This is the second project that I have posted, and I would love to get any and all feedback from the Dataquest community! (especially critical - I’m trying to grow and improve.)
The URL of the last mission screen of the Guided Project:
Learn data science with Python and R projects
@adamson.tracy Congrats on completing the project. You have done a great job . There is an overall clean feel to the entire projects. The following are absolutely commendable:
- Being your second project, the way you have commented on your code especially the functions is excellent. I did that much later.
- You have a good intro with a link to the dataset and an explanation about your project, which is not usually seen for these projects. Again this is a commendable feature. Your conclusion was also informative.
- Your notes had links to external sources. This is something I learned at a later point. You are on a roll with for a second time author.
Keep these up
I’ve added a couple of pointers to help you improve your project. (Click on the bullet points to view the respective feedback.)
- Once you are done with a project, it is recommended to re-run the entire project so that the cells order correctly and you can ensure there are no errors introduced at some later stage which you may not have noticed. You can do the same using this
- You should consider rounding your outputs where they are extensively large after the decimal point . A simple
.round()function is useful for this. e.g. In cell , a small edit on the avg_num_comments function by doing the following
should return a nice and clean rounded value.
- I could not find any issues, but I haven’t gone too deep in to your code.
- Once you have a hold on visualization. I recommend that you re-do this project and add a couple of visualizations.
Keep working on the projects. You are doing well and are on a roll with the practices you have initiated. Keep those going. At this pace you could be a rising star pretty soon .
Thank so much for taking the time to review my work and provide valuable feedback and advice. I really appreciate your kind words and assistance. I’m very grateful for your help on my data journey.