Bachelor's Degree Programme in Mathematical Modelling and Data Science Programme description

The plan is prepared by OsloMet - Oslo Metropolitan University in accordance with the regulations on the framework plan for engineering education, established by the Ministry of Education and Research on May 18, 2018.

The National Qualifications Framework for Higher Education, established by the Ministry of Education and Research on March 20, 2009, provides an overview of the total learning outcomes defined in knowledge, skills, and general competence that candidates are expected to have after completing their education. The learning outcome descriptions in the plan are developed in accordance with the framework plan and the qualifications framework.

The program is a three-year framework-regulated engineering education. Candidates who have completed the program in accordance with the program plan are awarded the degree Bachelor of Engineering – Mathematical Modeling and Data Science. The program's profile is characterized by the interaction between computer science, mathematics, statistics, and physics. The education aims to provide students with the competence to work on engineering problems related to natural science applications. Through three years of engineering-oriented courses, students will acquire knowledge that is essential for scientific problems in the workplace. The program is tailored to engineering premises and is research-based; research and development work form the basis for continuous development of the program's content and structure, involving both PhD candidates and students.

Students follow the same courses in the first year of study and then increasingly specialize in the second and third years. In the final semester, students complete a bachelor's thesis related to work-oriented problems.

Further studies

There are numerous further education opportunities for candidates with a bachelor's degree in engineering. Some continue towards a master's degree at OsloMet, where Applied Computer and Information Technology (ACIT) is the most relevant. The program is particularly suited for specializations in "Applied Artificial Intelligence," "Data Science," "Mathematical Modeling and Quantum Technologies" within the ACIT program. Exciting master's programs are also available at NTNU, UMB, UiO, or other Norwegian and international universities.

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.

The program is a three-year engineering education and awards the degree Bachelor in Mathematical Modeling and Data Science. Each academic year comprises 60 credits, meaning the bachelor's program has a total of 180 credits. Each course has a final exam.

In the first semester, the curriculum and the language of instruction will primarily be in Norwegian, but English literature will be increasingly used throughout the program. The fifth semester is offered in English to facilitate increased student exchange. Although the bachelor's program is mainly taught in Norwegian, there is an expectation that students have sufficiently good English skills as much relevant literature and resources are in English.

The content of the teaching in the common part of the education can be summarized as follows:

First year common courses: Foundation of natural science

• Engineering foundation
• Calculus and discrete mathematics
• Programming
• Physics and chemistry

Second year common courses: Breadth

• Linear algebra
• Multivariable calculus and differential equations
• Statistics
• Numerical mathematics

Third year: Specialization

• Quantum mechanics
• Bachelor thesis

In additional there will be elective courses in theoretical mathematics, artificial intelligence and data science, scientific computing and computer science.

The program is structured into the following course groups according to the framework plan:

• Engineering foundation: 30 credits with fundamental mathematics, engineering system thinking, and introduction to engineering practice and methods. This should mainly relate to engineering education and lay the foundation for the engineering profession.
• Program foundation: 50-70 credits with technical subjects, natural sciences, and social sciences. This should mainly relate to the study program and lay the foundation for the field of study.
• Technical specialization: 50-70 credits providing a clear direction within the respective field, building on the engineering foundation and program foundation. This should mainly relate to the study direction and lay the foundation for the field. The bachelor thesis is included in the technical specialization.

Elective courses: 20-30 credits contributing to further academic specialization, either in breadth or depth. The elective courses in the third year can provide a focus on theoretical mathematics, artificial intelligence and data science, or scientific computing. If there are too few students choosing a given elective course in a semester, the course will not be offered that semester.

Elective courses available in the 5th semester:

• MAMO3200 Simulation and Visualization
• MAMO3300 Real Analysis
• DATS2300 Algorithms and Data Structures
• DATA3800 Introduction to Data Science with Scripting
• DAVE3625 Introduction to Artificial Intelligence

Elective courses available in the 6th semester:

• DAVE3606 Resource-efficient programs
• ADSE3200 Visualization
• MAMO2500 Symmetries and Algebraic Structures
• MAMO2400 Thermodynamics and Statistical Physics

The program facilitates methods that promote the student's academic development and self-activity, encouraging both individual and group studies. The teaching methods are chosen to ensure that students achieve the learning outcomes. Each student shares responsibility for and has influence over their own study and learning situation. This involves active participation throughout the course of study, with discussions of academic questions, fostering a learning environment that encourages reflection, analysis, and critical thinking. Formative evaluation can consist of both oral and written feedback.

