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:

"About Me" sections tailored to job descriptions.

Auto-generated cover letters based on job requirements.

GitHub Collaboration Want to contribute? Join the project here: 🔗 https://github.com/JoyKimaiyo/Web-scraping-data-jobs-and-automating-about-me-section

Acknowledgments Data scraped from LinkedIn for educational/non-commercial use.

LinkedIn is a widely used professional networking platform that hosts millions of job postings. This dataset contains 1.3 million job listings scraped from LinkedIn in the year 2024.

This dataset can be used for various research tasks such as job market analysis, skills mapping, job recommendation systems, and more.

If you find this dataset valuable, please upvote 😊💼

This is the same master dataset that powers SkillExplorer

Interesting Task Ideas:

1. Practice data cleaning on raw data.
2. Analyze the most in-demand job titles or industries in different cities or countries.
3. Identify the top companies hiring for specific job positions.
4. Utilize the skills data to determine the most sought-after skills in different job categories.
5. Build a job recommendation system based on user profiles and job listing data.
6. Discover patterns in job types or levels across different industries.
7. Identify skill gaps in the job market to inform educational or training programs.
8. Explore the relationship between job title and required skills.

Photo by Clem Onojeghuo on Unsplash

The data comprises job-related information from LinkedIn job postings scraped over a 2-day period. Key features include company details and job-specific information like title, description, and salary. The dataset provides a comprehensive view for exploring factors influencing job posting characteristics and has been reformatted from its original source to improve its compatibility among various machine learning algorithms.

Dataset

This dataset was created by RATNESH SATYARTHI

Released under Apache 2.0

Contents

The dataset contains information on 30,000+ job postings collected from LinkedIn till the year 2023 which provides a rich source of information on job postings on LinkedIn, with concise information on the job title, company, location, and other key attributes of each posting. This data can be used to gain insights into employment trends and dynamics, identify key skills and experiences that are in high demand, and optimize job postings to attract the right candidates. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F18065122%2Ff51a9373d9b0bbc78235ae4be8dbdce5%2F1.jpeg?generation=1751200071553062&alt=media" alt="">

Data science is a rapidly growing field in the tech industry, and LinkedIn is a popular platform for finding job opportunities in this domain.

This dataset provides valuable insights into data analyst job postings, including the required skills and software proficiency sought by employers.

If you find this dataset useful, don't forget to hit the upvote button! 😊💝

Checkout my top datasets

• Top Spotify Songs in 73 Countries
• Wages by Education in the USA
• Amazon Products Dataset (1.4M Products)
• TMDB 950K Movies
• Amazon Kindle Books (130K Books)

Interesting Task Ideas:

1. Analyze the most in-demand skills and software for data analyst positions.
2. Find common job titles in the data science field.
3. Explore the geographical distribution of data engineering job opportunities.
4. Identify the most sought-after skills

Photo by Lukas Blazek on Unsplash

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

This dataset contains 10,000 people on LinkedIn as well as the jobs held by that person on the 1st of January 2018. It was scraped using a browser extension. A rough example of which can be found here https://chrome.google.com/webstore/detail/edna-scrape-ext/hcchbehfooacdlebbpgodbfleicooahi.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

LinkedIn Job Postings Dataset

Description

This dataset contains information about job postings on LinkedIn. The data is divided into several files, each containing different aspects of the job postings:

1. job_postings.csv: This file contains detailed information about each job posting, including the job title, description, salary, work type, location, and more.
2. companies.csv: This file contains detailed information about each company that posted a job, including the company name, website, description, size, location, and more.
3. company_industries.csv: This file contains the industries associated with each company.
4. company_specialities.csv: This file contains the specialties associated with each company.
5. employee_counts.csv: This file contains the employee and follower counts for each company.
6. benefits.csv: This file contains the benefits associated with each job.
7. job_industries.csv: This file contains the industries associated with each job.
8. job_skills.csv: This file contains the skills associated with each job.

Usage

This dataset can be used for various purposes such as: - Analyzing the job market - Analyzing company trends - Analyzing salary trends - Building a job recommendation system - Natural Language Processing (NLP) tasks such as keyword extraction, topic modeling, etc.

Acknowledgements

This dataset was collected from LinkedIn. Please note that the data may be subject to LinkedIn's terms of use.

License

This dataset is released under the Open Database License (ODbL).

This dataset contains a curated collection of job listings sourced from LinkedIn, featuring a variety of positions across multiple industries and locations. Each entry includes essential details such as job title, company name, job location, employment type, and base pay range, alongside a comprehensive job summary and required qualifications.

Key Features:

• Job Titles & Companies: Explore a wide range of roles from top companies including Amazon, AT&T, and PVH Corp.
• Location Data: Analyze job opportunities across various regions in the United States and the UK.
• Employment Types: Includes full-time, part-time, and contract positions, catering to diverse career preferences.
• Salary Information: Insights into base pay ranges for various roles, aiding in competitive salary analysis.
• Seniority Levels: From entry-level to mid-senior roles, this dataset covers multiple experience levels.

This dataset is ideal for researchers, data scientists, and job seekers looking to analyze job market trends, understand salary expectations, or develop predictive models for career growth. Use this resource to gain insights into the evolving job landscape and make informed career decisions.

