Data Science and Process Improvement


Prerequisite/Recommended prerequisite for participation in the module

This module is based on knowledge gained in the modules Introduction to Probability and Applied Statistics, and either Industrial Vision, Sensors and Quality Control or Engineering Design and Quality Control.

Content, progress and pedagogy of the module

Learning objectives


  • A coherent and profound understanding of approaches, tools and techniques of data science and business process improvement approaches, to be used for continuous improvement, i.e. simplification, standardization, automation of business processes in industry and service organizations.


  • Master various principles, tools and techniques to be applied in business improvement projects, as e.g. SIPOCs, process mapping, value stream mapping, KPI and PPI analysis, root cause analysis, control charts, box plots, regression analysis, 5S, value, waste, 5Rs for process redesign, flow, pull, jidoka, pokayoke, cash flow analysis, stakeholder management etc.

  • Skills in leading operations and business process improvement projects as well as kaizen activities according to Systems Thinking, PDCA or DMAIC methods.

  • Skills in leading operations and business improvement projects towards meeting deliverables and broader stakeholder objectives, also taking role of human resources into account.

  • Skills for designing and leading larger scale organizational transformations centered around lean-six sigma approaches towards sustainable practices of continuous improvement – also ability to identify enablers / barriers for success, e.g. the role that governance of IT investments play in enabling process improvement projects to succeed


  • Be able to deploy knowledge and skills in relation to business process improvement (Lean-Six Sigma, TPM, TQM) challenges of manufacturing, transportation or service organizations
  • Be able to deploy knowledge and skills in relation to larger scale organizational transformations targeting a kaizen / continuous improvement culture
  • Develop abilities to do project and stakeholder management of business improvement projects in own organization

Type of instruction

The teaching is organized in accordance with the general form of teaching. Please see the programme cirruculum §17.

Extent and expected workload

Since it is a 5 ECTS course module the expected workload is 150 hours for the student.



Name of examData Science and Process Improvement
Type of exam
Written or oral exam
Assessment7-point grading scale
Type of gradingInternal examination
Criteria of assessmentThe criteria of assessment are stated in the Examination Policies and Procedures

Facts about the module

Danish titleDatavidenskab og procesforbedring
Module codeM-MOE-B5-5
Module typeCourse
Duration1 semester
Language of instructionEnglish
Empty-place SchemeYes
Location of the lectureCampus Copenhagen
Responsible for the module


Study BoardStudy Board of Production
DepartmentDepartment of Materials and Production
FacultyFaculty of Engineering and Science