Introduction to Computational Thinking and Data Science in Python

How do we use computers to make sense of the complex data around us? This text-based course guides you through the fundamental principles of computational thinking and data science, showing you how to translate real-world challenges into solvable programming tasks. By reading and working through the structured explanations, you will develop the ability to write Python programs that analyze datasets, run simulations, and model complex systems. You will transition from writing basic code to designing computational solutions that help make data-driven decisions. What you'll learn: - Understand the core principles of computational thinking, optimization models, and graph theory. - Apply Python to clean, structure, and analyze datasets using modern coding practices like type hints. - Build stochastic models and run Monte Carlo simulations to predict real-world outcomes. - Interpret data patterns using statistical concepts, experimental design, and basic machine learning. - Solve optimization problems using greedy algorithms and dynamic programming techniques. - Practice writing clean, modular Python scripts to automate complex data analysis. The course begins with foundational concepts of computational modeling and basic data structures, then progresses to simulations, statistical analysis, and experimental design. You will explore practical techniques for solving optimization problems and organizing your data workflows cleanly. This course is designed for beginners who have a basic grasp of Python syntax and want to apply their skills to data analysis and problem-solving. No advanced mathematics or prior data science experience is required. Start developing your computational mindset and learn to solve real-world problems with data.