Dataset

This dataset was created by davideev9

Contents

Collated 8 different data source. Filtered for only Data and ML jobs, titles and descriptions. Applied text data cleaning and preprocessing, documented here: https://tianyimasf.github.io/ai/data-cleaning/.

1. LinkedIn-Tech-Job-Data: A compilation of job posts and metadata scraped from various tech categories on LinkedIn

2. Data Analyst Jobs: This dataset was created by picklesueat and contains more than 2000 job listing for data analyst positions

3. US Job Postings from 2023-05-05: This dataset is an excerpt of our web scraping activities at Techmap.io and contains a sample of 33k Job Postings from the USA on May 5th 2023.

4. LinkedIn Job Postings Dataset: This dataset contains information about job postings on LinkedIn.

5. LinkedIn Job Postings - Machine Learning Data Set: The data comprises job-related information from LinkedIn job postings scraped over a 2-day period.

6. Linkedin Canada: Data Science Jobs 2024: The "LinkedIn Canada: Data Science Jobs 2024" dataset presents an insightful overview of the data science job market in Canada as sourced from LinkedIn.

7. Data Scientist - Linkedin Job Postings: This dataset provides valuable insights into data science job postings, including the required skills and software proficiency sought by employers.

8. LinkedIn Job Postings Dataset: This dataset contains information about job postings on LinkedIn.

Initially used for my project analyzing data job market, including analyzing titles, skills, and company functions. Could be used for other purposes like posting generation.

Dataset

This dataset was created by Alex Ma

Released under Apache 2.0

Contents

The following dataset is extracted using an API and contains job data from LinkedIN about 10 common job roles in Inida.

The data can be used for a comprehensive analysis on application trends, peak posting times, popular job titles, company dynamics, geographical patterns, sector-specific insights, job freshness, company-specific behaviors, and predictive modeling. Predictive modeling can be used to anticipate job market dynamics. In summary, this dataset facilitates a thorough exploration of the job market, providing actionable insights for both job seekers and employers.

Dataset Columns

id: Unique identifier for each job posting (Integer).

publishedAt: Date when the job was published (String, formatted as 'YYYY-MM-DD').

title: Job title (String).

companyName: Name of the hiring company (String).

postedTime: Time since the job was posted (String).

applicationsCount: Number of job applications received (Float).

description: Job description, including required skills (String).

contractType: Type of employment contract (String).

experienceLevel: Level of experience required for the job (String).

workType: Type of work arrangement (String).

sector: Industry sector of the job (String).

companyId: Unique identifier for the hiring company (Integer).

city: City where the job is located (String).

state: State where the job is located (String).

recently_posted_jobs: Indicates whether the job is recently posted (String, 'Yes' or 'No').

Dataset

This dataset was created by Steve Marcello Liem

Contents

The jobs_linkedin.csv file comprises the scraping results obtained from LinkedIn website. It includes the following columns: 1. title: Signifies the job title associated with each entry. 2. location: Provides information about the job's location. 3. time: Indicates the timestamp when the job post was uploaded. 4. link: Contains a unique identifier (UUID) and a direct link to the respective job post. 5. desc: Contains the comprehensive description of each job opportunity.

For a more detailed exploration of my NLP work, please refer to: - LinkedIn-NLP-Notebook - LinkedIn-NLP&DL-Notebook

The dataset contains information on 30,000+ job postings collected from LinkedIn till the year 2023 which provides a rich source of information on job postings on LinkedIn, with concise information on the job title, company, location, and other key attributes of each posting. This data can be used to gain insights into employment trends and dynamics, identify key skills and experiences that are in high demand, and optimize job postings to attract the right candidates.

Taxonomy of the Dataset https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13623947%2F85fde0e9bcd9e6532b63e65ca1e5b58a%2FWhatsApp%20Image%202024-02-27%20at%2012.12.59.jpeg?generation=1709016197299811&alt=media" alt="">

Dataset

This dataset was created by AJ Strauman-Scott

Released under Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC-SA 3.0 IGO)

Contents

LinkedIn Job Salary Classification Using SVM and KNN This project uses machine learning to predict salary ranges (Low, Medium, High) for LinkedIn job postings by comparing Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) algorithms. Approach:

Extract features from job postings: title, description, skills, experience, location, and company size Preprocess and clean the dataset Train both SVM and KNN models on labeled data Evaluate performance using accuracy, precision, recall, and F1-score

Goal: Develop an automated system to help job seekers understand salary expectations and assist recruiters in creating competitive compensation packages.

This dataset contains job postings from Linkedin from 2023 with the following features It can be used to analyze the current trends based on job positions, location,company nameetc

Introduction The OpenWeb Ninja JSearch API offers a fast, reliable, and comprehensive real-time job postings data and salary data from Google for Job - the largest job aggregate on the web. The API sources job postings and salary data from LinkedIn, Indeed, Glassdoor, ZipRecruiter, Monster + all public job sites on the web.

The API supports several options and filters, including filtering by posting date, job title, location, remote jobs, job requirements, employer, and many other options. Each job posting includes 40+ job data points, including job title, job description, required experience, education, skills, job location, job expiration, and many other details.

See it in action here: https://google.com/search?gl=us&ibp=htl;jobs&q=marketing+in+texas.