Explorez tous les épisodes du podcast Data Science x Public Health

This episode expands on the foundational descriptive statistics covered in the quantitative data episode. We take a deeper look at populations, samples, continuous and categorical variables, and how they are used throughout public health and biostatistics. You’ll also learn how measures like mean, median, mode, variance, standard deviation, and outliers support real-world public health decisions.

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Learn how predictive modeling is used in epidemiology and why it matters in modern public health decision making. In this episode, we break down three major programming languages used in data science and epidemiology — Python, R, and SAS — and show how each language supports analysis, prediction, and real world public health tasks.

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Learn the fundamentals of Epidemiology in this beginner-friendly episode. We cover what Epidemiology is, why it’s important in public health, how it connects to data science, and the role of an Epidemiologist.

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This first new episode discusses the concepts of data science and public health, and then delves into their intersection, exploring the connection between the two fields in greater detail.

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Learn the key infrastructure and management branches of data science—including big data analytics, data warehousing, and cloud computing—and see how they’re applied in real public health settings. This beginner-friendly lesson breaks down each branch in simple terms and walks through step-by-step examples to help you understand how public health organizations store, manage, and analyze data at scale.

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Learn the basics of quantitative statistics and how numerical data is used in research, public health, biostatistics, and data science. This episode breaks down key concepts in a simple way—perfect for beginners who want to understand variables, measures, and how quantitative data helps answer real-world questions.

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In this episode, we break down the meaning of surveillance in epidemiology—what it is, why it matters, and how it is used to protect communities and guide public health decisions. This beginner-friendly lesson walks you through the surveillance process, key functions of epidemiologic surveillance, and an easy step-by-step real-world example showing exactly how surveillance works from detection to public health action.

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In this episode, we break down the application-specific branches of data science—including Natural Language Processing (NLP), Computer Vision, and Deep Learning (Neural Networks, CNNs, RNNs). You’ll learn what each branch means in simple terms, why these techniques matter in public health, and how they’re applied using real-world examples.

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Learn the basics of populations, samples, continuous variables, and categorical variables in biostatistics. In this episode, we explain why these concepts are essential in public health and how they’re used in real data. We also walk through an example problem to help beginners understand the process.

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Learn the basics of outbreaks in epidemiology. In this episode, we explain what outbreaks are, how they’re detected, how they spread, how they are predicted, and the steps used to solve them. We also walk through real outbreak examples in the United States and break down a simple step-by-step problem to help beginners understand the process.

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This episode breaks down the core branches of data science — including machine learning, data engineering, data analysis, business intelligence, data mining, statistics, and probability — and explains how each plays a powerful role in the field of public health.

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In this episode, you’ll learn what biostatistics is, why it’s essential in public health, and how it connects with epidemiology and data science.

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Learn core biostatistics concepts used in public health. This episode explains parametric and non parametric data, confidence intervals, p values, and common statistical tests. You will see how to calculate a confidence interval, when to choose parametric versus non parametric tests, and a step by step example that walks through calculating and interpreting a p value. 

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Learn how to become an epidemiologist with this simple beginner-friendly roadmap. In this episode, you’ll discover the typical work environment, required education, job outlook, important skills, and the different areas epidemiologists specialize in. We also walk through a basic example of the data collection steps used in real public health research.

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Learn what public health analytics is and why it plays a crucial role in modern public health practice. In this beginner friendly episode, we break down what public health analysts do, the importance of data driven decision making, and how analytics connects to data science. We will also walk through a simple real world task that a public health analyst might complete on the job.

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Bias doesn’t look like a mistake — and that’s why it’s so dangerous.

In this episode, we explain how bias enters statistical studies, why bias is fundamentally different from random error, and why it is often difficult to detect and remove. You’ll learn how bias can be built into data collection, study design, and real-world systems, even when advanced statistical or machine learning methods are used.

This episode introduces common types of bias encountered in public health, biostatistics, and data science, and explains how biostatisticians work to identify, assess, and reduce bias while acknowledging its limitations.

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Your immune system doesn’t just react — it learns.

In this episode, we break down adaptive immunity in simple terms and explain how it works, how it differs from innate immunity, and why adaptive immunity is essential in epidemiology and public health. You’ll learn how the body builds immune memory, responds to specific pathogens, and plays a critical role in disease prevention and population-level health strategies.

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Correlation alone isn’t enough to make real-world decisions. In Part 2 of this series, we explore advanced causal inference concepts used in biostatistics, including propensity score methods, dynamic treatment regimes, missing data, unmeasured confounding, and nonparametric and semiparametric estimation. This episode explains how these tools help researchers strengthen causal interpretation, improve study validity, and support evidence-based conclusions in public health and applied research.

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How do diseases actually begin—and why does that matter to epidemiologists? In this episode, we explain what pathogenesis is, how diseases develop at the biological level, and why understanding pathogenesis is essential in epidemiology. You’ll learn how pathogenesis connects with epidemiology through molecular pathological epidemiology (MPE), how molecular mechanisms influence disease progression, and how this knowledge supports disease prevention, risk stratification, and targeted public health interventions.

