The goal of this course is to introduce students to the basics of applied statistics, univariate and multivariate, from a descriptive and inferential point of view. For inference we will take both a frequentist and a Bayesian prospective. Simulation algorithms such as optimization algorithms, Bootstrap, Jackknife, Monte Carlo integration, Importance sampling and Markov chain Monte Carlo will also be introduced. Data and examples will be used to illustrate the theory and the code to perform applied statistical analysis will be either provided by the instructor or developed by the students.