Data Science for Healthcare is an intermediate-level specialization focused on healthcare data science, machine learning, clinical analytics, and AI. Designed for learners with a basic knowledge of Python, statistics, healthcare terminology, and machine learning, this three-course program builds skills in preparing clinical data, developing predictive and machine learning models, and applying advanced techniques such as medical imaging and clinical natural language processing, with a strong emphasis on interpretability, privacy, and responsible AI. Through hands-on labs and projects grounded in real healthcare use cases, learners develop the ability to design and evaluate data-driven solutions for modern healthcare analytics.
Applied Learning Project
Each course in this specialization concludes with a hands-on, portfolio-ready project that reflects how data science is applied in real healthcare settings.
You will begin by building a model-ready clinical dataset from raw, multi-source healthcare data, addressing data quality issues, standardizing identifiers and codes, and applying HIPAA-aligned de-identification to prepare the data for modeling. You will then complete an end-to-end early warning system project, where you translate longitudinal clinical data into actionable predictions, engineer temporal features, train and evaluate predictive models using appropriate metrics, and interpret results in a healthcare decision-making context. In the final course, you will build and evaluate binary disease prediction models using structured clinical data, applying both logistic regression and neural networks to assess model performance and interpret results in a realistic clinical analytics workflow.
