Introduction to Data Science
2022/2023
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 exam | Introduction 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. |
| ECTS | 10 |
| Permitted aids | All written and all electronic aids |
| Assessment | 7-point grading scale |
| Type of grading | Internal examination |
| Criteria of assessment | The 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 title | Introduktion til datavidenskab |
| Module code | KAKDMVM2042 |
| Module type | Course |
| Duration | 1 semester |
| Semester | Spring
KA elective 2. semester |
| ECTS | 10 |
| Language of instruction | English |
| Location of the lecture | Campus Copenhagen |
| Responsible for the module |
Organisation
| Study Board | Study Board of Communication and Digital Media |
| Department | Department of Communication and Psychology |
| Faculty | Faculty of Social Sciences and Humanities |
