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ME1316 Quantitative Business and Operations Analytics 6.0 credits

This course sets out to give students applied knowledge about quantitative analysis in industrial operations. The students should after completed course be well prepared to both perform analyses, interpret the results based on context and critically evaluate analyses performed by others.  

Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.

Headings with content from the Course syllabus ME1316 (Spring 2022–) are denoted with an asterisk ( )

Content and learning outcomes

Course disposition

See the course syllabus.

Course contents

This course intends to give the students an understanding of how quantitative analyses can be used in business. In an increasingly digitalised world, it s predicted that the importance of these skills will increase. The course focuses on the application of statistical methods to carry out business and operations analyses based on large datasets. The course consists of two parts. Firstly, the students' ability to analyse and present data in an industrial context is developed. Secondly, the students' ability to critically review, both their own analyses and those of others, through discussions of limitations and assumptions, both in methods and in data.

The course is examined through attendance in seminars, group projects where the students carry out different forms of analyses and an individual examination.

Intended learning outcomes

To provide the course participants applied knowledge of quantitative data analysis in different types of industrial activities. The aim is that the course participants should be prepared on completion of the course to both carry out analyses, interpret the results of these analyses and to critically review analyses completed by others.

After passing the course, the students should be able to:

  1. Give an account of why and in what ways quantitative analyses are used in business operations.
  2. Apply quantitative analytical methods on operational data such as production data, customer data and product data.
  3. Interpret result of statistical analysis in an business context.
  4. Give an account of assumptions and limitations in a completed analysis. This includes both analysis of data quality and the assumptions and limitations of the methods.

Literature and preparations

Specific prerequisites

45 first cycle higher education credits,

6 higher education credits in basic industrial engineering and management or in business administration,

Knowledge in probability theory and statistics equivalent to at least 6 higher education credits, for example by having participated in SF1918 Probability Theory and Statistics.

Recommended prerequisites

None in addition to the specific prerequisites.




Course literature will be announced at the start of the course.

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

A, B, C, D, E, FX, F


  • INL1 - Assignment, 3.0 credits, grading scale: A, B, C, D, E, FX, F
  • SEM1 - Seminar assignment, 1.0 credits, grading scale: P, F
  • TEN1 - Exam, 2.0 credits, grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Opportunity to complete the requirements via supplementary examination

See the course syllabus.

Opportunity to raise an approved grade via renewed examination

Decided by the examiner.


Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study


Education cycle

First cycle

Add-on studies

See the course and program directory.


Andreas Feldmann (