Semester Project

2022/2023

Content, progress and pedagogy of the module

This module aims at providing the student with an opportunity to apply a set of data science methods – a combination of techniques covered in the first two semester of the programme as well as other relevant analytical approaches – to a well defined business problem. Students are expected to consider perspectives on data driven business development, ethics & compliance as well as data engineering and model deployment.

Empirical semester project within business data science and ML deployment in collaboration with an external organisation (external partner collaboration is not required but highly recommended, and supported). The project departs from a real-life empirical problem and uses a suitable combination of methods and frameworks covered throughout the first two semesters to address it. If possible, the analysis is based on real data provided by the collaborating institution, possibly combined with other sources. Projects have to consider issues related to data engineering, deployment and other production-critical aspects.

After completion of the module, students are able to define an appropriate problem formulation within business data science, identify a sophisticated data collection and analysis strategy, carry out the analysis and present their results using state-of-the-art data science approaches, as well as critically self-evaluate their findings. They can select the most suitable among the wide range of methods presented within  previous modules and autonomously apply it to their specific problem.

Learning objectives

Knowledge

The objective is that the student after the module possesses the necessary knowledge on:

  • define relevant real-world empirical problems within data driven business development and ML deployment.
  • central methodological considerations within the study of data science considering ontological and epistemological positions.
  • central theoretical and practical challenges, potentials and limitations within the study of data science.
 

Skills

The objective is that the student after the module possesses the necessary skills in:

  • developing and carrying out a business data science project from data collection to ML deployment on a proof-of-concept level 
  • reflecting on the robustness / limitations / ethical, legal, social consequences regarding the analysis and results.
  • presenting and discussing results written and orally at an appropriate academic level.

Competences

 

The objective is that the student after the module possesses the necessary competences in:

  • initialising, controling and completing problem-oriented business data science project work.
  • coordinating own resources for the solution of domain-specific related problems.
  • taking responsibility for own professional learning and development.

Type of instruction

For information see § 17.

Exam

Exams

Name of examSemester Project
Type of exam
Oral exam based on a project
Group examination with max. 4 students.
ECTS10
Assessment7-point grading scale
Type of gradingExternal examination
Criteria of assessmentThe criteria of assessment are stated in the Examination Policies and Procedures

Facts about the module

Danish titleSemesterprojekt
Module codeKADAT20227
Module typeProject
Duration1 semester
SemesterSpring
ECTS10
Language of instructionEnglish
Location of the lectureCampus Aalborg
Responsible for the module

Organisation

Study BoardStudy Board of Economics and Business Administration
DepartmentAAU Business School
FacultyFaculty of Social Sciences and Humanities