MATH-517 / 5 credits

Teacher: Mhalla Ep Marchand Linda

Language: English

Withdrawal: It is not allowed to withdraw from this subject after the registration deadline.


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

Office hours No
Assistants Yes
Forum No

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
  • 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
  • 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     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22