Probability, Statistical Inference, and Stochastic Processes

In an uncertain world, understanding the language of probability is essential for making sense of data and predicting future outcomes. This course provides a robust foundation in probabilistic reasoning, moving beyond basic concepts to practical applications. By completing this course, you will develop a strong analytical framework to interpret random phenomena, perform basic statistical inference, and analyze sequential events, empowering you to approach complex problems with confidence. What you'll learn: * Understand fundamental probability concepts, including axioms, conditional probability, and independence. * Apply discrete and continuous random variables, common probability distributions, and expected values. * Master the core principles of statistical inference, covering point estimation, confidence intervals, and hypothesis testing. * Explore introductory Bayesian reasoning and its application to updating beliefs with new evidence. * Analyze basic stochastic processes, such as Markov chains, to model sequences of events over time. * Practice interpreting and communicating probabilistic insights from various real-world scenarios. The course begins with foundational probability theory, progresses through random variables and their distributions, then delves into the essentials of statistical inference, and concludes with an introduction to stochastic processes. This course is designed for absolute beginners with no prior knowledge of probability or statistics. All concepts are introduced from the ground up. Start your journey into the fascinating world of probability and data-driven decision-making today.