Biodiversity in National Parks - Portfolio Project

Hello! Here is the github link to my project, where you can find my presentation as well.

At the beginning, I was a little bit lost because I didn’t find any pattern. But with time, the data and working with it became more and more intersting for me. I just don’t know whether my work is good enough, so I would be grateful for feedbacks. Thank you in adnvance! Veronka

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Hi Veronka. I just completed this same Portfolio Project, and I am looking how other people did it. I am leaving just a few tips which I think could improve your work in my humble opinion:

  • Even you are using a Jupyter Notebook to do the analysis and then a Powerpoint to show the results/conclusions, add some context at the beginning of the Jupyter Notebook. I think title and index are mandatory, so when someone starts reading it can trully understand what is the structure of the analysis. Also, a good practice is to write an introduction block to summarize the content of the analysis.

  • Your first plot is of observations.obs_num, but it is not clear what you are plotting. Add labels and title to the plot, so the reader can understand what he/she is looking at.

  • After reading more of your analysis I find a section named ‘Purposes of Analysis’. This should be more at the beginning of the notebook. First you explain what you plan to do, and then you develop it.

  • There is a little trap regarding common names in the species DataFrame. There are duplicated rows because of different common names, so drop_duplicates() doesn’t detect them at first. If you check, for example, the scientific name ‘myotis lucifugus’, you will see that each row is three times, only changing the variables at the common names column. That will provoke that when you sum the observations of this species, you will get it three times (which is unreal).

That being said, you did an overall great and complete work, congratulations!

I am going to share my Portfolio Project with you so you can check it as well if you want:

Best regards!