short description of how the synthetic values were made.
2
Check the data, record cleaning notes or code, and flag any deliberate choices.
3
Make two readable charts that answer different questions about the small dataset.
4
Write a one-page summary separating observations, assumptions, and limitations.

04Evidence to submit

A CSV and synthetic-data note describe the dataset.
Cleaning notes or code show how the data was checked.
Two readable charts communicate the pattern.
A one-page summary separates observation, assumption, and limitation.

05Ask yourself

Which statement is directly supported by the rows?
Which assumption shaped the synthetic data?
What would real data be needed to investigate next?

06Sources and licensing

Use code, chart assets, and other materials that you created or that have a suitable open license. Record license names and source URLs for outside materials. Disclose team or AI contributions, and do not imply that synthetic data came from a real service.

Read the ethical presentation guide