GP4: Visualizing Earnings Based On College Majors

Hello Everyone, It’s indeed a fantastic experience when working with plotting tools built into pandas to explore data on job outcomes. I really enjoyed it. Since this is a journey, I’ll appreciate all your suggestions and the likes as well. So kindly guys, have a look at my work.

screen link here

here is the notebook Guided Project_ Visualizing Earnings Based On College Majors.tar (700 KB)

2 Likes

Hi @brayanopiyo18,

Congratulation on finishing this project. I have also finished this very recently. This is a challenging one. Isn’t it?

First of all it would be great if you can upload the notebook in ipynb format.
.tar makes you want to download it, extract it and read it on jupyter while if it is ipynb, DQ platform can straight away show it to anyone on the browser itself.
Anyway here is your notebook in ipynb format for anyone who wants to have a look at your project.

guided project 4.ipynb (335.0 KB)

So I have gone through your project and it looks good. But there are so many more things you could have added in the project, that is what I felt.

I can see that you have followed all the major instructions diligently which is great. But it would have been great if you could add your observation from each of the plots you have created.

For example, in this you could have said Astronomy and astrophysics has more women percentage among the 10 highest paying majors.

In the same example, you have used

recent_grads[:10].plot.bar(x = "Major", y ="ShareWomen")
recent_grads[163:].plot.bar(x = "Major", y ="ShareWomen")

You can also use head(10) and tail(10) get the first and last 10 rows.

recent_grads.tail(10).plot.bar(x = "Major", y ="ShareWomen")

So my main suggestion would be to add some of your observations from the plots you have created. I hope this helps.

Click here to view the jupyter notebook file in a new tab

1 Like

@jithins123 thanks a lot for the concern. I thought by visualizing , every output is self explanatory and that’s why I ended up with less explanation and observation. But with your suggestion,giving a little bit of observation in every output, will indeed make my project easy to follow and understandable as well , and by that I will have to get back to my project to do so.

Talking about format, indeed I was so perplexed but thanks to you, moving forward, I am therefore well concerned with the format to use while uploading the notebook i.e ipynb format and I appreciate all the adjustment you have made to this project. Thanks once more for the concern.

Happy learning!

1 Like

No worries @brayanopiyo18
By the way, i forgot to mention about conclusions from your observations. As a reader, I might just want to know the conclusions you have arrived from your visualisation. Some users are not analytical and they just want to read about your wonderful conclusions that you have arrived from these datasets.

So please add a conclusion as well at the end. Glad to be of help.

1 Like

I am doing that right away.

1 Like