goated69/mauritius-it-jobs

Mauritius IT Jobs Dataset

This dataset contains IT job listings scraped from myjob.mu, the primary job board in Mauritius.

This dataset is part of the MyJobViz project, which provides visualization and analysis tools for the Mauritius IT job market.

Dataset Structure

Each job listing contains the following fields:

Field	Type	Description
job_title	string	Title of the job position
company	string	Name of the hiring company
date_posted	datetime	When the job was posted (Unix timestamp in ms)
closing_date	datetime	Application deadline (Unix timestamp in ms)
location	string	Job location within Mauritius
employment_type	string	Type of employment (e.g., Full-time, Trainee, Contract)
salary	string	Salary range or information
job_details	string	Full job description and requirements
url	string	Original job posting URL
timestamp	datetime	When the job was scraped (Unix timestamp in ms)

Example Entry

{
    "job_title": "it helpdesk support - raw it services ltd",
    "company": "TAYLOR SMITH GROUP",
    "date_posted": 1676332800000,
    "closing_date": 1678924800000,
    "location": "Port Louis",
    "employment_type": "Trainee",
    "salary": "10,000 - 20,000",
    "job_details": "Your role will consist of providing support to the technical team...",
    "url": "http://myjob.mu/Jobs/IT-Helpdesk-Support-Raw-IT-141921.aspx",
    "timestamp": 1676427651200
}

Files

• jobs.json: Complete job listings in JSON format
• jobs.csv: Complete job listings in CSV format (easier to preview)
• metadata.json: Backup metadata and statistics

Usage

Load with HuggingFace Datasets

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("goated69/mauritius-it-jobs")
print(dataset)

Load with Pandas

import pandas as pd

# From CSV
df = pd.read_csv("hf://datasets/goated69/mauritius-it-jobs/jobs.csv")

# From JSON
df = pd.read_json("hf://datasets/goated69/mauritius-it-jobs/jobs.json")

# Convert timestamps to datetime
df['date_posted'] = pd.to_datetime(df['date_posted'], unit='ms')
df['closing_date'] = pd.to_datetime(df['closing_date'], unit='ms')
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')

Potential Use Cases

• Job Market Analysis: Analyze trends in Mauritius IT job market
• Salary Research: Study compensation patterns for IT roles
• Skills Demand: Identify most in-demand technologies and skills
• Location Analysis: Understand geographic distribution of IT jobs
• Time Series Analysis: Track job posting trends over time
• NLP Tasks: Job description classification, entity extraction, skill identification
• Career Planning: Research companies, job titles, and requirements in Mauritius IT sector

Data Collection

Data is collected through automated web scraping of myjob.mu, focusing exclusively on IT-related job postings. The scraper runs periodically to capture new job listings.

Scraping Code: The code used to scrape and process this data is available at github.com/creme332/myjobviz

Limitations

• Only includes IT jobs (other sectors not included)
• Geographic scope limited to Mauritius
• Dependent on data availability from source website
• Some fields may contain inconsistent formatting (e.g., whitespace in location field)
• Salary information may be missing or in various formats

Statistics

The statistics.json file (if included) contains aggregated data on:

• Programming languages mentioned in job descriptions
• Technologies and frameworks (databases, cloud platforms, tools)
• Job title distributions
• Location distributions
• Salary ranges
• And more...

Citation

If you use this dataset in your research or analysis, please cite:

Mauritius IT Jobs Dataset, scraped from myjob.mu
Part of the MyJobViz project: https://github.com/creme332/myjobviz
Available at: https://huggingface.co/datasets/goated69/mauritius-it-jobs

License

This dataset is provided under CC-BY-4.0 license. The data is scraped from publicly available job listings.

Backup History

This dataset is automatically updated with regular backups to preserve historical job market data.

Last Updated: 2026-04-01_04-38-56