Introduction to Data Science

2021/2022

Content, progress and pedagogy of the module

This module provides students with a basic introduction to data science with the aim of approaching design problems from a data-driven perspective. Students will be introduced to concepts and principles behind relevant data science methods, such as testing research hypotheses, machine learning, analyzing social and textual data, and information access (search, recommendation & personalization). Students are expected to apply this knowledge to data-driven design problems. 

Learning objectives

Knowledge

Through the module, the student must gain knowledge and understanding of:

  • relevant data-driven methods for testing research hypotheses 

  • and understanding of basic principles behind supervised and unsupervised machine learning 

  • principles behind methods & techniques to derive insights for large amounts of textual data 

  • and understanding of the basic concepts & principles behind analyzing network data 

  • principles behind methods & algorithms for information access, such as search, recommendation & personalization 

Skills

The student must through the module acquire skills in:

  • identifying and using data science methods and techniques with the purpose of informing data-driven design decisions 

Competences

The student must through the module acquire competences for:

  • critically reflect on which data science methods and techniques are most relevant to solve design problems in a data-driven manner 

  • independently take responsibility for their own learning, development and specialization within data-driven design 

Type of instruction

Reference is made to §17

Exam

Exams

Name of examIntroduction to Data Science
Type of exam
Written exam
The exam takes form of a given 7-day homework assignment, where the student, based on the module, answers the question(s) within the subject area. The assignment must not exceed 15 pages and must be prepared individually.

The assignment is assessed by the examiner and an internal co-assessor.
ECTS10
Permitted aids
All written and all electronic aids
Assessment7-point grading scale
Type of gradingInternal examination
Criteria of assessmentThe criteria of assessment are stated in the Examination Policies and Procedures

Additional information

Electives are updated on our website:

https://www.kdm.aau.dk/studiehaandbog/uddannelsen/kandidat/valgfag/

Facts about the module

Danish titleIntroduktion til datavidenskab
Module codeKAKDMVM2042
Module typeCourse
Duration1 semester
SemesterSpring
KA elective 2. semester
ECTS10
Language of instructionEnglish
Location of the lectureCampus Copenhagen
Responsible for the module

Organisation

Study BoardStudy Board of Communication and Digital Media
DepartmentDepartment of Communication and Psychology
FacultyThe Faculty of Humanities