Overview

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.

Syllabus

Course 1: Fundamentals of Data Science in Healthcare
Course 2: Machine Learning for Healthcare Applications
Course 3: Advanced Healthcare Analytics

Courses

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8 hours 7 minutes
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Take your healthcare analytics and machine learning skills to the next level! Advanced Healthcare Analytics brings together neural networks, deep learning imaging models, and clinical natural language processing (NLP) to solve high-value problems in modern healthcare. You will explore architectures for clinical prediction, apply convolutional neural networks to medical imaging, and use domain-specific text models for clinical notes. The course also covers responsible AI for safe, ethical deployment, including chatbots and LLM-powered tools. Using datasets representative of electronic health records, radiology studies, and provider documentation, you will build practical skills through labs in imaging and NLP. In the final project, you will build and evaluate a binary disease prediction model using structured clinical data and compare Logistic Regression with a neural network to interpret performance on the same dataset. You will also learn model evaluation, workflow-integrated decision support, privacy, and safety.
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8 hours 15 minutes
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Build the data science foundation healthcare demands! Learn how to transform raw clinical data into reliable, analysis-ready datasets across real healthcare systems. This course equips you with the foundational data science skills needed to work effectively with real-world healthcare data. You will learn how healthcare data is generated, structured, standardized, and prepared for analytics across clinical, operational, and administrative settings. You’ll explore major healthcare data sources such as electronic health records, claims, labs, and registries. The course covers typical challenges such as missing data, inconsistent formats, fragmented systems, and complex timelines. It introduces essential healthcare standards, including ICD-10, SNOMED CT, HL7, and FHIR, and explains how interoperability enables reliable data integration and analysis. Through hands-on labs, you’ll clean raw clinical datasets, assess data quality, engineer analytical features, and apply HIPAA-aligned de-identification techniques. You’ll also work with multi-source healthcare data to prepare model-ready datasets suitable for downstream analytics and machine learning.
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7 hours 41 minutes
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Build the machine learning foundation for healthcare demands! Learn how to turn complex clinical data into models that drive decision support, early warning, diagnostic assistance, and personalized treatment insights. This course equips you with practical machine learning skills for real-world healthcare analytics. You will apply supervised, unsupervised, and temporal modeling techniques that match common healthcare data realities and clinical use cases. You’ll learn to frame clinical prediction problems, construct features from structured and time-based data, and develop classification and regression models for healthcare settings. You’ll also discover patient subgroups using clustering and dimensionality reduction and interpret patterns in patient populations. Across the course, you’ll focus on interpretability, robustness, and healthcare-appropriate evaluation metrics tied to clinical risk and patient safety. In hands-on labs, you’ll build a Readmission Risk Classifier, cluster patients for phenotype discovery, visualize populations with dimensionality reduction, engineer temporal features for an early warning model, and compare models using ROC, PR, calibration, and threshold-based utility analysis.