DATA3750 Applied AI and Data Science project Course description

Course name in Norwegian
Anvendt kunstig intelligens og data science prosjekt
Study programme
Bachelorstudium i ingeniørfag - data / Bachelorstudium i informasjonsteknologi / Bachelorstudium i anvendt datateknologi
Year of study
Course history


In this course, students will acquire an understanding of some of the most important principles of data science and cognitive technologies through project work and online resources. The students will be introduced to fundamental principles of machine learning, data science and artificial intelligence. The main focus will be on how to use these principles to solve industrial tasks by using open-source or other data science platforms (for instance IBM Watson). The goal is to provide the students with an introduction to machine learning, data science and artificial intelligence using online resources at the same time as the students solve an industrial problem in the form of a comprehensive project work.

In addition to the projects on offer, students can find their own projects within a relevant company, public organization or nonprofit. In this case, it is the student's responsibility to find a supervisor for the project within the external organization. All student-initiated projects must be approved by a supervisor at OsloMet before the start of the project. 

The workload for the project should correspond to two days a week over a twelve-week period during either the Spring or Autumn semester. If the project is completed in the summer, the workload should equal four days a week over a six-week period.

The elective course will only run if a sufficient number of students a registered.

Recommended preliminary courses

The course builds on DAPE1400 Programming and DAPE2000 Mathematics with statistics. Students that do not have a basic knowledge of programming and statistics must be prepared to make considerable individual efforts to acquire such knowledge.

Required preliminary courses

No requirements over and above the admission requirements.

Learning outcomes

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and competence:


The student:

  • has a basic technological understanding of the most important concepts in machine learning, data science and artificial intelligence
  • has knowledge of the most important methods in machine learning, data science and artificial intelligence
  • has knowledge of platforms that can be used to complete major data science projects (for instance IBM Watson’s cloud services)


The student:

  • masters basic data science tools and can extract and visualise information from large quantities of data
  • understands the workflow in bigger data science, artificial intelligence or machine learning projects
  • is capable of using open-source and commercial tools that are used in industrial projects in the fields of data science, machine learning or artificial intelligence

General competence

The student:

  • masters methods and tools used to develop and carry out projects in data science, machine learning or artificial intelligence
  • is familiar with the different methods that are used to find the right tools to carry out data science projects
  • has an overview of how to visualise and manipulate data and how to develop predictive methods for solving industry problems and other issues relevant to working life

Teaching and learning methods

Project work is the principal work method used in this course, either individual or in groups of up to five students. The students are given access to relevant online resources, and will receive supervision from an internal and/or external supervisor. The students will work in groups of three to five students to complete a project in data science, machine learning or artificial intelligence in cooperation with relevant external parties such as companies or public organisations. The course can be carried out individually by agreement with the course coordinator.

The projects are chosen/assigned at the start of the semester. 

The supervisor will suggest suitable online courses in AI and data science that the students should take during the first few weeks of the course. The students are also encouraged to take other courses (https://cognitiveclass.ai) that will be useful in order to carry out the chosen project assignment. These courses may, among other things, deal with the following areas: Blockchain, the Internet of Things, Chat Bots, advanced use of data science, etc.

These courses should be completed before the students start working on their respective projects.

Course requirements

The following coursework is compulsory and must be approved before the student can take the exam:

  • The course starts with a compulsory Orientation Meeting.
  • A project outline that describes how the group will organise their work on the project.
  • A standard learning agreement must be entered into between the project provider/supervisor and the student(s), and this must be approved by the internal supervisor before the project can begin.
  • Three minutes of meetings from the supervision meetings held during the project period.

The deadline for submitting the project outline and the minutes of the meetings will be presented in the teaching plan, which is made available at the beginning of the semester.


A portfolio exam consisting of:

1. A written project report, individual or in groups (max 5 students), 3000 words +/-10%

2. An oral presentation, individual or in groups (max 5 students), 10 minutes + 5 minutes Q&A

The exam result cannot be appealed.

The portfolio is assessed as a whole and given a single grade, but both the project report and the oral presentation must be passed in order for the portfolio to receive a grade E or higher. 

For group projects, all members of the group receive the same grade. Under exceptional circumstances, individual grades can be assigned at the discretion of the project supervisor(s) and Head of Studies. 

Permitted exam materials and equipment

All aids are permitted, as long as the rules for source referencing are complied with.

Grading scale

Grade scale A-F.


Two internal examiners. External examiners are used regularly.