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
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Modern statistical computing environments (e.g., R, Rstudio and Python)
- Aids to efficiency and reproducibility (e.g., GitHub, Markdown, Jupyter)
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Data management, wrangling, and ethics
- Statistical graphics (grammar, good practices, applications, and examples)
- Kernel density estimation and smoothing
- EM algorithm and applications
- Resampling methods for uncertainty assessment (bootstrap, jackknife, cross-validation), with applications to regression, time series, and dependent data
- Monte Carlo methods for sampling and numerical integration
- Introduction to Bayesian inference
-
Markov chain Monte Carlo techniques (Gibbs sampler, Metropolis-Hastings algorithm, Hamiltonian Monte Carlo, convergence diagnostics) and software (e.g., Stan)
Keywords
Bayesian inference, Data visualisation, Data wrangling, EM algorithm, MCMC, Resampling methods, Statistical computation.
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
- Expound the main approaches used for problem solving
Transversal skills
- Take feedback (critique) and respond in an appropriate manner.
- Demonstrate the capacity for critical thinking
- Identify the different roles that are involved in well-functioning teams and assume different roles, including leadership roles.
- Write a scientific or technical report.
Teaching methods
Two lecture hours per week, two hours of exercises and support on mini-projects and assignments
Expected student activities
Students will work on individual assignments and mini-projects in teams
Assessment methods
Contrôle continue
Supervision
| Office hours | No |
| Assistants | Yes |
| Forum | No |
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
- R for Data Science / Wickham
- An Introduction to Statistical Learning / Gareth
Moodle Link
Prerequisite for
Applied Statistics (MATH-516)
In the programs
- Semester: Fall
- Exam form: During the semester (winter session)
- Subject examined: Statistical computation and visualisation
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: During the semester (winter session)
- Subject examined: Statistical computation and visualisation
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: During the semester (winter session)
- Subject examined: Statistical computation and visualisation
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: During the semester (winter session)
- Subject examined: Statistical computation and visualisation
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: During the semester (winter session)
- Subject examined: Statistical computation and visualisation
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: mandatory
- Semester: Fall
- Exam form: During the semester (winter session)
- Subject examined: Statistical computation and visualisation
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: mandatory