Data Science
Academic Study Board of the Faculty of Engineering
Teaching language: English
EKA: T520003102
Censorship: Second examiner: External
Grading: 7-point grading scale
Offered in: Odense
Offered in: Autumn
Level: Master
Course ID: T520003101
ECTS value: 10
Date of Approval: 31-08-2018
Duration: 1 semester
Version: Archive
Course ID
Course Title
ECTS value
10
Internal Course Code
Responsible study board
Administrative Unit
Date of Approval
Course Responsible
| Name | Department | |
|---|---|---|
| Kristian Severin Rasmussen | krsr@tek.sdu.dk | |
| Mikkel Baun Kjærgaard | mbkj@mmmi.sdu.dk | |
| Sofie Birch | sbirch@tek.sdu.dk |
Teachers
Programme Secretary
Offered in
Level
Offered in
Duration
Mandatory prerequisites
Learning objectives - Knowledge
- Explain different methods and algorithms regarding data collection, data processing, data analysis, and data visualisation.
Learning objectives - Skills
- Implement and apply different methods and algorithms as stated above.
Learning objectives - Competences
- Identify relevant problems within the subject area of the module.
- Assess, select and apply methods within the subject area of the module.
- Explain and discuss different methods within the subject area of the module.
- Provide a proper documentation regarding the findings of the project.
- Communicate findings in a clear and understandable manner.
Content
The course will consist of two parts, namely theory (lectures) and practice (a project).
Lectures:
Data science is the extraction of knowledge from data. In particular it focuses on the collection, filtering, processing, creation and distribution of data. Dramatic growth in the scale and complexity of data that can be collected and analysed (Big Data) is affecting all aspects of work and society. This implies that development of effective and ethical ways of using vast amounts of data is a significant challenge to science and to society as a whole. Therefore, the main focus of this course will be on techniques and methods for data analysis and decision making, which requires interdisciplinary research in many areas, including databases, data processing, machine learning algorithms, statistics, and data visualization.
Project:
Broader understanding of the theory and practice of above stated algorithms and methods as well as a deep understanding of several selected methods.
URL for Skemaplan
Number of lessons
48 hours per semester
Teaching language
Examination regulations
Exam regulations
Name
Exam regulations
Examination is held
By the end of the semester
Tests
Exam
EKA
T520003102
Name
Exam
Description
The examination is based on an overall assessment of:
- Project report
- Oral examination
Form of examination
Combined test
Censorship
Second examiner: External
Grading
7-point grading scale
Language
English
ECTS value
10
Additional information
The module covers the key areas described above. The focus in the module is currently the specialized key areas “Data Mining” or “Decision Support”. Possible future focus areas are "Decision Support", “Big Data” and “Modeling, simulation and performance evaluations”.