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 j
In the following z-table, we are highlighting how to determine the area to the left of z = 2.34. Notice how we separated 2.34 into 2.3 and 0.04. Then, we look for 2.3 in the first column and 0.04 on the first row. The intersection of these two locations in the table will be the p-value to the left of z. This means that P(z < 2.34) = 0.99036. In the following z-table, we are highlighting how to determine the area to the left of z = -1.86. Notice how we separated -1.86 into -1.8 and 0.06. Then, we look for -1.8 in the first column and 0.06 on the first row. The intersection of these two locations in the table will be the p-value to the left of z. This means that P(z < -1.86) = 0.03144. You can find an unblurry version of the z-table at https://www.math.arizona.edu/~rsims/ma464/standardnormaltable.pdf.