In this project I focused on two things that I would love feedback on:
Clear visualizations that convey information and add to the analysis. The side-by-side bar graphs were really fun to make, but I also can’t quite put my finger on why they still seem to be missing something/the data story doesn’t “jump out”
Education level analysis. I found it really interesting that education-level seemed to impact who had seen star wars so I did some rough statistical significance testing by dusting off my stats book and re-learning Chi Squared, but I’m not sure if there is a better way to show this in a notebook/project setting (or if it is even necessary).
I’d also love any general feedback you have on the presentation of the project and the “believability” of my conclusions.
Hi @anna.strahl, I applaud the time and work you invested in the project, not just to show it off but also with the goal of making it a resource for future learners.
The following impressed me in your work
your introduction section, first, the image added helped tell more about your work and the text in the introduction is also very commendable as you were able to explain what you’re trying to achieve with your work.
Comments on each code were so nice, you took us through it step by step
The use of visualisations to communicate key insights throughout the analysis
Good use of functions for code reuse.
Application of the question-visualization-observation framework in practice.
The conclusion was presented well.
Things to consider
Try running all your codes again as this may enable you to know if there are errors anywhere
The doubleheader here isn’t really necessary, so why not make it Analysis for the average rating of each movie
You could consider importing all the necessary python libraries in cell one.
I discovered that some/most charts lacked axis titles and labels. These specifics, in my opinion, would aid readers in properly interpreting them.