Summary

The course will provide an overview of everyday challenges in applied statistics through case studies. Students will learn how to use core statistical methods and their extensions, and will use computational and problem-solving tools to provide reproducible solutions for the problems presented.

Content

Keywords

Smoothing, regression analysis, model selection, time series, functional data analysis, time-to-event analysis

Learning Prerequisites

Required courses

Regression Analysis, Statistical Computation and Visualisation

Recommended courses

Time series.  Statistical Inference.

Learning Outcomes

By the end of the course, the student must be able to:

• Propose suitable statistical solutions for real-world problems
• Apply suitable statistical solutions for real-world problems
• Assess / Evaluate the adequacy of a statistical method for a given task
• Report results clearly in writing and orally to different types of stakeholder

Transversal skills

• Give feedback (critique) in an appropriate fashion.
• Take feedback (critique) and respond in an appropriate manner.
• Communicate effectively with professionals from other disciplines.
• Identify the different roles that are involved in well-functioning teams and assume different roles, including leadership roles.

Teaching methods

One hour of lectures per week, plus three hours of work on team mini-projects.

Expected student activities

Students will work on several mini-projects in teams.

Assessment methods

Contrôle continu

Dans le cas de l'art. 3 al. 5 du Règlement de section, l'enseignant décide de la forme de l'examen qu'il communique aux étudiants concernés.

Supervision