Mixed models are an extremely useful modeling tool for situations in which there is some dependency among observations in the data, where the correlation typically arises from the observations being clustered in some way. For example, it is quite common to have data in which we have repeated measurements for the units of observation, or in which the units of observation are otherwise grouped together (e.g. students within school, cities within geographic region). While there are different ways to approach such a situation, mixed models are a very common and powerful tool to do so. In addition, they have ties to other statistical approaches that further expand their applicability.Read more.
The techniques and tools covered in Mixed Models with R are most similar to the requirements found in Data Scientist job advertisements.