Introduction to Probabilistic Prediction

How do we make accurate decisions when the future is uncertain? Whether you are analyzing business trends, forecasting weather, or building predictive models, understanding how to express predictions in terms of probability is a vital modern skill. This course guides you through the fundamental principles of probability and shows you how to apply them to real-world forecasting. You will transition from guessing outcomes to calculating precise likelihoods using statistical models. By understanding the math behind uncertainty, you will learn to make smarter, data-backed decisions and evaluate risks with confidence. What you'll learn: - Understand foundational concepts of probability, random variables, and probability distributions - Calculate conditional probabilities and apply Bayes' theorem to update predictions with new data - Evaluate prediction accuracy using modern metrics like Brier scores and logarithmic loss - Build basic predictive models that account for uncertainty and variance - Apply probabilistic thinking to real-world scenarios, including risk assessment and decision trees This course begins with core terminology and essential mathematical foundations before moving on to practical modeling techniques and modern evaluation metrics. Through clear written explanations and structured exercises, you will build a practical framework for analyzing uncertainty. This course is designed for beginners who want to develop strong quantitative reasoning skills. No advanced background in mathematics or programming is required. Start thinking probabilistically and master the science of predicting the future.