The Healthcare Data Analytics is designed to equip professionals with the skills to analyze, interpret, and leverage healthcare data for informed decision-making and improved patient outcomes. This comprehensive program covers the entire healthcare analytics lifecycle, including data collection, cleaning, visualization, predictive modeling, and reporting using advanced analytical tools. Participants will gain hands-on experience with real-world healthcare datasets, learning to extract actionable insights that can optimize hospital operations, enhance patient care, and support strategic healthcare initiatives. The course blends theoretical knowledge with practical applications, ensuring that learners can apply analytics techniques effectively in diverse healthcare settings, from hospitals and clinics to research institutions and health-tech companies.
Module 1: Introduction to Healthcare Data Analytics Overview of healthcare analytics and its importance Types of healthcare data: structured vs unstructured Introduction to healthcare data sources (EHRs, claims data, patient surveys) Key concepts: KPIs, metrics, and data-driven decision-making.
Module 2: Healthcare Data Collection & Management Data collection methods in healthcare Data cleaning, preprocessing, and validation techniques Understanding data quality and integrity Introduction to HIPAA and other data privacy regulations.
Module 3: Statistical Methods for Healthcare Analytics Descriptive and inferential statistics Probability, distributions, and hypothesis testing Correlation, regression, and trend analysis Application of statistics in clinical and operational decisions.
Module 4: Data Visualization & Reporting Principles of effective data visualization Tableau, Power BI, Matplotlib, and Seaborn Designing dashboards for hospitals, clinics, and research institutions Communicating insights to non-technical stakeholders.
Module 5: SQL & Database Management Introduction to relational databases and SQL queries Data extraction, filtering, aggregation, and joins Handling large healthcare datasets efficiently Case studies on querying EHR and claims data.
Module 6: Predictive Analytics in Healthcare Introduction to predictive modeling Regression, classification, and time-series forecasting Risk prediction and patient outcome modeling Python, R, and machine learning libraries.
Module 7: Machine Learning & AI in Healthcare Overview of AI applications in healthcare Supervised and unsupervised learning techniques Clinical decision support systems and predictive diagnostics Implementing ML models using Python.
Module 8: Health Informatics & Data Standards Understanding EHRs and healthcare IT systems Healthcare data standards: HL7, FHIR, ICD-10 Data interoperability and integration Real-world case studies of health informatics implementation.
Module 9: Big Data Analytics in Healthcare Introduction to big data frameworks: Hadoop and Spark Handling high-volume healthcare data efficiently Real-time data processing and analytics Applications in population health and operational optimization.
Module 10: Capstone Project & Industry Applications End-to-end healthcare data analytics project Real-world datasets from hospitals or health-tech companies Analysis, visualization, and presentation of actionable insights Guidance on deploying analytics solutions in clinical or operational settings.
Mobile: 9100348679
Email: coursedivine@gmail.com
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