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AhmedMF
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data · Documented

Job Market Analysis

Scrape, clean, and report hiring demand: skills, seniority, and salary bands.

GitHub
Studio plate for the job-market analysis pack
Studio plate from the public dossier. Not a capture of a private interface.

Overview

An end-to-end analytics project on real job-market postings: what is being hired, which skills repeat, and how seniority and pay are distributed.

Problem

Job posts are text. Hiring questions are tables: skill mix, seniority, and salary bands.

Solution

Scrape, clean, and report. Python and Pandas for preparation, SQL where the grain needs it, Power BI and Excel for the reading layer, Jupyter for the trail.

Architecture

  • Collection from public job posts.
  • Cleaning and feature preparation in Python.
  • Reporting of skills, seniority, and salary bands.
  • Public GitHub archive and Demo 1 / Demo 10.

Technology stack

  • Python
  • Pandas
  • SQL
  • Power BI
  • Excel
  • Jupyter

Features

  • Hiring-demand view rather than a single-company HR report.
  • Notebook and dashboard kept as a pair.

Challenges

  • Salary fields are dirty. The honest chart is the one that shows the missingness.

Results

  • Source archive is public on GitHub.
  • Demo PDFs are in the library. No market-size claim is added on top.

Lessons learned

  • Publish the archive. A dashboard without the cleaning trail is a picture.

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