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.
Working as a Public Health Analyst can be deeply engaging. You might find yourself analyzing data to pinpoint the source of a disease outbreak, evaluating the effectiveness of a community health initiative, or presenting findings to policymakers to advocate for healthier environments. This career often involves a blend of technical skill, critical thinking, and a strong sense of social responsibility, making it a compelling choice for those passionate about making a tangible difference in people's lives.
What Does a Public Health Analyst Do?
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Find a path to becoming a Public Health Analyst. Learn more at:
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Reading list
We've selected some books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Public Health Analyst.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Comprehensive introduction to the field of health data science. It covers the basics of data science, including data collection, analysis, and visualization, as well as specific applications of data science in healthcare. It can help readers understand the fundamentals of health data science and develop skills in data analysis and interpretation.
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.
Explores the ways in which white people often react defensively when they are confronted with racism. DiAngelo argues that this defensiveness form of white fragility, and she shows how it can prevent white people from understanding the experiences of people of color and from working to create a more just society.
Provides a modern introduction to statistics using R, covering data handling, visualization, and statistical inference, including hypothesis testing. It is suitable for students and practitioners who want to learn statistical concepts alongside practical implementation in R. It reflects contemporary approaches to data analysis.
Provides an introduction to data analytics for healthcare professionals. It covers topics such as data collection, analysis, and interpretation, and valuable resource for learners seeking to use data to improve patient care.
Is designed for upper-level undergraduate students and provides a comprehensive introduction to both probability and statistical inference, including essential topics in hypothesis testing. It is known for its clear explanations and numerous examples, making it a good resource for solidifying fundamental concepts.
Another book by Rand Wilcox focusing on modern statistical methods, this text covers alternatives to classical techniques when assumptions are violated. It includes robust hypothesis testing procedures and is valuable for researchers and graduate students seeking to apply more robust methods in their work.
Authored by a highly influential statistician, this book discusses the fundamental principles of statistical inference, including different approaches to hypothesis testing. It offers a high-level perspective on the subject and is suitable for graduate students and researchers with a solid statistical background.
Provides a practical guide to becoming an antiracist. Kendi offers a clear and concise definition of racism, and he outlines the steps that people can take to challenge racism in their own lives and in the world around them.
A concise book covering the essential concepts of statistical inference, including hypothesis testing, aimed at advanced undergraduate and graduate students. It provides a solid theoretical overview without getting bogged down in excessive mathematical detail, making it a good option for those seeking a focused theoretical review.
Offers an intuitive and practical introduction to hypothesis testing, focusing on understanding the concepts and applying them to make data-driven decisions. It is suitable for beginners and those who need to understand hypothesis testing for business or data analysis without a deep mathematical background. It emphasizes interpretation and practical application.
Provides a detailed guide to permutation tests, which are non-parametric methods for hypothesis testing that do not rely on assumptions about the distribution of the data. It is valuable for researchers and practitioners looking for flexible alternatives to traditional parametric tests.
Delves into the philosophical underpinnings of statistical inference and hypothesis testing, particularly the author's severe testing philosophy. It's a more theoretical and philosophical take on the topic, suitable for graduate students and researchers interested in the foundations and interpretations of statistical methods. It offers a critical perspective on common statistical practices.
Provides a comprehensive overview of the use of health informatics in interprofessional healthcare. It covers topics such as data collection, analysis, and visualization as well as specific applications of health informatics in interprofessional healthcare settings.
Simplifies statistical concepts, including hypothesis testing, for readers without a strong quantitative background. It good resource for high school and undergraduate students, or anyone seeking a clear and accessible introduction to the subject. It focuses on explaining the 'why' and 'how' in a straightforward manner.
Utilizing a systems dynamics approach, this book offers a unique perspective on the complexity of health systems and their interactions.
Provides a broad overview of data science in healthcare, covering topics such as data collection, analysis, and interpretation. It good starting point for learners new to the field.
Provides a visually-oriented introduction to the concepts of hypothesis testing and statistical significance. It is designed for beginners and aims to build intuition through illustrations and clear explanations, making it a good supplementary resource for those new to the topic.
This engaging book tells the stories behind the development of modern statistics and the personalities involved, including the origins of hypothesis testing. While not a textbook, it provides valuable historical context and a broader understanding of the field's evolution, making the concepts of hypothesis testing more relatable. It's suitable for a general audience and those seeking historical background.
Offers a very basic and approachable introduction to statistics, including the fundamental ideas behind hypothesis testing, for those with little to no prior exposure or a fear of the subject. It is ideal for high school students or individuals in non-quantitative fields needing a gentle introduction.
For more information about how these books relate to this course, visit:
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