Master (2-årig)HeltidDatavitenskap
Dette studieprogrammet undervises på engelsk. Se hele programbeskrivelsen på den engelske versjonen av programmet.
Søknadsfrist:
15. april
Studiestart:
Høst 2023
Antall studieplasser:
25
Learning outcomes
- Upon completion, the candidates should be able to convey and communicate engineering related problems and solutions to both specialists and non-professionals.
- Be able to contribute to innovation and entrepreneurship.
- Complete an independent project, restricted to engineering related research- or development under supervision.
Knowledge:
- Understand the engineering sciences overall role in a societal perspective, show insight into ethical requirements and respect to sustainable development, and be able to analyze ethical problems regarding engineering related work.
- Have a broad knowledgebase in mathematics, natural sciences, technology and computer technology as a foundation for understanding methods, applications, professional innovation and adaptations.
- Have deep knowledge in a defined area connected to active research, including an adequate professional understanding in using new research.
- Knowledge of computer safety, law and ethics.
Skills:
- Be able to develop overall solutions to engineering related problems, including creating solutions in a multidisciplinary context. Be able to evaluate tools for analyzing, methods, technical models, calculations and solutions independently and critically.
- Plan and bring about data collection, as well as process, analyze and interpret the data. Collect and organize data, execute a multivariate analysis of high-dimensional data, pattern recognition and machine learning, evaluate the quality of data as well as the results.
- Be able to analyze mathematical models for processes e.g. in physics, biology, technique
This Master's degree programme in Data Science is a two year programme consisting of a total of 120 ECTS credits.
The Master's degree consists of:
Programming, computer science and data mining will give you basic skills in Data Science. Linear algebra and statistics are offered as optional courses if you lack these subjects in your bachelor degree. You will complement this basis with two specialisation courses in one of the following areas: Geographical Modelling and Analysis, Geographical Database Systems, Geographical data Mining, Climate Modelling Biostatistics, Biophysics, Bioinformatics, Computational Neuroscience, Energy Physics, Building Structures, Building Performance Simulation and Economics. In your 30 ECTS master thesis, you will apply your data science competence to a specific task and hone your problem-solving skills.
The teaching is varied, but the most common form is lectures and practice lessons. In some subjects there are compulsory activities such as group work, exercises in the lab with subsequent reports, project assignments, excursions and participation in seminars.
Many courses have a final exam that determines the entire grade in the course. The exam can be both written and oral. Other subjects have a combined assessment that includes several elements, such as e.g. mid-term exam, assignment, report or term paper, and where you get a grade on each of them. When there is portfolio assessment, each individual assignment is not assessed separately, but receives a joint grade at the end, when the portfolio is delivered. As a rule, we use letter grades, but in some subjects you get pass/fail. At the end of the study, an independent work, the master's thesis, is included, which should show understanding, reflection and maturity. The assignment is defended orally.
Modern societies produce large amounts of data.Data Sciences deal with the challenges to extract information from this data by combining the disciplines informatics, mathematics, data analysis and statistics.