LinkedIn job postings analysis (over 15000 data) using Apache Spark, Plotly, and Streamlit. Mainly focused on open job count and monthly salary.

Linkedin data science job postings analysis using natural language processing techniques and prediction on candidate's education level

📊 Exploratory Data Analysis (EDA) of LinkedIn job postings in India. Features insights on in-demand roles, experience levels, and hiring sectors using Python, Pandas, and Seaborn.

Performed an exploratory data analysis on Linkedin Job postings

An exploratory analysis of the 2024 job market using job postings scraped from LinkedIn, with an emphasis on the Data Job market and the skills/tools currently in demand.

Repository presents a data analysis on LinkedIn job postings, from Kaggle. It includes preliminary analysis. Setting specific hypothesis. Deeper statistical analysis. Testing and evaluating hypothesis. Done with Python, within a Jupyter Notebook environment. It was done as exercise for Data Science Role. :)

India Job Market Analysis End-to-end data project analyzing LinkedIn job postings across India. Includes cleaned datasets, Python EDA, and a multi-page Power BI dashboard revealing insights on job roles, top skills, hiring trends, salaries, and experience levels. Clear structure with visuals and demo video.

Data Analysis on LinkedIN Jobs Postings

Exploratory Data Analysis on LinkedIn job postings using python

Comprises an analysis of data engineering job postings on LinkedIn.

“Exploratory Data Analysis of LinkedIn job postings to uncover insights on job demand, salaries, and hiring trends.

Analyzed 1,000+ U.S. Data Analyst job postings on LinkedIn using web scraping and data analysis.

Here i have performed exploratory data analysis(EDA) of job postings on social media platform linkedin.

SQL + Tableau analysis of 100,000 LinkedIn job postings focusing on data roles, skills demand, and hiring trends.

Analysis of job postings for Data Analyst, BI Analyst, Data Scientist, Data Engineer, and ML Engineer roles on Indeed, Glassdoor, and LinkedIn.

Exploratory Data Analysis on 122,130 LinkedIn job postings (2023-2024) using Python, Pandas and Seaborn. Uncovers trends in job roles, skills, salary and remote work.

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.

SQL Project & Data Visualization: LinkedIn Data" transforms over 10,000 job postings into a structured database, enabling analysis on job trends, skills, and salaries. It provides insights for job seekers and companies, focusing on benefits, remote work preferences, and industry demands.

This project focuses on performing Exploratory Data Analysis (EDA) on job postings extracted from LinkedIn between 2023 and 2024, aiming to uncover trends in hiring practices, in-demand roles, and skill requirements across industries.

Making plots to conduct an exploratory data analysis on 25,000 LinkedIn job postings from 2024 in order to select a top contender for the anchor client of a speculative Talent Intelligence startup.

It is a Python-based data analysis tool built on the LinkedIn Job Postings dataset (2023–24). Features 5 analysis phases covering EDA, skills demand, sector trends, salary insights, and an ML-powered salary predictor using Random Forest, Gradient Boosting, and Linear Regression models.

It is a Python-based data analysis tool built on the LinkedIn Job Postings dataset (2023–24). Features 5 analysis phases covering EDA, skills demand, sector trends, salary insights, and an ML-powered salary predictor using Random Forest, Gradient Boosting, and Linear Regression models.

A Python-based tool using requests and BeautifulSoup to scrape job postings from LinkedIn based on a specific title (e.g., "Data Analyst") and location. Extracts key details like company, experience level, and contract type, compiling them into a clean CSV file for market analysis.

End-to-End workforce Intelligence Project ~1.3M LinkedIn job postings from 2024. Implements a Bronze–Silver–Gold architecture in Databricks to extract confidence-aware role and skill demand signals. Focuses on skill penetration, persistence, role skill structure, geographic variation, and data coverage to support transparent labor market analysis.