Dataset Overview
Europass Survey
June - September 2019
Description
- Name: occupations_ngrams
- Number of observations: 7,081,544
- Number of features: 10
- Description: This dataset collects statistics derived from free-text analysis on the job-related responses collected in the Europass Survey. It describes the frequency of one and two-word terms with respect to certain given variables.
- Sources: Europass Survey: June - September 2019
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| Feature | Name | Description | Form1 | Stat2 | Text3 | |
|---|---|---|---|---|---|---|
| term | Term | Unigram or bigram of keywords in a free-text response. | ● | ● |
|
|
| job_esco | Job ESCO | ESCO classification for job. | ● | ● |
|
|
| job_isco | Job ISCO 3 | ISCO level 3 classification for job. | ● | ● |
|
|
| recruitment_year | Recruitment Year | Year of recruitment. | ● |
|
||
| termination_year | Termination Year | Year of termination. | ● |
|
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| locale | CV Language | Language used to write CV. | ● |
|
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| country | Country | Country of residence. | ● |
|
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| birth_year | Birth Year | Year of birth. | ● |
|
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| gender | Gender | Female, male or missing value. | ● |
|
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| responses | Responses | Number of responses with a particular combination of values for the above variables. | ● |
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| 1 Variable derives directly from a Europass CV; | ||||||
| 2 Variable is a statistical transformation of one or more Europass CV variables; | ||||||
| 3 Variable is a result of information retrieval using text mining |
Summary Statistics
term
| Feature | Result |
|---|---|
| Variable type | factor (nominal) |
| Number of missing values* | 0 ( 0.00% ) |
| Number of unique values | 78445 |
| * No missing values permitted. |
job_esco
| Feature | Result |
|---|---|
| Variable type | character (nominal) |
| Number of missing values* | 737298 ( 10.41% ) |
| Number of unique values | 2931 |
|
* Missing features required for estimation, or unidentifi ed from classification algorithm. |
job_isco
| Feature | Result |
|---|---|
| Variable type | character (nominal) |
| Number of missing values* | 737298 ( 10.41% ) |
| Number of unique values | 126 |
|
* Missing features required for estimation, or unidentifi ed from classification algorithm. |
recruitment_year
| Feature | Result |
|---|---|
| Variable type | character (nominal) |
| Number of missing values* | 124092 ( 1.75% ) |
| Number of unique values | 61 |
| * WorkExperience.Period.From.Year missing. |
termination_year
| Feature | Result |
|---|---|
| Variable type | character (nominal) |
| Number of missing values* | 1308776 ( 18.48% ) |
| Number of unique values | 60 |
| * Education.Period.To.Year missing. |
locale
| Feature | Result |
|---|---|
| Variable type | character (nominal) |
| Number of missing values* | 0 ( 0.00% ) |
| Number of unique values | 29 |
| * SkillsPassport.Locale missing. |
country
| Feature | Result |
|---|---|
| Variable type | character (nominal) |
| Number of missing values* | 330832 ( 4.67% ) |
| Number of unique values | 184 |
| * SkillsPassport…Country.Code missing. |
birth_year
| Feature | Result |
|---|---|
| Variable type | character (nominal) |
| Number of missing values* | 2468464 ( 34.86% ) |
| Number of unique values | 84 |
| * SkillsPassport…Birthdate.Year missing. |
gender
| Feature | Result |
|---|---|
| Variable type | character (nominal) |
| Number of missing values* | 3074872 ( 43.42% ) |
| Number of unique values | 3 |
| * SkillsPassport…Gender.Code missing. |
responses
| Feature | Result |
|---|---|
| Variable type | integer |
| Number of missing values* | 0 ( 0.00% ) |
| Number of unique values | 210 |
| Min. | 1 |
| 1st Qu. | 1 |
| Median | 1 |
| Mean | 1.14 |
| 3rd Qu. | 1 |
| Max. | 656 |
| * No missing values permitted. |
