Popular Data Science Questions or Tags?

It was great to explore Stack Exchange Data Science and to get familiar with what it has to offer. I’m looking forward to interacting with various Stack Exchange communities.
Any feedback on this project is most welcome.

Popular+Data+Science+Questions.ipynb (1.6 MB)

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


Hi Bruce,

Thanks for sharing another amazing project with the Community! :star2: You conducted a very thorough and profound data analysis, it’s clear that you really enjoyed working with this data! Awsome clean, and insightful visualizations, helpful additional images, cool cover picture. Good idea to use plot annotations and create a horizontal bar plot (instead of a vertical one) for the most popular tags. Interesting observations and great emphasizing throughout the project. Well done indeed!

Some suggestions:

  • It’s better to avoid too long (especially multi-line), too wordy, or obvious code comments (# perform all appropriate import ‘libraries’ to ensure**# executability of various commands., # read data file provided for analysis., etc.). Ideally, they should be as laconic as possible while still informative.
  • The code cells [10] and [12]: consider using separators for the output (adding empty lines or mini-subheadings).
  • The code cells [13] and [14]: here it’s probably better to create a function for the line plots, since the code is similar.
  • When you want to briefly explore a dataframe, consider using print(df.head()) instead of print(df), to avoid too much information.
  • It’s better to make the conclusion less wordy, to remove the pictures from there, as well as the definition of machine learning. It should summarize all the insights from the project without adding any new information.

Great job your project, as usual! Good luck with you future ones!

Thank you Elena for your improvement suggestions!
Much appreciated.

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