地球资源数据云——数据资源详情

LinkedIn 数据职位数据集

发布时间：2026-03-17 14:30:40资源ID：2032010133297860609资源类型：免费

该数据集《LinkedIn Data Jobs Dataset》主要用于监督学习任务，数据形态以文本为主。 题目说明：This dataset contains job postings scraped from LinkedIn 任务类型：文本监督学习。 建议流程：先做文本清洗与分词，再比较 TF - IDF+线性模型 与 预训练语言模型。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：clean_jobs.csv。 LinkedIn Data Jobs Dataset Scraped LinkedIn job postings for data - related roles (Data Analyst, Data Engineer, Data Scientist, etc.) Overview This dataset contains job postings scraped from LinkedIn, including job titles, companies, locations, descriptions, and job types (remote/hybrid/onsite). The data can be used for data cleaning, NLP analysis, skill extraction, and building AI - powered job application tools. Dataset Features Column Name Description Title Job title (e.g., "Data Analyst," "Product Analyst") Company Hiring company name Location Job location (city/country) Description Full job description (may include company info) Job Type Remote, Hybrid, or Onsite (if available) Potential Use Cases Data Cleaning & Normalization – Standardize job titles, locations, and descriptions. NLP & Skill Extraction – Find the most in - demand skills (Python, SQL, ML, etc.). Job Type Analysis – Compare remote vs. onsite job trends. AI - Powered Job Tools – Build a Streamlit app to generate:

LinkedIn 数据职位数据集

摘要概览

该数据集《LinkedIn Data Jobs Dataset》主要用于监督学习任务，数据形态以文本为主。 题目说明：This dataset contains job postings scraped from LinkedIn

任务类型：文本监督学习。

建议流程：先做文本清洗与分词，再比较 TF - IDF+线性模型 与 预训练语言模型。

评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。

可用文件：clean_jobs.csv。

LinkedIn Data Jobs Dataset Scraped LinkedIn job postings for data - related roles (Data Analyst, Data Engineer, Data Scientist, etc.)

Overview

This dataset contains job postings scraped from LinkedIn, including job titles, companies, locations, descriptions, and job types (remote/hybrid/onsite). The data can be used for data cleaning, NLP analysis, skill extraction, and building AI - powered job application tools.

Dataset Features Column Name Description Title Job title (e.g., "Data Analyst," "Product Analyst") Company Hiring company name Location Job location (city/country) Description Full job description (may include company info) Job Type Remote, Hybrid, or Onsite (if available) Potential Use Cases Data Cleaning & Normalization – Standardize job titles, locations, and descriptions.

NLP & Skill Extraction – Find the most in - demand skills (Python, SQL, ML, etc.). Job Type Analysis – Compare remote vs. onsite job trends. AI - Powered Job Tools – Build a Streamlit app to generate: