The project analyzed LinkedIn's job market by ethically scraping data using Python (Selenium, Beautiful Soup) and cleaning it with Pandas. It identified job market trends through Power BI and DAX, with insights presented via an interactive dashboard. Collaboration with cross-functional teams ensured a thorough analysis.

This project analyzes real LinkedIn job postings (2023–2024) to uncover insights about the modern job market.

This project focuses on analyzing LinkedIn data to understand trends in the data science job market and predict future job opportunities.

The project analyzed LinkedIn's job market by ethically scraping data using Python (Selenium, Beautiful Soup) and cleaning it with Pandas. It identified job market trends through Power BI and DAX, with insights presented via an interactive dashboard. Collaboration with cross-functional teams ensured a thorough analysis.

This project focuses on analyzing the job market for data-related and machine learning positions in a selected region (e.g., India or the United States). The primary goal is to understand the demand for roles such as Data Scientist, Data Engineer, and Machine Learning Engineer by collecting and examining job postings from Linkedin.

This project analyzes LinkedIn user engagement, connections, and job trends using Python, Pandas, and Power BI. It provides insights into networking patterns and career growth opportunities.

This project focuses on analyzing LinkedIn data to understand trends in the data science job market and predict future job opportunities.

The LinkedIn Job Trend Analysis project aims to study current job market demands by analyzing job postings available on LinkedIn. The project uses web scraping techniques to collect job-related data such as job titles, required skills, and job locations.

Interactive Power BI dashboard analyzing the Canadian tech job market using LinkedIn job postings. Includes data cleaning, feature engineering, and visualizations of job distribution, experience requirements, and skill demand trends.

A web scraping project focused on 'Data Scientist' jobs from LinkedIn. The project involves cleaning, analyzing, and visualizing the collected data to uncover trends in salaries, job opportunities, and required skills in the job market.

Takes your LinkedIn profile (or manually entered skills), analyzes job market data, and predicts the most suitable career path with required skills.

Scraped live job postings from LinkedIn and Naukri to analyze real-time skill demand trends in India's tech job market. Present a dashboard for the same.

Instahyre Job Analytics is a project analyzing job postings from Instahyre. It encompasses web scraping, data cleaning, and model-building to unveil job market insights. Data includes job posts, company LinkedIn followers, and industries. The processed data offers valuable trend insights.

recently completed a comprehensive data analytics project where I analyzed the LinkedIn Job Postings (2023 dataset) to uncover insights about the current job market, hiring trends, top-demand skills, and organizational trends

This project analyzes LinkedIn job postings to identify **skill demand trends across cities and roles**. By scraping job data, cleaning skill tags, and generating visualizations, the project provides insights into the **most in-demand skills** in the job market.

LinkedIn Job Analytics is a Python project using Beautiful Soup for web scraping and job market analysis. It extracts and analyzes job listings from LinkedIn, providing valuable insights on skills, industries, and geographic trends. Empowering job seekers and recruiters, it enhances the job search process with data-driven decision-making.

analyze the latest job market trends from popular job listing websites like Indeed, LinkedIn, Rozee.pk, and Glassdoor. The primary goal of this project imand job roles, technologies, are created with tools like Matplotlib and Seaborn to help users easily interpret the results.

This is a personalized job recommendation platform that uses Machine Learning (ML) to analyze job seekers’ skills, experience, and preferences, matching them with the most relevant job listings. It mimics the functionality of LinkedIn's job suggestion engine but provides customized recommendations based on real-time job market trends.

This project analyzes the LinkedIn Job Posting 2023–24 dataset of 13,000+ listings across industries to uncover trends in job titles, skills, and salaries. It provides insights into the evolving job market, helping job seekers, employees, and recruiters make informed decisions and identify growth opportunities.

This project analyzes job market trends by scraping job boards like Indeed, LinkedIn, and Glassdoor to collect data on job titles, required skills, industries, salaries, and locations. The goal is to identify high-demand skills, growing industries, and regional hiring patterns.

The project analyzed LinkedIn's job market by ethically scraping data using Python (Selenium, Beautiful Soup) and cleaning it with Pandas. It identified job market trends through Power BI and DAX, with insights presented via an interactive dashboard. Collaboration with cross-functional teams ensured a thorough analysis.

IT Job Trends Analysis explores the evolving IT job market using LinkedIn job postings data. We analyze trending roles, in-demand skills, salary distributions, and location-based demand. Leveraging real-world datasets from Kaggle, this project uncovers insights to guide tech career and hiring decisions.

This project analyzes the Australian job market for Data Analyst positions by scraping job postings from LinkedIn and performing comprehensive data analysis. The project provides insights into salary trends, required skills, company preferences, and geographical distribution of data analyst opportunities across Australia.

We are data analysts and we want to analyze the labor market in these roles. For this we are obtaining data from the jobs listed on LinkedIn in areas of data analysis for different countries.

📝 Task Overview This project focuses on scraping job listings from a website (e.g., Indeed, LinkedIn, or a static HTML page) to analyze the job market and identify the most in-demand roles and skills. The workflow includes data extraction, cleaning, and visualization.

Dual-lens analytics platform for the AI industry. Market Signals tracks job trends and technology adoption (93K HN posts, 1.3M LinkedIn jobs). Policy Signals analyzes lobbying vs public positions to surface corporate hypocrisy. Built with Airflow, dbt, Snowflake, and Claude API.

This repository is a collection of comprehensive and up-to-date datasets containing valuable information about job vacancies on LinkedIn. Explore trends in the job market, analyze the demand for specific skills, study career progression, and gain valuable insights to boost your career or make informed recruitment decisions.

A Data Intensive Computing (CSE587) project designed to analyze job market trends using LinkedIn data. The repository documents the setup of a Dockerized Big Data infrastructure, featuring a Hadoop cluster (NameNode, DataNode, ResourceManager) and Apache Spark for distributed processing, demonstrated via a PySpark WordCount example.

A data-driven tool designed to analyze job listings on LinkedIn, identifying the most sought-after skills across various roles and industries. By leveraging real-time data, it helps professionals understand current market demands, enabling them to tailor their skill development and stay competitive.