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id course-da-visualization
type course
title Data Visualization with an Agent
summary Challenge course — turn a clean sales dataset into three spec'd charts plus a summary.json whose numbers are verified against the data. Charts must be generated from data, never hardcoded.
lang en-US
content_version 2
status reviewed
reviewed_on 2026-11-07
badge
id name_en name_zh requires
course-da-visualization
Visualization Challenger
可视化挑战者
All five checkpoints claimed (L01–L05)
course_id course-da-visualization

Data Visualization with an Agent

Coach P — guided learning

When the learner asks to start a lesson or requests guided help, act as Coach P. At your first introduction in that session, say:

I'm Coach P, an AI coach running inside your own agent, using your model. I'll help you reason through the task; verify.py checks the objective gates. Reflection checkpoints still need your own answers and explicit confirmation.

Ask what the learner tried and what they expect before suggesting a change. Give the smallest useful hint, then wait for their answer. Name the failing test and explain which contract it checks; do not paste a solution or edit solution/. Follow this course's lesson order and file boundaries. If the learner cannot explain a result, return to that test and ask them to predict the next run. Before --attest or a claim submission, obtain the learner's own answers and confirmation for those checkpoints; never answer or attest on their behalf. The Skill's authorization and human-confirmation gates still apply. For a verification-only or progress request, perform that workflow without forcing a lesson. Coach P is an AI teaching style, not a human teacher or a certification; do not promise learning or career outcomes. FlyPython does not run the model or receive the chat conversation through this workflow; course-file, account, and claim requests are separate API data.

TL;DR: challenge course — you solve, the agent is your tool. Turn scenario/sales-clean/sales_daily.csv into three spec'd PNG charts and a summary.json. Verification checks the files exist, are valid PNGs, and that the numbers match the dataset. It does not judge aesthetics — say so honestly anywhere you show the output.

What you build

A matplotlib script that produces out/revenue_by_region.png, out/daily_revenue.png, out/revenue_histogram.png, and out/summary.json — the machine-checkable half of "here are my charts".

Setup

cd courses/da-visualization
python -m venv .venv               # or: uv venv
source .venv/bin/activate          # Windows: .venv\Scripts\activate
pip install -r requirements.txt    # pandas + matplotlib (or: uv pip install -r requirements.txt)

Challenges (checkpoints)

# Challenge Gate
L01 A chart is packaged evidence — what does each chart claim? self-attested
L02 Spec first — write down which numbers each chart must show self-attested
L03 Three charts and a summary — implement the contract objective (starter suite)
L04 Numbers match the charts — both suites green objective (both suites)
L05 Honest boundary — what verification cannot see self-attested

Badge contract

  • Badge: Visualization Challenger (badge id da-visualization) — all five checkpoints claimed.
  • Challenges: L01–L05, 10 points each; +50 course-badge bonus on flypython.com.
  • Evidence: python verify.py — L03/L04 objective, L01/L02/L05 attested. Self-reported evidence, never a certificate.

What this course does NOT cover

Chart design, dashboards, seaborn/plotly, interactive charts. The Agg backend is deliberate: charts are evidence artifacts, not a UI.

Folder map

COURSE.md / COURSE_cn.md   this file (EN / 中文)
lessons/L01.md … L05.md    challenge notes (each has an _cn.md pair)
scenario/sales-clean/      sales_daily.csv (clean input)
TASK.md / TASK_cn.md       the contract
starter/charts.py          unfinished implementation
solution/charts.py         reviewed solution
tests/test_charts.py       contract suite (read-only)
verify.py                  objective pass/fail + claim codes
requirements.txt           pinned pandas + matplotlib
REVIEW.md                  maintainer run-through record

Code is MIT-licensed; prose is CC BY 4.0 (see repository LICENSE).