I am pleased and proud to share my guided project exploring population data in the CIA Factbook.
In the past couple of guided projects I have been working to improve the structure and flow of my notebooks and I believe this project shows a great improvement.
Working in SQL has been surprisingly fun and intuitive so I felt really comfortable playing around and refining the queries while also building complexity.
For the unguided portion I created population and area groups (P/A) and explored statistics within some of these groups.
’population group’ cases
P1: Below 1M
P4: Above 100M
’area group’ cases
A1: Below 10k sq km
A2: 10k-1M sq km
A3: Above 1M sq km
I only which I could have figured out how to GROUP BY on a column derived by using CASE. Research indicated I needed to LOAD a table generated from SELECT using CASE. I’m assuming that will be learned later on, so instead I just played with using CASE inside of ORDER BY.
It would be interesting to continue with this project by using data to derive the population and area group breaks, generating statistics within and across the unique P/A groups, as well as looking more into the area_water/area_land data.
Many thanks in advance for your feedback and for the feedback I’ve received on other projects that contributed to the quality of this notebook.
Details on the Mission
Guided Project - CIA Factbook - kwu.ipynb (127.3 KB)
Click here to view the jupyter notebook file in a new tab
Congratulations on completing another nice project and on the overall fast pace of learning! Good project structure, all the links provided including side links, and great job digging deeper into additional information about countries!
Some suggestions from my side:
- Please add a conclusion section to your project and reduce a little bit the Summary of Findings section. Ideally, it should be a short extract from the conclusion at the end.
- I would exclude Antarctica from the analysis and instead investigate the next country/countries after it. Also, it’s better not to mention it in the conclusion or summary of findings.
- It’s better to add some markdown explanation after every obtained table. What all those TOP10, or TOP 5, or TOP 20 mean? It would be good to find general trends in them, something what unites all those countries.
- It’s important to re-run your project when already completed, also in case of SQL projects. It can happen that you created a view in an earlier code cell and are going to use it further, and not earlier.
- You may find useful this guide, especially a section on white spaces (“rivers”).
- Please consider limitting the output of the code cell .
Hope my suggestions were of use. Good luck with your future projects!
Thanks very much for taking the time to review my project and provide me with your constructive feedback!
It seems like I still haven’t quite figured out how to tell a story with my output and observations.
For the Intro - Body - Conclusion, I believe this structure applies:
- summarize what I’m about to explore, my approach and most important findings
- summarize what I found in my exploration and make a conclusion.
But for the individual ‘explorations’ should I also be contextualizing what my code is about to do and then again summarizing what it did? The top x lists were meant to supplement or probe deeper into certain findings. I admit I tend to just generate some observations I believe are related without explaining their relations until all the pieces are done. When I try to lead the user through each step of my thought process I get a bit overwhelmed and stuck on the level of details I need to convey. So I admit to ‘spewing’ out a bunch of information and then making sense of it as a way to get around the writer’s block.
Could you please re-link the guide you wanted to share with me?
I would really like to try to apply your recommendations on the guided project for SQL Intermediate.
With gratitude for your support!
When I try to lead the user through each step of my thought process I get a bit overwhelmed and stuck on the level of details I need to convey.
Don’t worry, it’s a normal feeling for everybody when it comes to tell a story through the data and especially to be concise and at the same time informative. I would say that storytelling is a real art by itself, apart from knowing how to code or visualize the results. So you can find these materials useful:
Guide on how to write DS projects. It’s a great resource that I myself use a lot for my projects. Don’t be scared with its length Just take what you need from there at each step.
- An example project. I took this link from the guide above. You can find plenty of other good projects to use as an example here in the Community in the champion’s section and not only.
This course from @nityesh on how to write efficiently is really cool. I’m currently doing it and find it super-helpful.
- Finally, once again the SQL guide, I don’t know what’s happened with the link in my previous post.
Happy coding and storytelling!
Aww thanks so much for the shoutout, @Elena_Kosourova !
@Elena_Kosourova, thank you for pointing me to these excellent resources!
@nityesh, you’re writing course sounds great, I’ll definitely sign up soon!