NC State University Libraries Python Workshops

Emergency Room Wait Time Data Analysis

📓 Notebooks Available

🎓 Educational Template (Recommended for Learning)

er_wait_time_data.ipynb - Interactive learning template with:

• Step-by-step instructions
• Educational descriptions
• Accessibility features
• Placeholder cells for student work

📊 Completed Analysis (Reference)

er_wait_time_data_filled.ipynb - Complete analysis with:

• All code executed with results
• Full visualizations and outputs
• Statistical test results
• Comprehensive analysis conclusions

📊 Overview

This repository contains a comprehensive analysis of Emergency Room (ER) wait time data, designed for educational purposes. The notebook demonstrates data science techniques including exploratory data analysis, statistical testing, and data visualization using Python.

🎯 Learning Objectives

By completing this analysis, you will learn to:

• Load and inspect healthcare datasets
• Perform exploratory data analysis (EDA)
• Apply statistical tests to healthcare data
• Create meaningful visualizations
• Draw actionable conclusions from data

📋 Prerequisites

• Basic Python knowledge
• Familiarity with pandas, matplotlib, and seaborn
• Google Colab account (recommended) or Jupyter environment

🚀 Quick Start

Option 1: Open in Google Colab (Recommended)

Click the "Open In Colab" badge above to run the notebook directly in your browser.

Option 2: Local Setup

1. Clone this repository:

   git clone https://github.com/NCSU-Libraries/ai_in_data_science.git
   cd ai_in_data_science

2. Install required packages:

   pip install -r requirements.txt

3. Launch Jupyter:

   jupyter notebook er_wait_time_data.ipynb

📚 Dataset Information

• Source: NCSU Libraries AI in Data Science Repository
• Dataset: ER Wait Time Dataset
• Format: CSV, Excel
• Features: Patient demographics, wait times, satisfaction ratings, temporal factors

🔧 Required Packages

pandas >= 2.0.0
numpy >= 1.24.0
matplotlib >= 3.7.0
seaborn >= 0.12.0
scipy >= 1.10.0
statsmodels >= 0.14.0
jupyter >= 1.0.0
openpyxl >= 3.1.0

📖 Notebook Contents

1. Data Acquisition & Loading

• Downloading data from GitHub repository
• Loading CSV data into pandas DataFrame
• Initial data inspection and quality assessment

2. Exploratory Data Analysis

• Patient satisfaction analysis
• Temporal patterns (time of day, season)
• Wait time consistency analysis
• Statistical relationships

3. Statistical Analysis

• Mann-Whitney U tests
• ANOVA testing
• Correlation analysis
• Effect size calculations

4. Data Visualization

• Bar charts and pie charts
• Heatmaps and box plots
• Interactive visualizations
• Statistical plots

📚 How to Use These Notebooks

For Students (Learning)

1. Start with the template: Use er_wait_time_data.ipynb
2. Follow the instructions: Complete each cell step by step
3. Reference the filled version: Check er_wait_time_data_filled.ipynb if you get stuck
4. Focus on learning: The template is designed for educational purposes

For Instructors (Teaching)

1. Use the template: er_wait_time_data.ipynb for classroom instruction
2. Reference the filled version: er_wait_time_data_filled.ipynb for answer key
3. Customize as needed: Modify the template for your specific course requirements
4. Accessibility features: Built-in support for diverse learning needs

For Researchers (Reference)

1. Use the filled version: er_wait_time_data_filled.ipynb for complete analysis
2. Review methodology: See the statistical approaches used
3. Adapt the code: Modify for your own datasets
4. Cite appropriately: Follow the citation guidelines in the notebook

🎯 Notebook Comparison

Feature	Template (er_wait_time_data.ipynb)	Filled (er_wait_time_data_filled.ipynb)
Purpose	Learning and teaching	Reference and research
Code Execution	Students run cells	Pre-executed with results
Outputs	Students generate	Complete outputs included
Accessibility	Full accessibility guide	Accessibility guide included
Educational Content	Step-by-step instructions	Complete analysis
Best For	Classroom instruction	Research reference

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.