
edXActuaries don't predict the future—they quantify uncertainty. This course teaches you the mathematical language to do that: probability distributions and their properties.
You'll start with how to extract central tendency and variation from data, move into foundational probability concepts (events, conditional probability, independence), and focus on the discrete distributions that matter most in actuarial practice. You're learning to model questions like: "What's the likelihood a policyholder files a claim?" and "How severe could losses be?"
The course bridges data analysis and practical risk modeling—exactly what actuaries do when they price products and set reserves.
Actuarial work involves modeling future contingent events that are uncertain in terms of timing, frequency and severity. Understanding the central tendency of a contingency and its possible distribution is critical for an actuary to help individuals and institutions manage risk. This course starts with a brief discussion of data analysis, focusing on the central tendency and distribution of data. Introductory probability concepts are introduced next. The course concludes with a study of discrete probability models with an eye toward actuarial application.
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