Fundamentals of Data Science in Healthcare

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

• Describe characteristics and challenges of major healthcare data sources
• Explain key healthcare data standards and interoperability importance
• Apply data preprocessing and quality assessment techniques
• Implement privacy-preserving steps aligned with HIPAA requirements

• Basic familiarity with healthcare data and terminology
• Willingness to practice through applied exercises