MATH-517 / 5 credits

Teacher: Gholam Mehdi

Language: English


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 regression

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

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

Office hours No
Assistants Yes
Forum Yes

Resources

Bibliography

Wickham H. & Grolemund G. (2017) R for Data Science (https://r4ds.had.co.nz/)
Davison A. C. & Hinkley D. (1997) Bootstrap Methods and their Application

Ressources en bibliothèque

Prerequisite for

Applied statistics

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
  • 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
  • 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
  • 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

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14 GCD0386   
14-15    
15-16 GCD0386   
16-17    
17-18     
18-19     
19-20     
20-21     
21-22     

Tuesday, 13h - 15h: Lecture GCD0386

Tuesday, 15h - 17h: Exercise, TP GCD0386