Healthcare analytics starts with understanding where medical data originates, how it is captured, and the inherent challenges that arise throughout clinical workflows. This module will introduce you to the major healthcare data sources, such as electronic health records, claims data, imaging records, registries, and population datasets. Each of these data sources possess unique structures, levels of granularity, and analytical applications. You explore the ecosystem of healthcare information systems and how clinical, operational, and administrative data flow across hospitals, payers, and public health agencies. The module also highlights the common data quality problems in healthcare such as missingness, fragmentation, heterogeneity, and temporal inconsistencies. By the end of this module, you will be able to identify and differentiate healthcare data sources and understand the broader ecosystem within which medical data is generated and exchanged.

Interoperability is essential to producing clean, consistent, and analyzable healthcare datasets. This module introduces the major data standards that govern clinical documentation, diagnostics, billing, and cross-system communication. Learners explore coding systems such as ICD-10, SNOMED CT, CPT, and LOINC, along with interoperability frameworks such as HL7 v2, HL7 v3, and FHIR. The module explains why standardization is necessary, how vocabulary and messaging standards differ, and how interoperability issues can limit analytics. Through examples and hands-on labs, learners practice mapping clinical concepts into standardized formats and examine FHIR resource structures. By the end of this module, learners will understand how standards enable data integration, reduce ambiguity, and support scalable analytics across healthcare organizations.

Before any predictive modeling or analytics can occur, healthcare data must be cleaned, transformed, aligned, and validated. This module covers foundational preprocessing techniques with a focus on challenges unique to healthcare—missingness patterns, irregular time series, inconsistent measurements, and feature engineering from episodic records. Learners will work hands-on with real healthcare datasets to perform data cleaning tasks, create derived features, and assess temporal alignment for longitudinal records. The module also explores HIPAA-aligned de-identification and privacy-preserving preparation steps required before applying analytics. By the end of this module, learners will possess the practical skills necessary to convert raw clinical data into a structured, high-quality analytical dataset suitable for modeling tasks in later courses.

In this final module, you’ll consolidate your learning by completing a hands-on final project. In this project, you will apply the knowledge gained throughout the course to build a healthcare analytics dataset from raw, multi-source data. You will do this by cleaning, integrating, standardizing, and preparing healthcare data for downstream modeling and analysis. Working with a multi-source healthcare dataset, you will apply preprocessing techniques, assign coding standards, resolve data quality issues, and ensure HIPAA-aligned handling and de-identification. The module concludes with a course summary, a glossary of key terms, and a final exam designed to assess your conceptual understanding across all modules.