Medical Research with Python Tools

Medical Research with Python Tools

Harness the power of Python to accelerate your medical research journey.

What you’ll learn:

• Foundations of Python for healthcare research: Core Python, data types, and libraries like NumPy, Pandas, and Matplotlib.
• Medical data handling: Work with EHRs, DICOM imaging (pydicom), genomic data (Biopython), and clinical trial datasets.
• Data cleaning & standardization: Manage missing data, normalize units, and apply ICD/SNOMED CT codes.
• Statistical analysis: Perform hypothesis testing, ANOVA, chi-square tests, and survival analysis using SciPy, Statsmodels, and lifelines.
• Visualization & reporting: Build dashboards with Dash, visualize data with Seaborn and Plotly, and automate research reports.
• Machine learning in medicine: Predict disease progression with scikit-learn, analyze imaging with CNNs (TensorFlow, PyTorch), and use NLP for clinical text.
• Real-world applications: Drug discovery (RDKit), clinical trials, and IoT healthcare data.
• Ethics & future trends: Understand AI fairness, HIPAA compliance, and the future of personalized medicine.

Ideal for: Medical researchers, data scientists, and healthcare professionals looking to turn data into actionable insights.