Healthcare Data Analytics
Turn healthcare data into insights that improve outcomes, quality, and cost. This course teaches you to work with claims, electronic health record (EHR), and registry data using SQL and Python, and to build the descriptive, population-health, and predictive analyses that clinical and operational teams rely on.
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This course includes:
- 38 hours on-demand video
- 60 articles and resources
- 40 downloadable datasets and notebooks
- 30 SQL and Python exercises
- Certificate of completion
- Lifetime access
- Access on mobile and TV
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About This Course
Healthcare Data Analytics is a practical, intermediate course for people who want to analyze real healthcare data and produce results that clinical, quality, and operational teams can act on. You will learn how the main healthcare data sources fit together — administrative claims, EHR clinical data, and disease and quality registries — and how their structure and coding systems shape what you can measure.
Working in SQL and Python throughout, you will clean and validate messy real-world data, build dashboards for clinical and operational metrics, run population health and risk-stratification analyses, measure quality and outcomes using recognized methods, and build an introductory predictive model. The course closes with a capstone in which you take a healthcare dataset from raw extract to a documented analysis and a clear set of recommendations, while applying HIPAA and responsible-analytics practices at every step.
What You'll Learn
- Identify the strengths and limitations of claims, EHR, and registry data for a given question
- Query and join healthcare datasets in SQL and analyze them in Python with pandas
- Clean, validate, and standardize data using coding systems such as ICD-10, CPT/HCPCS, and LOINC
- Build dashboards that track clinical and operational metrics like readmissions, length of stay, and throughput
- Perform population health analysis and stratify patients by risk
- Measure quality and outcomes using risk adjustment and standardized measure definitions
- Build and evaluate an introductory predictive model and apply HIPAA and responsible-analytics principles
Requirements
- Basic comfort with spreadsheets and working with tabular data
- No clinical background required; healthcare concepts are explained as needed
- Python or SQL experience is helpful but not required — both are introduced from the basics in context
- A computer with internet access (Windows, Mac, or Linux); all tools used are free
- Willingness to work with real, imperfect datasets and verify your own results
Who This Course Is For
- Analysts and reporting staff moving into healthcare or health-plan roles
- Clinicians, nurses, and care managers who want to analyze their own data
- Quality, population health, and operations professionals who work with metrics
- Data professionals from other industries transitioning into health data
- Students preparing for careers in health informatics or healthcare analytics
- Anyone who needs to turn healthcare data into decisions while respecting privacy
Map the major sources of healthcare data and how they differ. You will learn where claims, EHR, and registry data come from, what each can and cannot tell you, and how data flows from a clinical encounter to an analyzable dataset.
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1.1 How healthcare generates data
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1.2 Administrative claims data
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1.3 EHR clinical data and its structure
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1.4 Disease and quality registries
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1.5 Choosing the right source for a question
Build the core querying and analysis skills you will use all course. You will write SQL to extract and join patient, encounter, and claim tables, then move the same data into Python and pandas for flexible analysis.
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2.1 SQL essentials for healthcare tables
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2.2 Joining patients, encounters, and claims
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2.3 Aggregating at the patient and visit level
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2.4 From SQL to pandas DataFrames
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2.5 Reshaping and time-windowing patient data
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2.6 Lab: building a reusable cohort query
Real healthcare data is messy. This module covers how to assess data quality and how clinical coding systems work, so you can clean, standardize, and group codes correctly before analysis.
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3.1 Profiling and assessing data quality
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3.2 ICD-10 diagnosis and procedure codes
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3.3 CPT/HCPCS, NDC, and LOINC
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3.4 Mapping codes into clinical groupers
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3.5 Lab: cleaning a raw claims extract
Learn to define and visualize the clinical and operational metrics that healthcare teams watch every day, and to assemble them into clear, trustworthy dashboards.
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4.1 Defining clinical and operational metrics
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4.2 Readmissions, length of stay, throughput
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4.3 Trends, rates, and denominators done right
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4.4 Building an analytics dashboard
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4.5 Lab: an operational dashboard for a unit
Shift from individual encounters to whole populations. You will define cohorts, measure prevalence and utilization, and stratify patients by risk to focus care management where it matters most.
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5.1 Defining and attributing populations
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5.2 Chronic disease cohorts and prevalence
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5.3 Risk stratification approaches
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5.4 Social determinants and equity in analysis
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5.5 Lab: stratifying a diabetic population
Learn how healthcare quality is measured and reported. This module covers standardized measure specifications, risk adjustment, and how to compare outcomes fairly across providers and over time.
