Unit 07 · lab

Evidence Check: Trace the Lifecycle

Choose a small data-science scenario such as school energy use, esports match statistics, public transportation delays, or robot test reliability.

Your evidence

Draw or write a lifecycle trace:

Question → Acquire → Prepare → Analyze → Visualize → Communicate → Revisit

For every stage, identify:

the action being performed;
the evidence produced;
one failure mode; and
the decision that determines what happens next.

Then identify where ethics, privacy, or bias must be checked.

Make the world change

Open the Data Drift Simulator. Change mean shift, noise, and missingness independently. Identify which lifecycle stage should detect the change and which stage should send the analyst backward.

Check your reasoning

The lifecycle is not a one-way conveyor belt. Show at least one place where weak evidence should send the analyst backward.

Done when

Your trace includes evidence, failure, and revision—not just seven boxes connected by arrows.

1. Dataset

Fictional robot test runs. No student or personal data.

run	model	sec	battery	°C	done
R-101	Atlas	52	88	22	yes
R-102	Atlas	57	79	23	yes
R-103	Nova	64	74	24	yes
R-104	Nova	71	—	27	no
R-105	Pulse	48	92	21	yes
R-106	Pulse	55	84	25	yes
R-107	Atlas	68	69	29	no
R-107	Atlas	68	69	29	no

2. Quick chart

ViewY

Change the variables before deciding what the data “says.” The same rows can support different questions.

3. Editable Pandas analysis

Runs locally in your browser. First use downloads Pandas and its verified dependencies from Robotnix.

import pandas as pd

rows = [
    {"run_id": "R-101", "model": "Atlas", "duration_s": 52, "battery_pct": 88, "ambient_c": 22, "completed": True},
    {"run_id": "R-102", "model": "Atlas", "duration_s": 57, "battery_pct": 79, "ambient_c": 23, "completed": True},
    {"run_id": "R-103", "model": "Nova", "duration_s": 64, "battery_pct": 74, "ambient_c": 24, "completed": True},
    {"run_id": "R-104", "model": "Nova", "duration_s": 71, "battery_pct": None, "ambient_c": 27, "completed": False},
    {"run_id": "R-105", "model": "Pulse", "duration_s": 48, "battery_pct": 92, "ambient_c": 21, "completed": True},
    {"run_id": "R-106", "model": "Pulse", "duration_s": 55, "battery_pct": 84, "ambient_c": 25, "completed": True},
    {"run_id": "R-107", "model": "Atlas ", "duration_s": 68, "battery_pct": 69, "ambient_c": 29, "completed": False},
    {"run_id": "R-107", "model": "Atlas ", "duration_s": 68, "battery_pct": 69, "ambient_c": 29, "completed": False},
]
df = pd.DataFrame(rows)

quality = {
    "rows_received": len(df),
    "duplicate_run_ids": int(df["run_id"].duplicated().sum()),
    "missing_battery": int(df["battery_pct"].isna().sum()),
    "model_labels": sorted(df["model"].unique().tolist()),
}

for key, value in quality.items():
    print(f"{key}: {value}")

print("\nWhich lifecycle stage should send us backward?")

Output

Run the analysis to see evidence.

Ready. Predict before you run.

Runtime boundary: Robotnix serves the core Pyodide runtime and the SHA-256-verified Pandas dependency set. This workbench does not expose pip, micropip, a shell, filesystem projects, cloud credentials, or arbitrary package installation.