First Guided Project: Visualizing Earnings Based On College Majors (v2)

I created the same topic earlier but accidentally linked to a wrong notebook. Thanks to Elena Kosourova for pointing this out to me. This is a repost of my first topic, but this time with the correct link!

Hello!

This is my first guided project, and it took me quite a while to finish it. English isn’t my native language and thus writing up the conclusions and answers to the questions was a little exhausting. Still, I’m not 100% happy, some things feel a little rusty, but hey, it is what it is :wink:

I am looking forward to seeing basically any kind of feedback. It wasn’t too much to code, so I feel quite confident about that, but I don’t have a lot experience in reading graphs and interpreting them. So any hint or help in that direction is very welcome.

You can find the project here: https://app.dataquest.io/m/146/guided-project%3A-visualizing-earnings-based-on-college-majors/6/next-steps

… and check out my take on it here:

https://nbviewer.jupyter.org/urls/community.dataquest.io/uploads/short-url/fjmKzMQ7vOaR8y61xDprkSoUA90.ipynb

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

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Hi @christoph.grabenstei

Congratulations for having completed your projects on Visualizing Earnings based on College Majors.

image christoph.grabenstei
I am looking forward to seeing basically any kind of feedback

Your project lacks a title, that is, what the project is all about. Introducing a title to the project makes the reader to have a rough idea of what the project is all about.

It would be better to include the aim of your projects ,what are these questions your are trying to answer…and by that, reaching onto a conclusion will be fairly easy.

Also do something on the history background of the dataset you are using, adding the links and so on. all this should be done in the introduction. This will guide you through.

image christoph.grabenstei
It wasn’t too much to code, so I feel quite confident about that, but I don’t have a lot experience in reading graphs and interpreting them.

I agree with you that reading and interpreting graphs require little bit of experience, but with consistency put in place all shall be well.

Otherwise everything to me looks good and please consider above as humble suggestions.

happy coding!

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Hi Christoph,

Perfect, I’m happy that I helped to resolve that issue! Your project looks nice, and by the way, your interpretations of the graphs also look good, coherent and exhaustive, so don’t be shy :blush:

I have some suggestions for your consideration:

  • About the necessity to add the project title, introduction and conclusion - totally agree with @brayanopiyo18.
  • A good practice is to re-run the project when it’s already completed, then all the code cells will be in order and start from 1.
  • Another good idea is to add comments to the code lines. Well, exactly in this project there is not much to comment, everything is quite clear from the code itself. But in general, keep in mind this suggestion for your future projects.
  • I would recommend you to divide long lines of code with a lot of arguments, like in the code cell [9] and all the subsequent scatter plots:
recent_grads.plot(x='Sample_size', y='Median', kind='scatter', title='Median vs. Sample_size', figsize=(10,10))

into several lines:

recent_grads.plot(
                  x='Sample_size', 
                  y='Median',                              
                  kind='scatter', 
                  title='Median vs. Sample_size', 
                  figsize=(10,10)
                  )

For each line - one argument. It will improve the code readability.

  • Be careful to check the already completed project for typos and correct them.
  • Now about visualizations. First, some general observations, related to all the graphs:
    • You should add titles and axis labels to each graph and make them readable enough (I mean increasing the font of titles and labels).
    • It’s better to despine the graphs and remove unnecessary ticks (you will learn these techniques in the mission Improving Plot Aesthetics, so afterwards you can return to this project and apply them).
  • Some comments on the particular groups of visualizations:
    • Scatter plots: it’s better to decrease figsize and change range on x and y axes, to avoid redundant white spaces on the plots.
    • Histograms: removing grid will improve readability.
    • Scatter matrix plots: it would be good to rotate x-axis values.
    • Bar plots: here I would recommend you to remove the legend, since it’s redundant in this case. Also, since we have a lot of bars here, you might want to consider using horizontal bar plots instead of vertical.
    • Grouped bar plot: here the legend is necessary, but you still can consider using grouped horizontal bar plot.

That’s all about my suggestions, hope they will be useful. Congratulations on finishing your first guided project, good job indeed! And good luck with your next projects!

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Thanks a lot for your reviews! I will keep those in mind when doing my next guided project!

And thank you for this nice and welcoming community. Really looking forward to learning and proceeding here!

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