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Visual Analytics Project

PROPOSAL

 

POSTER

 

APPLICATION

 

RESEARCH PAPER


Problem and Motivation

Problem:
The Graduate Employment Survey (GES) is conducted on a yearly basis to provide insights about the job prospects about a particular course. The Ministry of Education conduct these surveys yearly for both University in Singapore. However, the results of these surveys are not interactive and does not provide any trends and insights with respect to the Labour Market demands.

Motivation:
As graduates to be who going to enter the job market soon, we felt that it would be interesting to look at the historical salary data to know the gauge of our expected salary, the demand in the job market and as well our position in term of starting salary among our fellow school peers. In addition, we also felt that it would be interesting to find out whether does the starting salary reflect the market demand. Furthermore, we hope that our visualizations would help students from Polytechnic and Junior Colleges decide their courses choices with regards to its career prospects as it would be interesting to find out what are the top paid salary programmes through the year, and the changes to the market demands.

Objectives

In this project, we are interested to create a visualisation application that helps users perform and answer the following:

1. Visualise Ranking of Highest Starting Salary throughout the years
2. To find out intake trends for tertiary courses in Singapore
3. To find out the trend of the Starting Salary and the employment rate over the years with regards to the courses
4. To find out the popularity of the courses by intake, and what new courses are been introduced or omitted over the years

Dataset Description

DataSet Description
Ministry of Education (MoE) Graduate Employment Survey - NTU, NUS, SIT, SMU & SUTD

Source: https://data.gov.sg/dataset/graduate-employment-survey-ntu-nus-sit-smu-sutd
  • Graduate Employment Survey of local universities in Singapore for the year 2013 - 2015
SMU Student Statistics

Source: https://www.smu.edu.sg/about/university-information/student-statistics/student-statistics
  • Provides intake data of programmes from Year 2007 - 2016
NUS Student & Graduate Statistics

Source: http://www.nus.edu.sg/registrar/statistics.html
  • Provides intake data of programmes from Year 1994 - 2016
NTU Student Statistics

Source: http://www.ntu.edu.sg/AboutNTU/CorporateInfo/FactsFigures/Pages/Undergraduate-Population-by-Gender-AY2016-17.aspx
  • Provides intake data of programmes from Year 2016
  • Need to manually extract data from website over the period from 2010 - 2016
NYP Student Statistics

Source: https://data.gov.sg/dataset/nanyang-polytechnic-full-time-student-enrolment-annual
  • Provides intake data of programmes from Year 2016
Temasek Poly Student Statistics

Source: https://data.gov.sg/dataset/temasek-polytechnic-full-time-enrolment-figures-breakdown-annual
  • Provides intake data of programmes from April 25, 2016 to April 23, 2017

Background Research and Survey of Related Work

Visualization Explanation
DUOteamV1.jpg

Source: http://ideasillustrated.com/blog/2011/11/28/earnings-and-unemployment-by-college-major/
  • A divergent bar chart provides useful insights with regards to a specific course and is useful in telling the distribution of salary. In addition, it offers a good comparison overview between majors.
DUOteamV2.jpg

Source: http://ideasillustrated.com/blog/2011/11/28/earnings-and-unemployment-by-college-major/
  • A scatter plot is extremely useful into displaying the employment rate and starting salary. It provides a good visualization to show the trends over the years and more information to be displayed with the size of the circle.
DUOteamV3.jpg

Source: http://pay.sgcharts.com/index.html
  • This time series bar chart is useful in showing the starting salary of various majors as well the a line graph as the employment rate by major. This graph essentially provides a good summary and comparison of the various degrees and majors

Key Technical Challenges

Key Technical Challenges Mitigation Plan
D3.js Programming & implementation
  • Attend Workshop in Week8
  • Self-learn through online platform for more in-depth content
Data Collections and Clean up
  • Decide as a team on what data to use and what to do with missing data(if any)
Visualization Tool Usage
  • Peer Learning
  • Self-learning through online platform


Tools & Libraries

  • Tableau
  • Microsoft Excel
  • Photoshop
  • D3.js Library
  • HTML, CSS & JavaScript
  • Github

Project Timeline

DUOteamTimeLine.jpg

References

Comments

Feel free to give us comments!