Statistical computation and visualisation

Summary

The course will provide the opportunity to tackle real world problems requiring advanced computational skills and visualisation techniques to complement statistical thinking. Students will practice proposing efficient solutions, and effectively communicating the results with stakeholders.

Content

Keywords

Statistical computation, data visualisation, data wrangling, resampling methods, EM algorithm, Bayesian inference

Learning Prerequisites

Required courses

• Probability and statistics
• Linear models

Learning Outcomes

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

• Plan complex visualisation and computational tasks
• Perform complex visualisation and computational tasks
• Implement reproducible computational solutions to statistical problems in modern environments and platforms

Transversal skills

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

Teaching methods

Two lecture hours per week, two hours of exercises and support on mini-projects

Expected student activities

Students will work on mini-projects in teams

Assessment methods

Contrôle continue

Supervision

Resources

Bibliography

Wickham H. & Grolemund G. (2017) R for Data Science

Bootstrap Methods and their Application

An Introduction to Statistical Learning

Ressources en bibliothèque

• Bootstrap Methods and their Application / Davison
• An Introduction to Statistical Learning / Gareth
• R for Data Science / Wickham

Moodle Link

• https://go.epfl.ch/MATH-517

Prerequisite for

Applied Statistics (MATH-516)