Fundamentals of Data Science in Healthcare
About this course
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.
75/100
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What you'll learn
- understanding how healthcare data is generated and structured
- cleaning and preparing datasets for analysis
- engineering features for analytics
- applying data quality assessments
- mastering healthcare standards like ICD-10 and HL7
Course objectives
- to equip students with foundational data science skills specifically for healthcare data
- to enable effective analysis across clinical and operational settings
- to prepare students for real-world data challenges in healthcare
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