Name: Linkedin - Skills, Industry, Importance, Country
Creator: Kaggle
License: http://opendatacommons.org/licenses/dbcl/1.0/

About this Dataset

Linkedin - Skills, Industry, Importance, Country

In this data, you can find insights about skill importance in the specific industry or occupation. You can find relations about how did the skills changed over the time and their importance ranks. Also, you can find all those data for different countries.
You can use this data to find the skills of the specific industry or occupation, or get insights about how the industry changed over time with each country.

Tables

Kaggle Country Industry Year Skill Change

@kaggle.eslemgler_linkedin_skills_industry_importance_country.kaggle_country_industry_year_skill_change

57.94 kB
3,336 rows
7 columns

CREATE TABLE kaggle_country_industry_year_skill_change (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "country_code" VARCHAR,
  "country_name" VARCHAR,
  "industry_group_id" BIGINT,
  "industry_group_name" VARCHAR,
  "year_2" BIGINT,
  "skill_change" DOUBLE
);

Kaggle Country Lower Upper Bound

@kaggle.eslemgler_linkedin_skills_industry_importance_country.kaggle_country_lower_upper_bound

4.66 kB
25 rows
4 columns

CREATE TABLE kaggle_country_lower_upper_bound (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "country_name" VARCHAR,
  "lower_bound_skill_change" DOUBLE,
  "upper_bound_skill_change" DOUBLE
);

Kaggle Country Name Industry Year Skill Impt Rank

@kaggle.eslemgler_linkedin_skills_industry_importance_country.kaggle_country_name_industry_year_skill_impt_rank

53.09 kB
5,570 rows
6 columns

CREATE TABLE kaggle_country_name_industry_year_skill_impt_rank (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "country_name" VARCHAR,
  "industry_group_name" VARCHAR,
  "year" BIGINT,
  "skill_name" VARCHAR,
  "impt_rank" BIGINT
);

Kaggle Country Name Industry Year Skill Impt Rank Diff 1a46b5a9

@kaggle.eslemgler_linkedin_skills_industry_importance_country.kaggle_country_name_industry_year_skill_impt_rank_diff_1a46b5a9

103.84 kB
5,570 rows
9 columns

CREATE TABLE kaggle_country_name_industry_year_skill_impt_rank_diff_1a46b5a9 (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "country_name" VARCHAR,
  "industry_group_name" VARCHAR,
  "year" BIGINT,
  "skill_name" VARCHAR,
  "impt_rank" BIGINT,
  "rank_diff" VARCHAR,
  "highest_ss" DOUBLE,
  "closest_skill" VARCHAR
);

Kaggle Country Name Job Year Skill Impt Rank

@kaggle.eslemgler_linkedin_skills_industry_importance_country.kaggle_country_name_job_year_skill_impt_rank

219.03 kB
24,880 rows
6 columns

CREATE TABLE kaggle_country_name_job_year_skill_impt_rank (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "country_name" VARCHAR,
  "occupation_name" VARCHAR,
  "year" BIGINT,
  "skill_name" VARCHAR,
  "impt_rank" BIGINT
);

Kaggle Country Occupadion Id Name Year Skill Change

@kaggle.eslemgler_linkedin_skills_industry_importance_country.kaggle_country_occupadion_id_name_year_skill_change

271.41 kB
14,928 rows
7 columns

CREATE TABLE kaggle_country_occupadion_id_name_year_skill_change (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "country_code" VARCHAR,
  "country_name" VARCHAR,
  "occupation_id" BIGINT,
  "occupation_name" VARCHAR,
  "year_2" BIGINT,
  "skill_change" DOUBLE
);

Kaggle Country Year Skill Change

@kaggle.eslemgler_linkedin_skills_industry_importance_country.kaggle_country_year_skill_change

6.89 kB
150 rows
5 columns

CREATE TABLE kaggle_country_year_skill_change (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "country_code" VARCHAR,
  "country_name" VARCHAR,
  "year_2" BIGINT,
  "skill_change" DOUBLE
);

Kaggle Country Year Skill Type Percentage

@kaggle.eslemgler_linkedin_skills_industry_importance_country.kaggle_country_year_skill_type_percentage

18.04 kB
875 rows
6 columns

CREATE TABLE kaggle_country_year_skill_type_percentage (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "country_code" VARCHAR,
  "country_name" VARCHAR,
  "year" BIGINT,
  "skill_type_digital" VARCHAR,
  "pen_percentage" DOUBLE
);