1.3 Data Handling¶
Role at Stage 1: Foundations¶
Turn messy raw data into inspected, documented, and validated inputs. This part is one capability inside the stage. It should leave behind an artifact, measurements, and a short explanation of failure modes.
Explanation¶
This part has 4 sub-parts because the topic needs that many learning units to feel natural. Some stages have more parts and some have fewer; the structure follows the topic, not a fixed template.
Part Diagram¶
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flowchart LR
P["<b>1.3</b><br/>Data Handling"]
P --> S1["<b>1.3.1</b><br/>CSV JSON and Parquet"]
P --> S2["<b>1.3.2</b><br/>Pandas and NumPy<br/>Workflow"]
P --> S3["<b>1.3.3</b><br/>Data Validation and<br/>Schemas"]
P --> S4["<b>1.3.4</b><br/>Visualization for<br/>Debugging"]
P --> E["<b>Exam</b><br/>Part practice"]Sub-Parts¶
| Sub-part folder | What it explains |
|---|---|
| 1.3.1 CSV JSON and Parquet | CSV JSON and Parquet is the working skill inside Data Handling that helps you build the stage artifact, A tested Python data application with a CLI or API, setup notes, and a short data report, while collecting enough evidence to trust the result. |
| 1.3.2 Pandas and NumPy Workflow | Pandas and NumPy Workflow is the working skill inside Data Handling that helps you build the stage artifact, A tested Python data application with a CLI or API, setup notes, and a short data report, while collecting enough evidence to trust the result. |
| 1.3.3 Data Validation and Schemas | Data Validation and Schemas is the working skill inside Data Handling that helps you build the stage artifact, A tested Python data application with a CLI or API, setup notes, and a short data report, while collecting enough evidence to trust the result. |
| 1.3.4 Visualization for Debugging | Visualization for Debugging is the working skill inside Data Handling that helps you build the stage artifact, A tested Python data application with a CLI or API, setup notes, and a short data report, while collecting enough evidence to trust the result. |
What a Person Who Masters This Part Can Do¶
- Explain how Data Handling supports a tested python data application with a cli or api, setup notes, and a short data report..
- Build and inspect this artifact: Profile, clean, and export a real dataset.
- Measure progress with: Track missingness, duplicates, schema drift, outliers, and cleaning decisions.
- Debug at least one failure mode before moving to the next part.
Build and Measure¶
Build: Profile, clean, and export a real dataset.
Measure: Track missingness, duplicates, schema drift, outliers, and cleaning decisions.
Tests¶
Take one 30-question exam after studying this part. It opens in a new browser tab so the study page stays available.
Back to Stage¶
Return to Stage 1: Foundations.