Healthcare generates enormous amounts of data every day — from electronic health records and lab systems to medical imaging, claims, and registries — but turning that data into trustworthy insights and clinical decisions requires a very specific skill set. Healthcare AI and Data Analysis takes you from the fundamentals of healthcare data all the way through to building machine learning models for real clinical problems, across four courses.
You'll learn how healthcare data is generated, structured, and standardized (including key coding systems like ICD-10, SNOMED CT, and interoperability frameworks like HL7 and FHIR); how to protect patient privacy under regulations like HIPAA and GDPR; how to clean, prepare, and analyze real healthcare datasets using tools like Excel and SQL; how AI is transforming diagnosis and clinical workflows, along with the ethical and regulatory considerations involved; and how to apply supervised learning, unsupervised learning, and neural networks to healthcare-specific problems like disease risk prediction and medical image analysis.
By the end of this Specialization, you'll be able to responsibly and effectively prepare healthcare data, apply machine learning techniques to clinical problems, and build predictive models that are clinically relevant, statistically sound, and ethically grounded.
Applied Learning Project
Put your skills into practice through hands-on labs and real-world healthcare datasets. In Fundamentals of Data Science in Healthcare, you'll clean, standardize, and integrate multi-source clinical data into an analysis-ready dataset. In Foundations of AI in Healthcare, you'll work through guided labs on diabetes classification and bias detection, culminating in a liver disease detection project.
As you progress, you'll use Excel and SQL to analyze real healthcare data, apply HIPAA and GDPR-aligned privacy practices, and build machine learning and neural network models — including disease risk predictors and medical imaging classifiers — to support real clinical decision-making.
