Introduction to Theatre Directing Programme description

The target group for the program consists of applicants with a background in natural sciences who wish to pursue higher education in an engineering field. Applicants without a natural science background can apply for the university's preparatory course for engineering or the three-semester scheme to qualify for further engineering education. See the university's website: www.oslomet.no

This program is an interdisciplinary education that connects mathematical analysis, numerical and discrete mathematics, physics, statistics, and data-driven methods. Students who apply to this program should be motivated to work with these topics and understand how they are interconnected to solve complex engineering problems from the workplace. This is an ambitious program with a significant amount of mathematics throughout the study, which will refine a mathematical approach to problem-solving that can be applied both within and outside mathematical frameworks.

The Higher Education Entrance Qualification/prior learning and work experience, Mathematics (R1+R2) and Physics 1. An introductory course or qualifications from a technical college under previous regimes are sufficient to meet the qualification requirements. Applicants with qualifications from a technical college pursuant to the Act relating to Tertiary Vocational Education (2003) only need to take Mathematics R1+R2 and Physics 1.

Reference is made to the Regulations concerning Admission to Higher Education: https://lovdata.no/dokument/LTI/forskrift/2007-01-31-173

After completing and passing the three-year bachelor’s degree programme in Mathematical Modeling and Data Science, the candidate is expected to have achieved the following overall learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The candidate:

• has broad knowledge that provides a holistic system perspective on engineering in general, with a specialization in mathematical modeling and data science. Key knowledge includes mathematical problem-solving, understanding of physical principles, and the development and use of scientific software.
• has fundamental knowledge in mathematics, natural sciences, relevant social sciences and economics, and how these can be applied in engineering problem-solving.
• has knowledge of the history of technology, technological development, the engineer's role in society, relevant legislation related to the use of mathematical modeling and data science,forklare and the various consequences of using the technology.
• is familiar with research and development work in mathematical modeling and data science, as well as relevant methods and practices within engineering.
• can update their knowledge in the field through information retrieval and contact with professional environments and practices.

Skills

The candidate:

• can apply knowledge and relevant results from research and development work to solve theoretical, technical, and practical problems in mathematical modeling and data science, and justify their choices.
• has knowledge of software and programming languages relevant to mathematical modeling and data science and has broad engineering digital competence.
• can use relevant programming languages to solve scientific problems.
• can work in digital laboratories and master methods and tools as a basis for reproducible, targeted, and innovative work.
• can identify, plan, and carry out engineering projects, tasks, experiments, and tests both independently and in teams.
• can find, evaluate, use, and reference information and academic material and present it in a way that elucidates a problem.
• can contribute to innovation and entrepreneurship through participation in the development and realization of sustainable and socially beneficial products, systems, and solutions.

General competence

The candidate:

• has insight into the environmental, health-related, social, and economic consequences of using mathematical modeling and data science.
• can place the results of mathematical modeling and data science in an ethical and life-cycle perspective.
• can identify safety, vulnerability, privacy, and data security aspects in products and systems that use ICT.
• can communicate engineering knowledge to different target groups both in writing and orally and can help highlight the significance and consequences of technology.
• can reflect on their own professional practice, also in teams and in an interdisciplinary context, and can adapt it to the current work situation.
• can contribute to the development of good practice by participating in professional discussions within mathematical modeling and data science and share their knowledge and experiences with others.
• has information competence; understands why quality-assured knowledge sources should be sought, why sources should be referenced, and is aware of what is defined as plagiarism and cheating in student work.
• can update their knowledge through literature studies, information searches, contact with professional environments and user groups, and through experience.