Python-Data-Analysis-for-Healthcare-A-Learning-Journey

Overview

Hello, and thank you for visiting my project on Python Data Analysis for Healthcare. As I learn to utilize Python programming and data analysis approaches to tackle real-world healthcare challenges, this repository tracks my experience as I become more proficient in these areas. My primary objectives are establishing a solid data analysis foundation and gaining practical experience working with healthcare datasets.

Objectives

• Learn Python Programming: Master the basics of Python, including data types, control structures, functions, and libraries.
• Explore Healthcare Data: Understand how to work with healthcare datasets, including data cleaning, preprocessing, and visualization.
• Apply Data Analysis Techniques: Use statistical methods, exploratory data analysis (EDA), and machine learning to extract insights from healthcare data.
• Document the Learning Process: Keep detailed notes and explanations of the concepts and techniques I learn along the way.

Repository Structure

• /notebooks/: Jupyter notebooks where I practice and document my Python data analysis learning process.
• /data/: Healthcare datasets used in the analysis (note: ensure any sensitive data is handled according to privacy guidelines).
• /scripts/: Python scripts that demonstrate key concepts and techniques in data analysis.
• /resources/: Useful articles, tutorials, and references that have helped me in my learning journey.
• /projects/: Mini-projects applying what I’ve learned to specific healthcare-related problems.

Getting Started

1. Clone the Repository:

   git clone https://github.com/osesa1/python-healthcare-data-analysis.git

2. Set Up the Environment: Make sure Python is installed on your system, then install the required libraries:

   pip install -r requirements.txt

3. Explore the Notebooks: Start with the notebooks in the /notebooks/ directory. These contain step-by-step explanations of Python basics, data analysis techniques, and applications to healthcare data.

Key Learning Topics

• Python Basics: Variables, data types, loops, conditionals, and functions.
• Data Manipulation: Using libraries like Pandas and NumPy to manipulate and analyze data.
• Data Visualization: Creating meaningful visualizations with Matplotlib and Seaborn.
• Statistical Analysis: Understanding descriptive statistics, hypothesis testing, and probability distributions.
• Machine Learning Basics: Introduction to supervised and unsupervised learning using Scikit-learn.
• Healthcare Applications: Applying data analysis techniques to real-world healthcare datasets, such as predicting disease outcomes, analyzing patient data, and more.

Healthcare Datasets Used

• Diabetes Dataset: Analyzing factors that contribute to diabetes.
• Breast Cancer Dataset: Classifying breast cancer as malignant or benign.
• Heart Disease Dataset: Exploring risk factors for heart disease.
• Stroke Prediction Dataset: Identifying key predictors of stroke.

Learning Progress

• In Progress: Python basics, data manipulation, basic data visualization.
• Next Steps: Applying machine learning models to healthcare datasets and building a final project.

Acknowledgments

I want to thank my instructor, supervisor, and the online community for their support and guidance throughout this learning journey.

Contact

For any questions, suggestions, or collaboration opportunities, feel free to reach out to me at oses.aigbokhan@gmail.com.

---