Moodle @ FCTUNL

CNS serves as an introductory course aimed at acquainting students with the computational dimensions of various state-of-the-art statistical methods. Specifically, the focus is on computationally demanding techniques like Monte Carlo methods, bootstrap, Markov Chain Monte Carlo (MCMC), etc. The course is tailored for both graduate students, who are pursuing higher-level academic degrees beyond their bachelor's, and advanced undergraduate students, who have progressed beyond the introductory stages of their undergraduate studies. This groundwork enables the students to navigate the essentials comfortably and progressively explore more advanced statistical topics. It is important to note that CNS is not recommended for students who lack prior exposure to at least a foundational course in probability and statistics. Furthermore, a solid grasp of basic to intermediate R programming skills is required, as a substantial portion of the course entails practical programming tasks. The assessment structure heavily relies on the successful completion of three computational projects.

By the end of the course, students are expected to not only understand the computational underlying specifics of the methods studied but also to have developed the skills necessary to solve statistical computing problems using the R statistical software. Additionally, the curriculum should have also  equipped students with the ability to conduct simulation experiments, investigating stochastic  phenomena, performing statistical procedures, and reproducing essential theoretical results.