Course syllabus for Statistical modeling in logistics

Course syllabus adopted 2022-02-01 by Head of Programme (or corresponding).

Overview

  • Swedish nameStatistisk modellering med logistiktillämpningar
  • CodeMMS075
  • Credits7.5 Credits
  • OwnerTSILO
  • Education cycleFirst-cycle
  • Main field of studyShipping and Marine Technology
  • DepartmentMECHANICS AND MARITIME SCIENCES
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 81125
  • Open for exchange studentsYes
  • Only students with the course round in the programme overview.

Credit distribution

0119 Written and oral assignments, part A 5 c
Grading: UG		0 c0 c5 c0 c0 c0 c
0219 Examination, part B 2.5 c
Grading: TH		0 c0 c2.5 c0 c0 c0 c

In programmes

Examiner

Eligibility

General entry requirements for bachelor's level (first cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

The same as for the programme that owns the course.
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

Knowledge and skills equivalent to the learning outcomes of the following courses:

SJO915 Applied statistics

Aim

The course aims to give the students skills in statistical modeling on larger data sets linked to the logistics area. The students get to develop their skills in applying the theoretical knowledge they have acquired in previous courses on large, unstructured data sets.

Learning outcomes (after completion of the course the student should be able to)

- Demonstrate an understanding of the key concepts and ideas in statistical modeling on larger datasets - Describe suitable statistical methods for using on larger datasets relevant in logistics - Choose and use appropriate statistical methods for answering a logistics related problem, and report the findings in a suitable and compelling format - Critically evaluate statistical materials and methods and reason about their limitations - Reflect on ethical aspects and considerations when collecting and analyzing larger datasets

Content

- Key concepts in statistical modeling with a focus on larger datasets - Statistical methods relevant for statistical modeling in logistics - Opportunities and limitations of different statistical methods - Reporting statistical findings in a compelling way - Ethical aspects on collecting and analyzing data

Organisation

The course consists of one or more project assignments together with lectures and a written exam.

Literature

See course homepage.

Examination including compulsory elements

One or more project tasks (part A). Written examination (part B). The final grade is determined by the grade on the written exam.

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.