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Correlation alone doesn’t prove cause. In public health and biostatistics, understanding causal inference and clinical trials methodology is essential for answering real-world questions about interventions, treatments, and outcomes.

In this episode, we explain what causal inference is, how clinical trials are designed, and why these methods matter when interpreting health data. You’ll learn the difference between association and causation, explore key concepts like counterfactuals and interventional questions, and see how tools such as randomized controlled trials (RCTs), observational studies, and directed acyclic graphs (DAGs) are used in biostatistics research.

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Not all outbreaks spread the same way. Some happen all at once, others unfold in waves, and some continue for long periods of time. The difference often comes down to how people are exposed.

In this episode, we break down single, multiple, and continuous exposures in epidemiology using clear definitions, real-world examples, and simple explanations of epidemic curve shapes. Understanding these exposure types helps explain why certain outbreaks explode quickly while others persist over time.

This beginner-friendly overview is designed to build strong foundations in epidemiologic thinking and outbreak analysis.

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Stata is one of the most widely used tools in public health and epidemiology research—but what exactly does it do?

In this episode, we break down what Stata is, why it matters in public health, and how it’s used across fields like biostatistics, epidemiology, economics, education, and medical research.

This beginner-friendly overview focuses on practical understanding, not coding experience.

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Discover how genes become meaningful data! In this episode, we break down the basics of genomics and bioinformatics, explain how they connect to biostatistics, and explore why these fields are transforming biological research and population-level health studies.

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Multiple sclerosis (MS) is a complex neurological disease with patterns that vary across populations and regions. In this episode, we break down what multiple sclerosis is, explore key risk factors and epidemiological associations, and explain why MS trends matter in the field of epidemiology and public health. This beginner-friendly overview focuses on population-level insights rather than clinical treatment.

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Data science plays a critical role in modern public health. In this episode, we explore the most relevant data science projects used in public health practice and explain why hands-on projects are essential for building real-world analytical skills. You’ll learn about project ideas like disease outbreak prediction, health equity analysis, risk modeling, and public health chatbots, along with where to find real datasets from trusted sources such as the CDC, WHO, and Data.gov. 

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How are new drugs actually developed—and where does biostatistics fit in?

In this episode, you’ll learn what pharmaceutical product development is, how biostatistics is used throughout the drug development process, and why statistics play a critical role in turning research into approved medicines. 

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What does herpetology have to do with epidemiology?

In this episode, we break down what herpetology actually studies, why it’s often confused with epidemiology, and where the two fields truly connect through disease ecology and the One Health approach. This beginner-friendly explanation shows how studying reptiles and amphibians can help epidemiologists understand disease transmission, environmental risk, and population-level health.

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Pandemics don’t emerge at random — patterns form long before the crisis becomes visible.

In this episode, we explain how AI models support pandemic preparedness, from scenario modeling and early warning systems to vaccine planning and healthcare resource allocation. You’ll learn how artificial intelligence helps public health experts anticipate outbreaks, test response strategies, and detect emerging threats earlier than traditional methods.

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What does an AI engineer do in public health?

In this episode, we explain what artificial intelligence engineers are, how they work in the public health field, and why their role is becoming increasingly important. You’ll learn where AI engineers typically work, the tools they use, and how AI supports disease surveillance, health analytics, and population-level decision-making.

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Biometry is a term you’ll often see in biostatistics and public health—but what does it actually mean?

In this episode, we break down what biometry is, how it relates to biostatistics, and why it plays an important role in public health research and data analysis. This beginner-friendly overview explains the key differences between biometry and biostatistics and shows where biometry is used in real-world public health applications.

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Cloud computing isn’t just for tech companies—it’s transforming public health. Learn what cloud computing is, how it’s applied in public health and data science, and why it’s essential for modern health initiatives.

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Cholera may seem like a disease of the past, but it remains critical in public health and epidemiology. Learn what cholera is, how it spreads, and why understanding it is essential for protecting populations.

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Nuclear fusion is often discussed as the future of clean energy—but what does it mean for public health? In this episode, you’ll learn what nuclear fusion is, why it’s gaining attention now, and how it could impact population health, environmental safety, and long-term health outcomes. We’ll also explore the role of public health professionals and data science in evaluating fusion’s benefits, risks, and real-world implications.

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Computational biology uses computer science, data analysis, and statistics to understand complex biological and health-related data. In this episode, you’ll learn what computational biology is, how it connects to biostatistics, why it’s important in research and public health, and how these fields work together in real-world applications. 

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Molecular epidemiology helps public health experts track, understand, and control disease outbreaks using genetic data. In this episode, you’ll learn what molecular epidemiology is, why it’s important in public health, and how it’s used in real-world epidemiology practice. This beginner-friendly explanation breaks down key concepts, common techniques, and practical examples.