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6.1 Quality measures and their specifications
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6.2 Numerators, denominators, and exclusions
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6.3 Risk adjustment and case mix
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6.4 Comparing outcomes fairly
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6.5 Lab: calculating a readmission measure
Build your first healthcare predictive model and learn to evaluate it honestly. The module focuses on framing a clinically useful question, engineering features from health data, and avoiding common pitfalls like leakage and biased evaluation.
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7.1 Framing a prediction problem (e.g. readmission risk)
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7.2 Feature engineering from health data
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7.3 Logistic regression and tree-based models
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7.4 Evaluation, calibration, and leakage
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7.5 Lab: a readmission risk model
Understand the legal and ethical framework around health data. This module covers HIPAA, protected health information, de-identification, minimum necessary use, and how to recognize and reduce bias in healthcare analyses.
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8.1 HIPAA and protected health information (PHI)
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8.2 De-identification and the Safe Harbor method
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8.3 Minimum necessary, access, and governance
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8.4 Bias, fairness, and responsible reporting
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8.5 Case studies in responsible analytics
Bring everything together. You will take a healthcare dataset from raw extract through cleaning, analysis, and modeling, then present a documented set of findings and recommendations that respect privacy and uncertainty.
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9.1 Scoping the capstone question
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9.2 Building the analytic dataset
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9.3 Analysis, modeling, and validation
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9.4 Communicating findings to stakeholders
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9.5 Capstone submission and review
Dr. Priya Raman
Principal Healthcare Data Scientist, MPH, PhD
About the Instructor
Dr. Priya Raman is a healthcare data scientist with more than 12 years of experience turning clinical and claims data into decisions for hospitals, health plans, and population health programs. She holds a PhD in Biostatistics and a Master of Public Health (MPH), and she has led analytics teams responsible for quality measurement, risk stratification, and predictive modeling.
Earlier in her career, Priya built readmission and care-management models for an accountable care organization and worked on CMS quality measure reporting. She is a frequent speaker on responsible healthcare analytics and has trained hundreds of analysts and clinicians to work confidently and ethically with health data.
Her teaching style is hands-on and grounded in real datasets, with a constant emphasis on data quality, privacy, and honest interpretation of results.
Other Courses by Dr. Priya Raman
Megan Ortiz
I work in hospital quality reporting and finally understand why our claims and EHR numbers never matched. The modules on coding systems and quality measures were directly relevant to my job. I rebuilt one of our readmission reports the week after finishing Module 6.
James Whitfield
Came from a finance analytics background with strong SQL but no healthcare context. This course filled exactly that gap — claims structure, risk adjustment, and HIPAA were the most valuable parts. The predictive modeling module is a solid introduction but light if you already know ML, hence four stars.
Aisha Bello
As a care manager with only spreadsheet experience, I was nervous about the SQL and Python parts, but they are introduced gently and always tied to a real healthcare question. The capstone made me confident enough to stratify our own patient panel by risk. Excellent, practical course.
No clinical background is required — healthcare concepts are explained as they come up. You should be comfortable working with data in spreadsheets. Python or SQL experience is helpful but not assumed; both are introduced from the basics and always in the context of a real healthcare task.
The course is structured over 10 weeks at roughly 6 hours per week, including about 38 hours of video plus exercises and the capstone. Many students finish in 8-12 weeks depending on prior experience. You have lifetime access, so you can move faster or slower at your own pace.
You will work with realistic but fully de-identified and synthetic datasets that mimic the structure of claims, EHR, and registry data. No real protected health information is used. The privacy and HIPAA module teaches you how to handle real PHI responsibly in your own work environment.
Everything used in the course is free and open source. You will use a SQL database engine (such as SQLite or PostgreSQL) and Python with pandas and scikit-learn, typically in a Jupyter notebook. Setup instructions and downloadable notebooks are provided for Windows, Mac, and Linux.
Yes. After completing the lessons, exercises, and the capstone project, you receive a certificate of completion that you can add to your LinkedIn profile or resume to demonstrate your healthcare analytics skills.
Yes. The course includes a Q&A discussion board where you can ask questions and get help from the instructor and other students, plus a community space for peer support. Dr. Raman and the teaching team typically respond within 24-48 hours.
In the capstone you take a healthcare dataset end to end. A typical project includes:
- Scoping a clinical or operational question and defining a cohort
- Cleaning a raw extract and standardizing coded fields
- Producing descriptive and population-level analyses
- Building and evaluating an introductory predictive model
- Documenting findings, limitations, and recommendations for stakeholders
The result is a portfolio-ready analysis that demonstrates the full workflow while respecting privacy and uncertainty.
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