It is finished! The second to the last vlog talked about the different visualizations and when they are appropriate to be applied while the last vlog was about the final project which utilized all the lessons learned from Vlog 1 up to now. And, here is my final output.
In the above dashboard, we can see some demographics as indicated by the 630 total survey participants with an average age of 29.87 years old. We can also see that data scientist has the highest average salary ($94k) followed by data engineer ($65k) then data architect ($64k) among data professionals. We can also see that the favorite programming language of data professionals is Python, followed by R and other programming languages and the distribution of data professionals in each of the given programming languages. For example, for those who selected Python as their favorite programming language, 255 of those were data analyst, 54 are either a student or still looking for a job or no job at all, while 54 have other job titles. We can also observe that a significant number of data professionals found it neither easy nor difficult to transition to becoming a data professional, followed by those who found it difficult and then followed by those who found it easy. Work-life balance is at 5.74 while salary satisfaction is at 4.27. This means that, on average, data professionals have above average work-life balance and a below average salary satisfaction.
What I found interesting in the final project is that whenever I click the country of origin, all the data pertaining to that country of origin will be displayed. It shows that the dashboard varies depending on your preferences. For example,
I am amazed by this platform and the possibilities it could make with data. Can't wait to apply this on a real-world data and work as a data professional. Maybe take this survey in the nearest future. Thank you Alex the Analyst.
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