Highlights
Here is advice from the lecturing team on exploring the dataset:
1. It is important to emphasize that there is not a correct answer to the assignment. There are lots of differentdimensions of the data to explore, and some aspects and dimensions of the data are likely to be more usefulthan others. Thus, it is important that prior to starting your assignment, that you systematically explore thedifferent variables in the dataset.
2. To help focus your analysis and insights, think of potential problems that could drive low moral across theworkforce. Asking questions, like are there differences across departments or job roles for key factors thatmay drive low job satisfaction, can help provide greater structure for the analysis.
3. Although you may create many graphs for your assessment (e.g., histograms to better understand the data),you only want to include figures that support your main findings. These graphs should summarize therelationships that you are reporting on or analyzing.
4. Also look for potential outliers in the dataset. What can we infer from these outliers? Do these outliersrepresent people who may at risk for leaving the organization? Should the outliers be included in the analysisof the data? Any decisions made about including or not including outliers should be justified in the report.
5. Remember that your conclusions should well supported by the undertaken data exploration and createdvisualisations. You should also outline any key assumptions in your data-driven conclusions andacknowledges limitations
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