April 11, 2024
Updated May 20, 2025
20 minute read
A Comprehensive Guide to a Career as a Public Health Scientist
Public health science is a dynamic and vital field dedicated to protecting and improving the health of entire populations. It encompasses a wide range of activities, from tracking disease outbreaks and researching environmental hazards to developing health policies and promoting healthy behaviors. At its core, public health science seeks to prevent illness, prolong life, and enhance well-being through organized community efforts. This field is inherently interdisciplinary, drawing on expertise from medicine, epidemiology, biostatistics, social sciences, and environmental health, among others. For individuals passionate about making a broad impact, a career as a public health scientist offers the opportunity to contribute to the well-being of communities at local, national, and even global levels.
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Find a path to becoming a Public Health Scientist. Learn more at:
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Reading list
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Provides a comprehensive overview of deep learning. It covers a wide range of topics, from basic concepts to advanced topics. It is written in a clear and concise style, and it includes many examples and exercises to help readers understand the material.
Providing a comprehensive overview of the field, this book covers study designs, data analysis methods, and the application of epidemiology to public health.
Classic text on statistical learning. It covers a wide range of topics, from supervised learning to unsupervised learning. It is written in a clear and concise style, and it includes many examples and exercises to help readers understand the material.
Provides a comprehensive overview of mathematical models for infectious disease spread, covering topics such as compartmental models, spatial models, and stochastic models. It is an essential resource for researchers and students in the field.
Provides a comprehensive overview of measure theory and probability theory. It covers a wide range of topics, from basic concepts to advanced topics. It is written in a clear and concise style, and it includes many examples and exercises to help readers understand the material.
Provides a comprehensive overview of machine learning. It covers a wide range of topics, from supervised learning to unsupervised learning. It is written in a clear and concise style, and it includes many examples and exercises to help readers understand the material.
Provides a comprehensive overview of reinforcement learning. It covers a wide range of topics, from basic concepts to advanced topics. It is written in a clear and concise style, and it includes many examples and exercises to help readers understand the material.
Emphasizes the application of epidemiology to public health, providing practical guidance.
Provides a comprehensive overview of mathematical modeling in epidemiology, covering topics such as compartmental models, spatial models, and stochastic models. It is an essential resource for researchers and students in the field.
Provides a comprehensive overview of statistical models, from basic concepts to advanced topics. It is written in a clear and concise style, and it includes many examples and exercises to help readers understand the material.
Classic text on generalized linear models. It provides a comprehensive overview of the topic, from basic concepts to advanced topics. It is written in a clear and concise style, and it includes many examples and exercises to help readers understand the material.
Provides a comprehensive overview of model selection and multimodel inference. It is written in a clear and concise style, and it includes many examples and exercises to help readers understand the material.
Provides a hands-on introduction to machine learning. It covers a wide range of topics, from data preprocessing to model evaluation. It is written in a clear and concise style, and it includes many examples and exercises to help readers understand the material.
Considered a more advanced counterpart to 'An Introduction to Statistical Learning,' this book delves deeper into the theoretical underpinnings of statistical learning. It's a valuable reference for those looking to deepen their understanding and is highly regarded in both academic and industry settings. It covers a wide range of topics relevant to statistical modeling and machine learning.
A comprehensive and widely used textbook covering linear statistical models, this book is excellent for developing a deep understanding of the subject. It is often used in undergraduate and graduate programs and serves as a valuable reference for professionals. It covers regression, analysis of variance, and experimental design.
Bridges the gap between statistical modeling and machine learning, focusing on the practical aspects of building predictive models. It covers various techniques and provides guidance on the entire modeling process. It's highly relevant for those interested in applying statistical modeling for prediction.
Considered a classic, this book offers a deep and intuitive look at statistical models, particularly in the linear setting. Written by a highly respected statistician, it provides valuable insights into the principles of model building. It's suitable for those with a solid statistical background looking for a foundational perspective.
Focusing on practical applications rather than just theory, this book provides numerous examples to illustrate regression analysis techniques. It's a good resource for understanding how to apply regression in real-world scenarios and is suitable for those with an elementary statistics background. The book covers diagnostics, transformations, and various regression types.
Standard reference for analyzing categorical data using statistical models like logistic regression and log-linear models. It's essential for researchers and practitioners who work with data that fall into categories. It provides comprehensive coverage of methods and examples.
Focuses on the relationship between nutrition and health, addressing the design, conduct, and analysis of nutritional epidemiological studies.
Focusing on cancer epidemiology, this book covers the etiology, risk factors, and prevention of various types of cancer.
Covers statistical modeling from a computational perspective, which is increasingly important in modern statistics. It delves into computational methods relevant to fitting and analyzing statistical models. It's suitable for graduate students and researchers interested in the computational aspects.
Provides a comprehensive overview of statistical methods in infectious disease epidemiology, covering topics such as study design, data analysis, and interpretation of results. It is an essential resource for researchers and students in the field.
Offers an innovative approach to statistical modeling, emphasizing the use of models to understand and quantify variation in data. It introduces statistical reasoning with a focus on applying concepts using R. It's suitable for building a solid foundation and understanding the logic behind statistical inference.
For more information about how these books relate to this course, visit:
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