Differential privacy has become the leading approach to privacy protection in computer science in the last two decades. There is now large body of theoretical work in this area as well as some important real world deployments of those tools in government agencies and industry. The goal of this course is to introduce the differential privacy paradigm in a statistical context. We will review various definitions of differential privacy, basic strategies for designing differentially private algorithms and discuss their applications to the problem of constructing differentially private tools for statistical data analysis. We will pay special attention to convex optimization approaches and connections to robust statistics.