Demonstrates how to calculate these metrics to compare the health status of different countries and explores the world of infectious diseases. Tests various machine learning tools for analyzing trends and relationships among key variables, aiming to prevent unexpected outcomes.
Demonstrates how to calculate these metrics to compare the health status of different countries and explores the world of infectious diseases. Tests various machine learning tools for analyzing trends and relationships among key variables, aiming to prevent unexpected outcomes.
Federica Gazzelloni is an Actuary and Statistician with a focus on health metrics, machine learning, and data visualisation. Her collaboration with the Institute for Health Metrics and Evaluation (IHME) inspired her to create this book as a practical guide for analysing health metrics data, bridging complex methodologies with real-world applications. Prior to her work in public health, she gained experience in both corporate and academic settings, where she served as a research-oriented actuary, taught mathematics to high school students, and instructed university students in computer science. This varied background enables her to bridge complex statistical concepts with real-world applications, making data-driven insights accessible to broader audiences. A dedicated advocate for open-source technology and inclusivity, Federica is an active contributor to organisations such as the Data Science Learning Community (DSLC), Actex Learning, The Carpentries, Bioconductor, and the R Consortium. As the lead organiser of R-Ladies Rome, she fosters a supportive space for underrepresented groups in technology to develop skills in data science and visualisation. Her passion for turning complex data into clear, actionable visual narratives is reflected throughout her work. For updates on her latest projects and initiatives, visit federicagazzelloni.com.
Inhaltsangabe
1. Introduction 2. Introduction to Health Metrics 3. Methods and Calculations 4. Metrics Components 5. Causes and Risks 6. Introduction to Machine Learning 7. Techniques for Machine Learning Applications 8. Essential R Packages for Machine Learning 9. Predictive Modelling and Beyond 10. Introduction to Data Visualization 11. Interpreting Model Results Through Visualization 12. Spatial Data Modelling and Visualization 13. Advanced Data Visualization Techniques 14. Introduction to Infectious Diseases 15. COVID-19 Outbreaks 16. The Case of Malaria 17. Summary: The State of Health
1. Introduction 2. Introduction to Health Metrics 3. Methods and Calculations 4. Metrics Components 5. Causes and Risks 6. Introduction to Machine Learning 7. Techniques for Machine Learning Applications 8. Essential R Packages for Machine Learning 9. Predictive Modelling and Beyond 10. Introduction to Data Visualization 11. Interpreting Model Results Through Visualization 12. Spatial Data Modelling and Visualization 13. Advanced Data Visualization Techniques 14. Introduction to Infectious Diseases 15. COVID-19 Outbreaks 16. The Case of Malaria 17. Summary: The State of Health
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