MODULE 1

Introduction to Healthcare Analytics (15 Hours)

• Overview of healthcare analytics.
• Data types and sources in healthcare.
• Introduction to health informatics.
• Key challenges and opportunities in healthcare data analysis.

MODULE 2

Fundamentals of AI and Machine Learning (20 Hours)

• Basics of artificial intelligence.
• Introduction to machine learning and deep learning.
• Supervised vs. unsupervised learning in healthcare.
• Tools and technologies for AI development (Python, TensorFlow, etc.).

MODULE 3

Predictive Modeling in Healthcare (20 Hours)

• Understanding predictive analytics in healthcare.
• Techniques for disease outbreak prediction.
• Patient risk stratification models.
• Machine learning algorithms for healthcare predictions.

MODULE 4

Natural Language Processing for Clinical Documentation (15 Hours)

• Introduction to NLP and its applications in healthcare.
• Text mining and analysis of clinical notes.
• Sentiment analysis for patient feedback.
• Implementing NLP projects using Python and NLTK.

MODULE 5

Data Ethics and Privacy in Healthcare (10 Hours)

• Ethical considerations in AI applications.
• Data privacy laws and regulations (HIPAA, GDPR).
• Ensuring fairness and avoiding bias in AI models.
• Case studies on ethical dilemmas in healthcare analytics.

MODULE 6

Capstone Project on Real-World Healthcare Data Analysis (20 Hours)

• Identifying a real-world problem in healthcare analytics.
• Data collection, cleaning, and preparation.
• Applying AI and ML techniques to analyze the data.
• Project presentation and peer review.