Academic Study Board of the Faculty of Engineering
Teaching language: English
Censorship: Second examiner: External
Grading: 7-point grading scale
Offered in: Odense
Offered in: Autumn
Course ID: T520003101
ECTS value: 10
Date of Approval: 31-08-2018
Duration: 1 semester
Internal Course Code
Responsible study board
Date of Approval
|Kristian Severin Rasmussenemail@example.com|
|Mikkel Baun Kjærgaardfirstname.lastname@example.org|
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.
The course will consist of two parts, namely theory (lectures) and practice (a project).
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.
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 MySchedule
Number of lessons
48 - hours per semester
Examination is held
By the end of the semester
The examination is based on an overall assessment of:
- Project report
- Oral examination
Form of examination
Second examiner: External
7-point grading scale
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”.