COURSE PLAN: Observations from biological laboratory experiments, clinical trials, and health surveys always carry some amount of uncertainty. In many cases, especially for laboratory experiments, it is inevitable to just ignore this uncertainty due to large variation in observations. Tools from statistics are very useful in analyzing this uncertainty and filtering noise from data. Also, due to the advancement of microscopy and molecular tools, rich data can be generated from experiments. To make sense of this data, we need to integrate this data into a model using tools from statistics. In this course, we will discuss different statistical tools required to (i) analyze our observations, (ii) design new experiments, and (iii) integrate a large number of observations in a single unified model.