Computing jobs monitoring dashboard in Malaysia

This project proposed computing jobs monitoring dashboard in Malaysia and the dashboard will analyze and visualize the scraped data to help job seekers to better understand the current job market in the IT industry. The main motivation to propose this project is that there is vast amount of data ava...

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Main Author: Tan, Zhen Wei
Format: Final Year Project / Dissertation / Thesis
Published: 2022
Subjects:
Online Access:http://eprints.utar.edu.my/4700/1/fyp_CS_2022_TZW.pdf
http://eprints.utar.edu.my/4700/
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spelling my-utar-eprints.47002023-01-15T13:52:53Z Computing jobs monitoring dashboard in Malaysia Tan, Zhen Wei T Technology (General) This project proposed computing jobs monitoring dashboard in Malaysia and the dashboard will analyze and visualize the scraped data to help job seekers to better understand the current job market in the IT industry. The main motivation to propose this project is that there is vast amount of data available in online job recruitment platform but however, no tools or software are available to analyze that data into meaningful representation to job seekers. This project will focus on scraping data about computing jobs, this is because the IT industry changes and grows rapidly year by year, yet there is no data analysis and statistics about the related industry in Malaysia. Therefore, in this work, a computing jobs monitoring dashboard is proposed to solve the aforementioned issues. The proposed dashboard is able to automatically extract relevant data from online job recruitment platform such as JobStreet and Indeed, analyze the extracted data and visualize them in an interactive manner. The scraped data includes job title, company, location, salary, job requirements, qualifications, years of relevant job experience and application link. Apart from that, the Logistic Regression was used to classify the jobs into different computing jobs categories and a custom Named Entity Recognition (NER) model was built to extract the Information and Communication Technology (ICT) skills from each job requirements. The dashboard displays useful information for job seekers, including popular programming languages and skills, distribution of job opportunities, etc. The proposed dashboard is an interactive dashboard that provide users with several filtering options to view relevant data and information based on certain filtering criteria. In this work, Beautifulsoup has been used to program web scraping scripts and WayScript is used as the main development platform to automate the data scraping and storing them in Azure Blob Storage. In addition to that, the front end of this project is a highly interactive dashboard is developed using Plotly's Dash framework. 2022-09-09 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/4700/1/fyp_CS_2022_TZW.pdf Tan, Zhen Wei (2022) Computing jobs monitoring dashboard in Malaysia. Final Year Project, UTAR. http://eprints.utar.edu.my/4700/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic T Technology (General)
spellingShingle T Technology (General)
Tan, Zhen Wei
Computing jobs monitoring dashboard in Malaysia
description This project proposed computing jobs monitoring dashboard in Malaysia and the dashboard will analyze and visualize the scraped data to help job seekers to better understand the current job market in the IT industry. The main motivation to propose this project is that there is vast amount of data available in online job recruitment platform but however, no tools or software are available to analyze that data into meaningful representation to job seekers. This project will focus on scraping data about computing jobs, this is because the IT industry changes and grows rapidly year by year, yet there is no data analysis and statistics about the related industry in Malaysia. Therefore, in this work, a computing jobs monitoring dashboard is proposed to solve the aforementioned issues. The proposed dashboard is able to automatically extract relevant data from online job recruitment platform such as JobStreet and Indeed, analyze the extracted data and visualize them in an interactive manner. The scraped data includes job title, company, location, salary, job requirements, qualifications, years of relevant job experience and application link. Apart from that, the Logistic Regression was used to classify the jobs into different computing jobs categories and a custom Named Entity Recognition (NER) model was built to extract the Information and Communication Technology (ICT) skills from each job requirements. The dashboard displays useful information for job seekers, including popular programming languages and skills, distribution of job opportunities, etc. The proposed dashboard is an interactive dashboard that provide users with several filtering options to view relevant data and information based on certain filtering criteria. In this work, Beautifulsoup has been used to program web scraping scripts and WayScript is used as the main development platform to automate the data scraping and storing them in Azure Blob Storage. In addition to that, the front end of this project is a highly interactive dashboard is developed using Plotly's Dash framework.
format Final Year Project / Dissertation / Thesis
author Tan, Zhen Wei
author_facet Tan, Zhen Wei
author_sort Tan, Zhen Wei
title Computing jobs monitoring dashboard in Malaysia
title_short Computing jobs monitoring dashboard in Malaysia
title_full Computing jobs monitoring dashboard in Malaysia
title_fullStr Computing jobs monitoring dashboard in Malaysia
title_full_unstemmed Computing jobs monitoring dashboard in Malaysia
title_sort computing jobs monitoring dashboard in malaysia
publishDate 2022
url http://eprints.utar.edu.my/4700/1/fyp_CS_2022_TZW.pdf
http://eprints.utar.edu.my/4700/
_version_ 1755876969790046208
score 13.18916