Sequential Decision Making in Data Science: Decisions Over Time

In a dynamic world, making decisions based on a single snapshot of data is rarely sufficient. To build truly smart systems, you must understand how to model, analyze, and optimize choices that unfold sequentially over time. This course guides you from the fundamental principles of temporal data to implementing structured decision-making models that adapt as new information arrives. You will learn to navigate uncertainty, evaluate long-term trade-offs, and design systems that learn from feedback loops. What you'll learn: Understand the core terminology and mathematical foundations of sequential decision-making; Apply probability and risk analysis to choices that impact future states; Design basic feedback loops to continuously update models with incoming data; Explore modern concepts in reinforcement learning and adaptive algorithms; Practice structuring step-by-step decision frameworks through written scenarios. Starting with essential definitions and foundational probability, the course guides you through clear, written explanations of sequential algorithms and practical decision-making strategies. This text-based course is designed for beginners, data analysts, and aspiring decision scientists with no prior advanced mathematical training. Start learning how to make optimal, time-sensitive decisions with your data today.