The methods of work and teaching will vary somewhat from course to course but will often be based on problem-based teaching and learning. Students will continuously work on problems, solve tasks, and develop various projects. Computers, tablets, mobile phones, the internet, web, and other electronic channels and devices are systematically used for learning, dissemination, guidance, development, and communication.

The main work and teaching methods used in the program are described below. The course plans indicate which are applicable in each course. The program concludes with a large, independent, and practical bachelor thesis, which is normally assigned as a project from the industry.

Teaching specifically adapted for student-active learning

The teaching is specifically adapted for student-active learning methods. Specifically, students will work on challenges related to an engineering problem, a societal need, or similar. Students are expected to find a solution by demonstrating their thought process and approach to solving the problem.

This form of learning is highlighted through various methods such as:

• Project Work: Project work is an important method, where relevant problems are connected to relevant learning objectives and solution methods. Project work varies from individual work to larger group projects.
• Workshops: A method that can promote student-active learning, creativity, and collaboration with others in a concentrated time period.
• Presentations: Some courses provide students with the opportunity to gain experience in presenting academic material and/or project results to fellow students and the course instructor.
• Guidance individually and in groups: Guidance is a way for candidates to receive specific feedback and advice on their project with specific challenges and goals. It is similar to the relationship between a master and an apprentice, where an experienced practitioner shares their knowledge.
• Discussions and reflections: Developing the ability to critically reflect on one's own and others' knowledge is important for increasing the degree of self-evaluation and understanding related to the learning outcomes.

Lectures

Lectures are organized in periods of each course. Lectures are often used to introduce a topic for further work, spark interest, summarize a theme, facilitate study work within particularly difficult areas of a topic, and present current research on a topic.

Self-study

Students are expected to acquire knowledge of topics in the curriculum that are not covered through lectures or other scheduled teaching and to further develop their knowledge through problem-solving.**

Organized group work

Students are organized into groups to, among other things, learn to solve problems together. Students collaborate, share experiences, and reflect, which directly prepares them for collaborative situations in the workplace after completing their education.**

Work-related Bachelor's thesis

The bachelor's thesis will be carried out on work-related problems and can in many ways be compared to a kind of "craft test" in the subject. Students typically work in groups and solve complex problems that connect many of the learning outcomes at both the course and program levels in a large project. The project concludes with an oral presentation to an examiner.

Engineering and technology subjects are international. Much of the curriculum literature is in English, and several systems and work tools use English as the working language. Parts of the teaching may be conducted in English. The specific courses where this applies will be indicated in the respective course plans. Thus, students gain experience with and knowledge of English terminology within engineering.

Engineering and technology studies are also designed for internationalization, allowing students to take part of their studies abroad.

The Bachelor in Mathematical Modeling and Data Science has several partners to which students can go on exchange from the fifth semester onward.

For incoming students, the program offers English-taught courses in the fifth semester.

You can also choose to write your BA project in the 6th semester by completing The European Project Semester (EPS), either at one of our partner institutions or here at OsloMet. More information about where you can complete EPS abroad can be found on the exchange website for your program: Exchange Agreements. If you wish to complete EPS at home, you can find information here: European Project Semester (EPS)

General information regarding EPS: http://europeanprojectsemester.eu/

Required coursework means compulsory assignments/activities that must be approved by a given deadline in order for students to be able to sit the exam. Coursework can be written work, project work, oral presentations, lab courses, compulsory attendance at lectures etc. Required coursework can be done individually or in groups.

Required coursework is intended to ensure the students’ progress and development and that they participate in necessary elements of the programme. Coursework requirements can also be set to ensure that students achieve a learning outcome that cannot be tested in an exam.

The number and type of coursework requirements, the rules for meeting the coursework requirements, deadlines and other details are set out in the course descriptions and teaching plans that are announced at the start of the semester.

Previously approved coursework can be valid for two years after it is approved, provided that the course has not changed.

Required coursework is assessed as ‘approved’ or ‘not approved’.

Not approved coursework

Valid absence documented by, for example, a medical certificate does not exempt students from meeting the coursework requirements. Students who have valid grounds for absence, or who have submitted coursework that is not approved, should as far as possible be given a new chance to resubmit it before the exam. This must be agreed with the lecturer in question on a case-to-case basis. If another attempt at meeting a coursework requirement is not possible because of the nature of the subject/course, the student must be prepared to meet the coursework requirement on the first possible occasion. This can result in delayed progress in the programme.

