Clean and Analyze Employee Exit Surveys
- Guided Project
- 2.9 hours
- Intermediate
- Python
Practice cleaning and analyzing real-world employee exit survey data using pandas to uncover insights.
Overview
In this project, you'll step into the role of a data analyst for the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia. Your task is to analyze employee exit surveys from both institutes to answer questions about why employees resign.
You'll clean and combine the survey results from two different systems to create a single dataset ready for analysis. Using your data wrangling and exploratory analysis skills in Python and pandas, you'll uncover insights into the reasons for employee resignations and whether they vary by institute, employment period, or other factors. This real-world project will showcase your ability to apply data cleaning and analysis techniques to drive insights from messy HR data.
Objective: Clean and analyze employee exit surveys to gain insights into reasons for resignations across different institutes and career stages.
What You'll Learn
- ✓ Clean and analyze datasets
- ✓ Expand your portfolio with pandas
Before You Start
- ✓ Exploring and analyzing data using the pandas library
- ✓ Aggregating data using pandas groupby operations
- ✓ Combining datasets using pandas concat and merge functions
- ✓ Manipulating strings and handling missing data in pandas
Project Steps
11 steps
- 1 Introduction
- 2 Identify Missing Values and Drop Unnecessary Columns
- 3 Clean Column Names
- 4 Filter the Data
- 5 Verify the Data
- 6 Create a New Column
- 7 Identify Dissatisfied Employees
- 8 Combine the Data
- 9 Clean the Service Column
- 10 Perform Initial Analysis
- 11 Next Steps
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