What affects a Professor’s rating on RMP?


Please note that the scenario for this assignment is fictional.

The National Tertiary Education Union (NTEU) has hired your data science team to conduct an evaluation of the “Rate My Professors” (RMP) website. The NTEU team suspects that the RMP website is used in the hiring and promotion process of staff at universities in Australia. They believe that student evaluations are not a reliable measure of staff performance and this information should not be used. The NTEU believes that the universities have collected the RMP profiles in order to build a model to predict student ratings for professors without profiles.

The NTEU suggests that the perceived difficulty of the class, demographics of the professor, discipline of study are bigger influences on a Professor’s rating than the quality of education they deliver. The NTEU further claim that students use the website to choose professors or classes that are perceived as being easier, which undermines the academic process.

The NTEU has asked you, as an independent third party to address three key questions:

  1. What affects a Professor’s rating on RMP?
  2. Can a model be built to predict a Professor’s rating, and could this model be used to predict a rating if the Professor does not have a profile on RMP?
  3. What, in your expert opinion, are the social and ethical issues involved?

individual task( this is the part you need to do )

In this task you are required to produce a short EDA vignette that explores how one of the following is related to Professor ratings

  1. Reported difficulty
  2. Demographics of the Professor
  3. Discipline of study and university(I Seleted this one)

Your vignette will consist of a Jupyter Notebook, in which you will use Markdown cells to provide commentary on your EDA process.

As a team you will need to assign each team member to one of the three attributes. Each team member is expected to work independently as you will be marked separately for this component of the assignment. However, you are allowed to discuss your hypothesis, approaches and seek suggestions or feedback from your team members.

Suggested Structure

  1. Heading – include which attribute you are exploring
  2. Main body (approx. 400 words, only markdown cells included in word count) – this should contain the plots, tables, corresponding commentary and your Python code that make up your exploratory analysis
  3. Further work (approx. 100 words) – this is a small note to your teammates about how your analysis could be further refined and used in your final report
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