Course Overview

Technical Skills

• Program in Python for health data analysis
• Implement machine learning algorithms
• Work with healthcare databases and APIs
• Create data visualizations and dashboards

Analytical Skills

• Analyze complex health datasets
• Identify patterns in epidemiological data
• Evaluate model performance and validity
• Interpret AI-generated insights

Module 1: Foundations of AI in Public Health

Introduction to artificial intelligence, machine learning basics, and their applications in public health. Overview of current challenges and opportunities.

Module 2: Fundamentals of Python

Build a strong foundation in Python programming, covering variables, data types, control flow, functions, and basic data structures

Module 3: Scientific Computinig with Numpy

Master efficient numerical computing using Python’s NumPy library for array operations, linear algebra, and scientific data manipulation

Module 4: Data Wrangling with Pandas

Learn to clean, transform, and manipulate structured data using Python’s powerful pandas library for health data analysis

Module 5: Data Visualization with Matplotlib and Seaborn

Create clear, informative, and publication-ready visualizations using Python libraries matplotlib and seaborn for health and biomedical data.

Module 6: Medical Image Processing with Python

Learn to process and analyze medical images using Python libraries like OpenCV, scikit-image, PIL, and NumPy for deep learning and diagnostic applications

Module 7: Applied Machine Learning in Public Health

Apply machine learning methods using Python libraries like scikit-learn and pycaret to solve real-world public health problems through data-driven insights

Module 8: Applied Deep Learning in Public Health

Learn to develop and apply deep learning models using Python libraries like TensorFlow, Keras, and PyTorch to address complex public health challenges and medical data analysis

Module 9: AI Applications in Disease Surveillance

Explore how AI techniques and tools such as machine learning, deep learning, and NLP are used to detect, monitor, and predict disease outbreaks using diverse health data sources

Module 10: AI Applications in Medical Imaging

Learn to develop and apply AI models using tools like CNNs, TensorFlow, Keras, and PyTorch for image classification, segmentation, and diagnosis in medical imaging

Module 11: AI Applications in Multi-omics and Personalized Medicine

Image analysis techniques for medical imaging, satellite imagery for environmental health, and visual data interpretation.

Module 12: AI Applications in Drug Discovery and Development

Explore how AI techniques are used to integrate and analyze multi-omics data (genomics, transcriptomics, proteomics) for insights into disease mechanisms and personalized treatment strategies

Module 13: Large Language Models and Generative AI for Health

Learn how large language models (LLMs) and generative AI tools are transforming healthcare through applications in medical documentation, decision support, research synthesis, and patient engagement

Module 14: Ethics and Governance of Artificial Intelligence for Health

Understand the ethical, legal, and regulatory frameworks guiding the responsible development and deployment of AI technologies in healthcare and public health.

Programming Languages

ML Libraries

Visualization

Medical Image Analysis

Platforms

Public Health Professionals

Epidemiologists, health policy makers, and public health practitioners

Researchers

Academic researchers and data scientists in health-related fields

Students

Graduate students in public health, computer science, or related disciplines

Required

• Basic understanding of public health concepts
• Fundamental statistics knowledge
• Basic computer literacy
• Willingness to learn programming

Recommended

• Previous programming experience (any language)
• Experience with data analysis
• Familiarity with research methods
• Background in epidemiology or biostatistics