In the daily use of data science in business and industrial settings it is often necessary to infer how much time it takes until a until a certain event happens, e.g., what is the time to failure of a certain part. Besides industrial monitoring, these questions are common in customer and product analytics, finance, and so on. Common to all these scenarios is the fact we generally want to make statements regarding events that rarely happen, based on data consisting only of partial observations, due to censoring (e.g., one only knows a certain part has not failed during the experiment, but that does not mean the part will not fail in the future). Survival analysis allows us to address these challenges in a principled way by incorporating statistical thinking and modeling into the inference procedures. As sound statistical modeling is at the heart of the methods in survival analysis, this course is designed to equip students with a knowledge of statistical modeling and thinking, illustrated in depth in the context of survival analysis, and covering both classical and modern methodology.