The examination regulations are specified in the Act relating to Universities and University Colleges and the Regulations relating to Studies and Examinations at OsloMet and the National Curriculum Regulations for Engineering Education. See OsloMet’s website regarding Acts and regulations.

The following forms of exam may be used in the programm:

Individual written exam

A final comprehensive exam arranged as a written test with proctors. The answers are submitted with candidate numbers, not names.

Oral exam

Oral and practical exams are assessed by two examiners, as the exams results cannot be appealed. Formal errors can nonetheless be appealed.

Portfolio exam

One overall grade is given for the portfolio. It is only possible to appeal the exam result for the portfolio assessment as a whole. Any information provided about weighting is only considered additional information in relation to the final grade. If parts of the portfolio contain elements such as an oral presentation, practical assignments etc., the exam result cannot be appealed. The rules concerning right of appeal are described in each individual course description.

Take-home exam over an extended period

A written assignment that students work on within specified time frames, usually towards the end of the semester. The topic of the assignment is provided or approved by the course coordinator. The duration of a take-home exam can range from two days to up to two weeks. Students are expected to discuss the interpretation of the assignment and their answers among themselves, even though the submission is individual.

Shorter take-home exam

In this format, generally, all aids are allowed except communication with others. To limit the candidates' opportunity for undesired collaboration with others, the most natural exam task would be a writing assignment (essay or similar).

Partial exam

A partial exam is when a course combines different exam formats, e.g., part written and part oral, or a group exam and an individual exam. It can also involve two written submissions. This can also be used when it is desirable for students to receive partial grades during the semester with different deadlines for the various parts.

If grading occurs at different times, it must be stated when students can submit complaints, which is normally after the final grade is given. For other solutions, this must be specified.

The course plan must state whether a combined grade or partial grades are given, and if so, how the partial grades are weighted. It must also specify if all parts must be passed to achieve a passing grade. For the total grade on a partial exam, the calculation is done automatically.

Exams that are only assessed by internal examiners shall be regularly selected for external assessment.

Assessment

The grades pass/fail or a grade scale with 5 grades from A to E for pass and F for fail are used for exam assessment.

Prerequisite knowledge and study progress

Prerequisite knowledge over and above the admission requirements are described in the course descriptions.

Even if no specific requirements for prior knowledge are defined, the students should take courses worth at least 50 credits each year to be able to complete the programme within the nominal length of study.

From the first to the second year of the programme – courses worth 50 credits should be completedFrom the first and second years to the third year of the programme – courses worth 100 credits should be completedStudents must be registered in the third year of the programme and have completed at least 100 credits from the first and second years of the programme by 1 October, before they can write their bachelor’s thesis.

Programme supervisor scheme

The programme supervisor scheme is part of the quality assurance of each individual study programme. A programme supervisor is not an examiner, but someone who supervises the quality of the study programmes. All study programmes at OsloMet shall be subject to supervision by a programme supervisor, but there are different ways of practising the scheme. Reference is made to the Guidelines for appointment and use of examiners at OsloMet: Retningslinjer for oppnevning og bruk av sensorer ved OsloMet

Rescheduled/resit exams

Students must register for resit/rescheduled exams themselves at StudentWeb. Resit/rescheduled exams are normally organised together early in the following semester. Resit exams are for students who have taken the exam and failed. Rescheduled exams are for students who did not take the ordinary exam. The conditions for taking resit/rescheduled exams are set out in the Regulations relating to Studies and Examinations at OsloMet.

Diploma

The final assessment for each course is included on the diploma for the Bachelor’s Degree in Mathematical modelling and data science. The title of the bachelor’s thesis will also be included on the diploma.

The purpose of OsloMet’s quality assurance system is to improve the students’ learning outcomes and development by raising quality at all levels. OsloMet wishes to cooperate with the students, and their participation in the quality assurance work is crucial. The overriding goals for the quality assurance system include:

• to ensure a high level of quality in educational activities, including practical training and the learning and study environment
• to ensure that the study programmes are relevant to the professional fields
• to ensure that the quality continues to improve

For the students, this entails, among other things, student evaluations:

• course evaluations
• annual student surveys for all of OsloMet

More information about the quality assurance system is available here: Regelverk