Computational Thinking with Julia: Epidemic Modeling and Data Science

How do scientists and researchers make sense of global crises and predict the spread of infectious diseases? The answer lies in computational thinking—the ability to translate complex real-world phenomena into mathematical models and computer code. This text-based course introduces you to the foundations of computational thinking using Julia, a high-performance programming language designed specifically for scientific computing and data analysis. By reading through clear explanations and structured code examples, you will transform from a coding beginner into a practitioner capable of simulating real-world scenarios. You will learn how to write clean Julia code, manipulate datasets, and build mathematical models to understand systemic challenges. What you'll learn: Understand the core principles of computational thinking and algorithmic problem-solving; Write and execute clean, modern Julia code using standard syntax and package management; Build and simulate mathematical models, including classic epidemiological models like the SIR model; Analyze and visualize data trends using Julia data science libraries; Apply foundational mathematical concepts to simulate real-world disease transmission dynamics. The course begins with the absolute basics of Julia syntax, data types, and logical flow before introducing data manipulation and scientific libraries. You will then progress step-by-step to constructing, running, and interpreting your own computational models. This course is designed for beginners with no prior programming experience or advanced mathematical background. Start reading today to build your foundational skills in scientific computing and epidemiological modeling.