Guided project - Analyze Employee Exit Surveys

Hello everyone,

Sharing my Guided Project: Cleaning and Analyzing Employee Exit Surveys.
I’d like the reviewers to please validate my solution to the questions mentioned in step 11, see link below.

https://app.dataquest.io/m/348/guided-project%3A-clean-and-analyze-employee-exit-surveys/11/next-steps
employee_exit_survey_updated.ipynb (542.3 KB)
Also any comments/feedback on the how I’ve communicated the results will be much appreciated.

Thanks,
Anjali

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

Hi Anjali, I just had a quick look at your project. Wow! I really liked seeing all the breakdowns between age, experience, and reasons for resignation visualized with the graphs. The breakdown by job type was interesting too. Really cool project.

1 Like

Many thanks @april.g. :slight_smile:

Do you think that I have communicated the analysis well to the reader? Looking forward to your valued inputs on how to improve.

My brain was foggy yesterday (no coffee!), but I had another look at it today. I think you did a good job communicating. I was able to follow along with what you were doing (you’re better at this than I am!). It would be nice if the pivot table (and subsequent graphs) were ordered by experience level (this Stack Overflow post might help with that). It’s not a deal-breaker, but it helps when reading the graphs.

The other thing that occurred to me when I had a closer look was the chart with the breakdown of the reasons for resigning.
image
I’m wondering if it would make sense to have the ones that are similar (that all say “dissatisfaction”, except perhaps the dissatisfication with the department) combined? I’m not sure what differences there is between them. If there is a distinction, it might be good to highlight what the differences are. I don’t know, just something I wondered about. :slight_smile:

1 Like

Hi @april.g,

Regarding this, I was a bit confused with Contributing Factors. Dissatisfaction and Contributing Factors. Job Dissatisfaction (both from the TAFE dataset) when I was playing around with the data.
In this step, we are considering the above two columns from TAFE and job_dissatisfaction column from DETE as factors for dissatisfaction while categorizing the employees.
I was not able to figure out what sort of dissatisfaction does Contributing Factors. Dissatisfaction in TAFE refer to.

Would still make sense to combine the job dissatisfaction factor from the different institutes to one column? I tried to combine both of the said columns into one and the results changed drastically.

I’m not sure, I’m not familiar enough with the data source. :thinking: The data was pretty messy to begin with so maybe it would be difficult to ascertain without more information from the surveys themselves. The lack of consistency between the two datasets makes it difficult. I don’t really have an answer.

@Rucha what do you think?

I gave it a shot anyway and combined the said columns. Attaching the updated project below. Please have a look.
Thanks. :slight_smile:
employee_exit_survey_updated.ipynb (542.3 KB)

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

I think it works and it’s less distracting seeing job dissatisfaction appear twice. Either way it shows that job dissatisfaction is a major contributor.
:+1:

:smiley: Updating the project in the main topic.