Health Data Analytics Fundamentals
( 26 Modules )

Module #1
Introduction to Health Data Analytics
Overview of the role of data analytics in healthcare, importance of health data analytics, and course objectives

Module #2
Healthcare Data Sources and Systems
Types of healthcare data, data sources, and systems, including EHRs, claims data, and public health data

Module #3
Data Quality and Cleaning
Importance of data quality, types of data errors, and best practices for data cleaning and preprocessing

Module #4
Data Visualization for Healthcare
Introduction to data visualization principles, tools, and best practices for healthcare data

Module #5
Descriptive Analytics for Healthcare
Introduction to descriptive analytics, measures of central tendency, and data summarization techniques

Module #6
Inferential Statistics for Healthcare
Introduction to inferential statistics, hypothesis testing, and confidence intervals

Module #7
Data Mining and Pattern Recognition
Introduction to data mining, pattern recognition, and machine learning concepts

Module #8
Predictive Modeling for Healthcare
Introduction to predictive modeling, types of predictive models, and evaluation metrics

Module #9
Regression Analysis for Healthcare
Introduction to regression analysis, simple and multiple regression, and model interpretation

Module #10
Machine Learning for Healthcare
Introduction to machine learning, supervised and unsupervised learning, and common machine learning algorithms

Module #11
Text Analytics for Healthcare
Introduction to text analytics, natural language processing, and sentiment analysis

Module #12
Healthcare Data Governance and Ethics
Importance of data governance, data ethics, and regulatory considerations in healthcare analytics

Module #13
Healthcare Data Security and Privacy
Importance of data security, privacy considerations, and HIPAA regulations

Module #14
Big Data Analytics for Healthcare
Introduction to big data analytics, Hadoop, and NoSQL databases

Module #15
Cloud Computing for Healthcare Analytics
Introduction to cloud computing, cloud-based analytics platforms, and scalability considerations

Module #16
Real-World Applications of Health Data Analytics
Case studies and examples of health data analytics applications in healthcare, including population health, precision medicine, and quality improvement

Module #17
Health Data Analytics Tools and Technologies
Overview of popular health data analytics tools and technologies, including R, Python, Tableau, and Power BI

Module #18
Health Data Analytics Project Development
Guidelines for developing a health data analytics project, including problem definition, data preparation, and model evaluation

Module #19
Communication and Storytelling in Health Data Analytics
Best practices for communicating complex analytics results to non-technical stakeholders and telling stories with data

Module #20
Health Data Analytics for Quality Improvement
Using health data analytics to drive quality improvement initiatives, including identifying opportunities, developing interventions, and evaluating outcomes

Module #21
Health Data Analytics for Population Health
Using health data analytics to inform population health strategies, including identifying high-risk populations and developing targeted interventions

Module #22
Health Data Analytics for Precision Medicine
Using health data analytics to support precision medicine initiatives, including genomics, pharmaceuticals, and personalized treatment

Module #23
Special Topics in Health Data Analytics
Exploration of emerging trends and special topics in health data analytics, including AI, blockchain, and wearables

Module #24
Career Development and Professionalism in Health Data Analytics
Career paths, job roles, and professionalism in health data analytics, including certification, networking, and ongoing education

Module #25
Health Data Analytics Project Presentations
Student project presentations and feedback

Module #26
Course Wrap-Up & Conclusion
Planning next steps in Health Data Analytics Fundamentals career

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Health Data Analytics Fundamentals ( 26 Modules )

Health Data Analytics Fundamentals
( 26 Modules )