Subject Code : BEM2031
Assignment Task:

Context:

Consider you lead an analytics team at an international corporation. There have been a number of notable departures of your top talent recently - some of the best contributors have quit their jobs and left for new companies. The top management is concerned that something is systematically wrong with their retention policies. You have been collecting data on job satisfaction, performance, and other metrics for years and merged this information with other data on employee work loads and other information in order to determine what is leading to employees to leave the company. 

The report found in don-t-know-why-employees-leave-read-this.html is the report that was delivered to you by a member of your data science team. It is heavy on analytics and visualization, but very light on interpretation and explanation since it was intended to be a discussion piece for a series of meetings that will happen soon. You are to read the report and come to the meeting prepared to ask a number of questions about the analysis, ask for changes, and show some of your own results for comparison.

The data science team was able to produce a dataset, kaggle_hr_analytics.csv, consisting of the following features from 15,000 employees. 

  • Employee satisfaction level, based on survey data (satisfaction_level)

  • Last evaluation, supervisor rated performance evaluation (last_evaluation)

  • Number of projects employee worked on (number_project)

  • Average monthly hours (average_montly_hours)

  • Time spent at the company in a number of years (time_spend_company)

  • Whether they have had a work accident (1 = yes, 0 = no)

  • Whether they have had a promotion in the last 5 years (1 = yes, 0 = no)

  • Department, text data based on the different departments

  • Salary, are they highly paid, medium paid, or low paid. 

  • Whether the employee has left (1 = yes, 0 = no)

Additional Details

This data was part of a Kaggle competition to predict who would leave and who would stay based on the data. The original Kaggle report by Yassine Ghouzam can be found here. You are encouraged to explore the comments on Kaggle or other reports and analytics that have used this dataset, although the original dataset has been removed from Kaggle. 

Refer to the Proposal Review Guide in Appendix A in your Data Science for Business book for a good outline of what to look for and what to critique in the report. There is another sample report and critique in Appendix B as well.

Points of Critique

Your goal is to be critical of the report that was given to you by another team member. You need to consider ways in which this analysis could be improved and provide your own interpretation of the situation before this report is shared with company leadership.

  • Business Understanding

    • What is your understanding of the goals of this project? Is the data analysis suitable and the data used going to be to be to help guide decision making? What are the costs and benefits of this analysis? Who do you think will be harmed or benefited by this analysis? 

  • Data Understanding.

    • Is the data appropriate? What don’t we know from the data that would be helpful when understanding the results? What data should they have included that was missing?

    • How effective are the visualizations at building the narrative of the report? How could they be improved? What visualizations are missing that could help?

  • Data Preparation

    • Did the report appropriately explore all the different ways in which the data may be corrupted? What were the additional cleaning steps they could have considered? Should they have reshaped the data in any way? Do you trust the data? What would make you trust or distrust the data?

  • Modelling

    • Were the analytics choices here appropriate? Did they apply them correctly? You don’t need to know the specifics of the code, but more about the general approach (e.g. was a decision tree a good choice, or is there another analysis that would have been better used?)

    • How were the models evaluated? How do you know that they fit the data appropriately? What approaches did they use to avoid overfitting? Do we know if these models will work on unknown data in the future? What metrics could they have used to assess the quality of the model?

    • What were the important variables in the models and how do you know they are important? Do we know how these variable impact the outcome? How could they have measured that impact?

 

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  • Uploaded By : Alex Cerry
  • Posted on : March 25th, 2019

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