March 29, 2024
Updated April 10, 2025
18 minute read
Exploring a Career as a Public Health Analyst
Public Health Analysts play a crucial role in safeguarding and improving the health of communities. They are the investigators and storytellers of public health data, transforming raw numbers and observations into actionable insights. By examining health trends, evaluating programs, and identifying risks, they help shape policies and interventions that protect populations from disease and promote overall well-being.
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Find a path to becoming a Public Health Analyst. Learn more at:
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Reading list
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This Spanish-language book provides a comprehensive overview of health systems strengthening, covering topics such as health system governance, financing, service delivery, and monitoring and evaluation.
An essential reference guide from the WHO, this book presents a step-by-step framework for strengthening health systems based on evidence and best practices.
Explores the ways in which the criminal justice system in the United States perpetuates racial inequality, even in the absence of overt racism. It argues that mass incarceration new form of Jim Crow, and it calls for a radical rethinking of our approach to crime and punishment.
Argues that racism in the United States is no longer based on overt prejudice, but rather on a more subtle form of discrimination that is often unconscious and unintentional. Bonilla-Silva calls this "color-blind racism," and he shows how it operates in a variety of institutions, including the criminal justice system, the education system, and the housing market.
This French-language book provides a comprehensive overview of health systems strengthening, covering topics such as health system governance, financing, service delivery, and monitoring and evaluation.
Written by renowned experts in the field, this book covers the entire spectrum of health systems strengthening, from planning and implementation to monitoring and evaluation.
Provides a comprehensive overview of the history of racial segregation in the United States. Rothstein shows how the government has played a major role in creating and maintaining racial inequality, through policies such as redlining, zoning, and urban renewal. This book is essential reading for anyone who wants to understand the roots of structural racism in the United States.
Collection of essays by James Baldwin that explore the history of racism in the United States and its impact on the lives of African Americans. Baldwin writes with passion and eloquence about the need for racial justice, and his work has been praised by critics for its insights into the human condition.
This textbook covers hypothesis testing in depth, including both frequentist and Bayesian approaches. It is suitable for graduate students and researchers who need a comprehensive understanding of the subject.
Novel that tells the story of a former slave who is haunted by the memory of her murdered daughter. Morrison's novel powerful exploration of the horrors of slavery and its aftermath, and it offers a unique perspective on the experiences of African Americans in the United States.
Letter from Ta-Nehisi Coates to his son about the history of racism in the United States and the challenges that African Americans face today. Coates writes with honesty and vulnerability about his own experiences with racism, and he offers a powerful indictment of the systems that perpetuate racial inequality.
Classic work of African American literature that explores the experiences of African Americans in the United States. Du Bois writes about the beauty and resilience of black culture, as well as the challenges that African Americans face in a racist society.
This textbook provides a comprehensive overview of modern statistical learning methods, including hypothesis testing.
Comprehensively covers statistical methods commonly applied in the atmospheric sciences. It includes hypothesis testing, regression, time series analysis, and more, serving as a valuable resource for students and researchers.
Examines the history of policing in the United States and its impact on African American men. Davis argues that the criminal justice system is biased against African Americans, and she calls for a radical transformation of the way that we police our communities.
Explores the history of the "condemnation of blackness" in the United States. Muhammad shows how ideas about black inferiority have been used to justify racism and discrimination, and he argues that these ideas continue to shape the way that we think about crime and punishment today.
Considered a classic in the field, this book offers a deep dive into the theoretical underpinnings of hypothesis testing. It is highly rigorous and best suited for graduate students and researchers focusing on the mathematical theory of statistics. It serves as an invaluable reference for advanced topics.
Argues that racism is not just a matter of individual prejudice, but rather a system of social and political domination. Mills shows how the racial contract, which is an unspoken agreement between whites and non-whites, has created and maintained racial inequality in the United States.
Provides a more accessible introduction to statistical learning concepts, including hypothesis testing, with a strong emphasis on practical applications using R. It is well-suited for upper-level undergraduate students and those in applied fields like data science. It bridges theory and practice effectively and is widely used as a textbook.
Offers a concise yet comprehensive overview of statistical inference, including hypothesis testing, suitable for students in statistics, machine learning, and other quantitative fields. It moves quickly and covers a broad range of topics, making it excellent for those with a solid mathematical background seeking a fast-paced introduction or review.
Provides a comprehensive overview of hypothesis testing in clinical trials.
Is highly relevant for those interested in applying hypothesis testing in a business context, specifically for online controlled experiments (A/B testing). It covers practical considerations and statistical nuances of hypothesis testing in this domain and is suitable for practitioners in data science, marketing, and product management.
A more advanced counterpart to 'Introduction to Statistical Learning,' this book covers a wide range of statistical learning methods, with relevant sections on inference and hypothesis testing. It is aimed at graduate students and researchers and key reference in the data science community. It provides a deeper theoretical understanding alongside practical algorithms.
Focuses on robust statistical methods, which are particularly relevant in contemporary data analysis when assumptions of traditional tests are not met. It covers robust approaches to hypothesis testing and is valuable for researchers and practitioners dealing with real-world data that may contain outliers or deviations from normality. The latest editions incorporate R.
For more information about how these books relate to this course, visit:
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