This project consists of simple analysis of how employment rates and average salaries have changed in the past 5 years for students graduating from Accountancy and Business courses. However, the analysis are limited to NTU, NUS, and SMU. All of the analysis has been done using Jupyter notebook. Hence, you will only be able to see the analysis if you run it using Jupyter notebook.
Within the dataset, you will find many other degrees on top of Accountancy and Business. For this project, I have chose to focus on only the aforementioned degrees.
There are two reasons for this:
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Majority of the work for this project revolved around manipulating strings. This is because there are cases where a degree in 2013 was named differently than that of 2017. As such, in order for the trend to make sense, we must ensure they are named the same. Having to do it for all the degrees might not be a good use of time.
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As a graduating business student from NTU, I have a vested interest in knowing how the prospects have changed in terms of employment and average salary. Additionally, I am also interested to know where NTU stand as compared to its counterpart.
Since this project is largely about data preprocessing and data visualisation, there are certainly better tools such as Tableau or Power BI than using matplotlib to visualise the data. Using either of the two software would be much more efficent since they are largely drag and drop. My main reason for using Python to visualise data is more for my own practice, not to mention that there are also benefits such as eliminating the need to export and import files.
For users that are interested in the employment and salary trends of other degrees such as Engineering or Arts, the code is largely reproducible. As I have only ensure the same naming convention for Accoutancy and Business degrees, additional work needs to be done to ensure the naming of the degree of your interest is consistent as well. Once that is done, the function needs to be edited to suit your specific needs.
As you can see from my work, I am certainly still a beginner. While this project is fairly simple, it serves as a good opportunity to refine my Python skills and learning git. I hope to gradually improve my skills as time goes on and as much as possible, I will be showing my progress here on Github.