https://github.com/rohitblaze10/survey_monkey_analysis--using-ipython
This data analysis project focused on extracting insights from survey responses. It involves data cleaning, merging, and transformation using iPython (Pandas,OS) and SQL. The goal is to identify trends and patterns in survey data for better decision-making.
https://github.com/rohitblaze10/survey_monkey_analysis--using-ipython
data-analysis ipynb ipython-notebook
Last synced: 11 months ago
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This data analysis project focused on extracting insights from survey responses. It involves data cleaning, merging, and transformation using iPython (Pandas,OS) and SQL. The goal is to identify trends and patterns in survey data for better decision-making.
- Host: GitHub
- URL: https://github.com/rohitblaze10/survey_monkey_analysis--using-ipython
- Owner: rohitblaze10
- Created: 2025-02-10T15:20:22.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-24T12:51:28.000Z (over 1 year ago)
- Last Synced: 2025-04-05T19:36:25.040Z (about 1 year ago)
- Topics: data-analysis, ipynb, ipython-notebook
- Language: Jupyter Notebook
- Homepage:
- Size: 1.25 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# SurveyMonkey Data Analysis
## Overview
This project analyzes survey data collected via SurveyMonkey to extract key insights and trends. The dataset is processed using Python, and visualizations are created to present meaningful findings. The final results are compiled into a presentation.
## Project Structure
- **Data - Survey Monkey Output.xlsx**: Raw survey data exported from SurveyMonkey.
- **Script1-Data_manipulation.ipynb**: Jupyter notebook for data cleaning and analysis.
- **Final_Presentation.xlsx**: Summary of key insights and visualizations.
## Getting Started
### Prerequisites
Ensure you have the following installed:
- Python 3.x
- Jupyter Notebook
- Pandas
- Matplotlib / Seaborn (for visualization)
### Installation
1. Clone this repository:
```bash
git clone https://github.com/rohitblaze10/survey-analysis.git
cd survey-analysis
```
2. Install the required Python libraries:
```bash
pip install pandas matplotlib seaborn jupyter
```
## Usage
1. Open Jupyter Notebook:
```bash
jupyter notebook
```
2. Run `Script1-Data_manipulation.ipynb` to process and analyze the data.
3. View results in `Final_Presentation.xlsx`.
## Key Findings
- **Top 3 Divisions Represented:**
- Infrastructure (48 respondents)
- Finance (44 respondents)
- Information Technology (26 respondents)
- **Position Levels:**
- Staff (116 respondents)
- Managers (46 respondents)
- Department Leads (28 respondents)
- **Generational Breakdown:**
- Generation X (75 respondents, born 1965-1980)
- Millennials (66 respondents, born 1981-2000)
- Baby Boomers (39 respondents, born 1946-1964)
- **Gender Distribution:**
- Female (96 respondents)
- Male (86 respondents)
- Non-Binary (1 respondent)
- Prefer not to answer (13 respondents)
- **Employment Type:**
- Nearly all respondents (198) are full-time employees.
## Future Improvements
- Enhance data cleaning steps.
- Add more visualizations for better insights.
- Automate report generation.
## Contributing
Feel free to fork this repository and make improvements. Pull requests are welcome!
## License
[Specify a license, e.g., MIT, Apache 2.0] .