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This special edition episode closes out the year. Happy holidays, and thank you for your continued support. New weekly uploads resume on January 5, 2026.

Survival analysis is a core method in biostatistics used to study time-to-event data, especially in medical and public health research. In this episode, we break down survival analysis in simple terms, explain why it matters, and discuss how it’s used in real-world studies.

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This special edition episode closes out the year. Happy holidays, and thank you for your continued support. New weekly uploads resume on January 5, 2026.

The Salk vaccine was a major turning point in public health and epidemiology.

In this episode, we explain what the Salk vaccine was, why it was developed, how it helped stop polio, and how large-scale vaccine trials changed disease surveillance and public health practice.

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This special edition episode closes out the year. Happy holidays, and thank you for your continued support. New weekly uploads resume on January 5, 2026.

Healthy People 2030 is a national framework that guides public health goals across the United States—but what does it actually do, and why does it matter?

In this episode, we break down Healthy People 2030 in simple terms, explain how it works, explore its key goals and focus areas, and show you what kind of data and resources you can find on the Healthy People 2030 website. 

Link to Healthy People 2030 Official Website: https://odphp.health.gov/healthypeople

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What’s the real difference between an endemic, epidemic, and pandemic—and why does it matter?

In this episode, we break down these key epidemiology terms in simple, easy-to-understand language. You’ll learn how epidemiologists classify disease spread, what these situations look like in the real world, and why severity isn’t just about which label sounds “worse.”

We also discuss how diseases transition between stages, explain related concepts like outbreaks, sporadic disease, elimination, and eradication, and explore how COVID-19 is currently classified and debated as of 2026.

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What does “degrees of freedom” really mean in biostatistics—and why does it matter?

In this episode, we break down degrees of freedom in simple terms, explain how it’s used in biostatistics, and walk through the formula step by step so you understand what each part represents. 

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Public health doesn’t just treat disease — it works to prevent it at every stage.
In this episode, you’ll learn how epidemiology uses three levels of prevention to reduce disease impact before it starts, detect it early, and manage long-term outcomes. Using a tuberculosis example, this episode walks step-by-step through how prevention works in real-world public health practice.

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Public health is changing fast, and technical skills are becoming more important than ever. In this episode, we break down how a Computer Science degree can support a Master of Public Health program and open doors to modern public health careers.

You’ll learn what each degree focuses on, the skills you gain from both programs, how Computer Science tools can be used during an MPH, and the types of careers this combination can lead to.

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How do epidemiologists decide which approach to use when studying health problems?

In this episode, you’ll learn what an epidemiological approach is, why it matters, and how it guides research decisions in public health. We’ll break down descriptive and analytical epidemiology, hypothesis generation, intervention and evaluation, and explain how study designs like randomized controlled trials, cohort studies, and case-control studies fit into the process. We’ll also touch on modern methods such as statistical modeling, systems dynamics, and Bayesian approaches.

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Parametric tests are some of the most commonly used tools in biostatistics. In this episode, you’ll learn what parametric tests are, why sampling matters, and how Z tests, T tests, and ANOVA are used in biostatistical analysis. This explanation focuses on concepts to help you build a strong foundation in biostatistics.

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Public health is founded on three key pillars: promotion, prevention, and protection. In this episode, you’ll learn what each pillar means, why they are important in public health practice, and how they are applied with simple, real-world examples. This beginner-friendly explanation shows how public health strategies impact daily life.

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In this episode, we break down Chi-square tests and regression analysis in biostatistics using clear, beginner-friendly explanations. You will learn the differences between simple and multiple regression, when to use a Chi-square test, and how confounding and effect modification can impact your results. 

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In this episode, we explore the different types of epidemiology, including infectious disease epidemiology, environmental epidemiology, genetic epidemiology, and cancer epidemiology. This lesson is designed for beginners who want to understand what each category focuses on and why these areas are important in epidemiology. Each category is explained using simple examples to help you connect theory to practice.

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In this episode, we introduce SAS programming for public health, focusing on obesity data. You will learn how SAS is used in public health and explore simple SAS code for analyzing health outcomes. 

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Who actually decides whether a drug works?

In this episode, you’ll learn how biostatisticians play a critical role in clinical trials and FDA regulatory decisions. We break down how biostatisticians support Phase I–IV clinical trials, shape study design, determine sample sizes, analyze trial data, and influence regulatory approval.

You’ll also learn what happens on both sides of the table—where industry biostatisticians defend their analyses and FDA biostatisticians evaluate the evidence—plus the key difference between statistical significance and clinical significance.

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How can public health experts predict disease outbreaks before they spread?
In this episode, you’ll learn how time-series analysis and machine learning are used in public health to monitor trends, detect anomalies, and forecast disease outbreaks.

We break down classical time-series models like ARIMA, Holt, and Holt-Winters, explore how machine learning enhances outbreak prediction, and explain how early-warning models help identify emerging public health threats. 

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