Clean And Analyze Employee Exit Surveys
In this guided project, you’ll work with exit surveys from employees of the Department of Education, Training, and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia.
In this project, you’ll play the role of a data analyst and pretend our stakeholders want to know the following:
- Are employees who only worked for the institutes for a short period of time resigning due to some kind of dissatisfaction? What about employees who have been there longer?
- Are younger employees resigning due to some kind of dissatisfaction? What about older employees?
They want us to combine the results for both surveys to answer these questions. However, although both used the same survey template, one of them customized some of the answers. In the guided steps, you’ll aim to do most of the data cleaning and get you started analyzing the first question.
Keep in mind that now you have more tools you can use to clean and transform data than you did at the beginning of this data cleaning and analysis course, including:
- Vectorized string methods to clean string columns
- The `apply()`, `map()`, and `applymap()` methods to transform data
- The `fillna()`, `dropna()`, and `drop()` methods to drop missing or unnecessary values
- The `melt()` function to reshape data
- The `concat()` and `merge()` functions to combine data
Working on guided projects gives you hands-on experience with real-world examples, which also means they’ll be more challenging than lessons.
As with all guided projects, we encourage you to experiment and extend your project, taking it in unique directions to make it a more compelling addition to your portfolio!
- Learn to clean and analyze datasets in a less guided way.
- Learn to apply new pandas methods you haven’t used before.
- Identify Missing Values and Drop Unnecessary Columns
- Clean Column Names
- Filter the Data
- Verify the Data
- Create a New Column
- Identify Dissatisfied Employees
- Combine the Data
- Clean the Service Column
- Perform Initial Analysis
- Next Steps