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Data Analysis — major_occupations.csv

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Data Analysis — major_occupations.csv

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CCurran Kelleher
Last edited Oct 22, 2026
Created on Oct 22, 2026

Created from react-d3-starter template

major_occupations.csv — Dataset Documentation

Dataset Overview

  • Filename: major_occupations.csv
  • Rows: 1,000
  • Columns: 7
  • Size: ~152 KB

This dataset appears to link fields of academic study (undergraduate majors, identified by a 4-digit CIP code) to occupations (identified by SOC codes), each paired with numeric scores measuring exposure to artificial intelligence. The three exposure scores — an overall score plus language- and image-specific scores — appear to be standardized indices (values include negatives and fall roughly between −3.3 and +2.4), but their exact construction is not documented in the data itself and should be treated as tentative.

Column Descriptions

Column Apparent meaning (tentative) Type (Munzner) Missing / unusual values
cip4 4-digit CIP code identifying the field/major (e.g., 0100 = Agriculture). Stored as a number, but semantically a categorical code. Range 100–4504, 222 unique. Nominal (encoded as quantitative) 0 missing. Note that leading zeros may be stripped by numeric parsing.
field_of_study Human-readable name of the field of study. 417 unique values. Nominal 0 missing. Values may contain commas, slashes, and parentheses.
soc SOC occupation code (e.g., 19-1011). 289 unique. Nominal (identifier) 0 missing.
occupation Human-readable occupation title. 289 unique. Nominal 0 missing. May contain embedded commas (quoted in CSV).
ai_exposure Overall AI exposure score for the occupation. Range approx. −2.11 to 1.53, mean ≈ 0.84. Standardization method unknown. Quantitative 0 missing.
ai_exposure_language Language-related AI exposure score. Range approx. −1.66 to 1.86, mean ≈ 0.84. Quantitative 0 missing.
ai_exposure_image Image-related AI exposure score. Range approx. −3.31 to 2.42, mean ≈ 0.78. Quantitative 0 missing.

Parsing assumptions: The header is the first CSV record and may span multiple physical lines due to quoted cells with embedded newlines; a real CSV parser is used. All numeric columns are parsed with +; invalid or missing numeric values are coerced and filtered out rather than assumed to be zero.

Visualization Mappings

Chart type: Scatter plot.

  • X axis: ai_exposure_language (quantitative)
  • Y axis: ai_exposure_image (quantitative)
  • Color: ai_exposure (overall score), a continuous sequential scale
  • Tooltip: on hover, shows occupation, field_of_study, soc, and all three exposure scores
  • Zoom/pan: d3-zoom on the plot area for exploring the dense central cluster

Rationale: The dataset's core interest is how occupations relate along language vs. image AI exposure, and how those compare to the overall exposure score. A scatter plot places each of the 1,000 field–occupation pairs in that two-dimensional space, while encoding the overall score as color lets readers see whether the overall measure tracks the language/image components. Hover tooltips are essential because the categorical labels (occupation, field_of_study) are too numerous for a legend.