Description of assessment task
In this team assessment, you will apply key concepts discussed in weeks 1-5 of the course to a real-world scenario. In this assessment, you will continue to explore data related to the individual assessment, using predictive analytics to derive actionable insights to assist with business decision-making. You must present your findings in the form of a written report.
As you know, a pharmaceutical company called Globex Pharma has contracted your consulting company to investigate their firm’s workforce. There has been some recent turnover at Globex, and the leadership team, through the grapevine, is aware of rumors of dissatisfied employees that may seek other job opportunities. Fortunately, the firm’s employees have recently undertaken a survey (within the last year) measuring various attributes of their jobs (e.g., salary, time since last promotion) and demographics (e.g., education, distance traveled to work). Globex wants you to use their data to identify factors contributing to employees being frustrated and displeased with their work.
Your consulting company initially assigned several analysts to conduct individual assessments of Globex data, resulting in several short reports containing exploratory data analysis on the negative sentiment of their employees. Since the submission of the report, Globex has provided your consulting company with additional data on employee attrition, i.e., which employees left the company over the last year. In addition, Globex has provided other pieces of information collected from their employees that may prove useful.
Globex Pharma wants your consulting company to design a method for anticipating employee attrition and to use the resulting insights to make recommendations for the firm to retain valuable employees. As business analytics is a team sport, your consulting company has grouped together the individuals undertaking the exploratory analysis to form a team to build the predictive model. Your consulting company has promised to deliver the following in a report by Friday 23 rd July:
1. A model to predict which employees will leave the organization.
2. Information explaining:
I. How the model was selected.
II. Factors that drive employee attrition.
III. An assessment of the model’s accuracy.
3. Present insightful recommendations and/or interventions well supported by analysis of the model that Globex can implement.
4. Globex has also included 30 people who they are unsure will leave the organization in the next few months and wants your team to assess using the model to assess which employees are likely and unlikely to leave the organization.
5. A high-level executive summary of the model, issues, and recommendations.
6. R Code for building the model. (This will be in the report’s appendix.)
Submission instructions
Have your team leader submit the written report with all relevant code in the appendix to the TurnItIn assessment submission link on Moodle. To determine the team leader, list all group members in alphabetical order. Whoever is listed first is the designated team leader. The submission link can be reached by logging into Moodle or through Ed. Your submission must be in a word doc (.docx). Include a cover page, with group member's names and the word count. The appendix must have all relevant R codes. The code should take the raw data file provided as the input and reproduce all analysis in the report (i.e., for this assignment, you cannot pre-manipulate data in excel prior to analysis in R). Please note that the code and references (done via Harvard Referencing) do not count towards the assessment’s word count. Assignments that are submitted late will be penalized at a rate of 10% per day.
New data
The dataset has more observations (approximately 1300 observations) than your individual dataset. (You can assume that your group members have put together their respective datasets). The new variables are as follows:
• Attrition
• Stockoptionlevel
• Yearsatcompany
• Yearsincurretrole
• Yearssincelastpromotion
• YearsWithCurrManager
• ST1 (which equals 1 if the data was previously in one of your team members' datasets).
In addition, you will notice that the last 30 observations in your dataset do not have a value for attrition. These are the data observation that you must predict for Globex.
Other Tips
Here is advice from the lecturing team on developing models and recommendations:
1. It is important to emphasize that there is not a correct answer to the assignment. There are many different models that can be put forward that can effectively address the organization's brief. Thus, it is important that you clearly identify the analysis methods and set out a systematic, comprehensive plan to resolve Globex’s problem.
2. To ensure the rigor of the model development and subsequent analysis, apply the frameworks discussed in class and the workshops. In this assignment, you will be primarily leveraging the materials from Weeks 4 and 5. We are not expecting the use of statistical/machine learning methods beyond the scope of this course.
3. Remember that your conclusions should be well be supported by the undertaken data exploration and created visualizations. You should also outline any key assumptions in your data-driven conclusions and acknowledges limitations.
4. If appropriate, connect findings or questions from your individual reports to your team report.
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