DATA2500 Operating Systems Course description

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. 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.

Javaprogramming.

This course is a complete solution for learning and developing Enterprise applications, and is divided into two parts, "Software Architecture" and "Framework".

The "Framework" section focuses on learning Enterprise-oriented application development through programming in popular frameworks such as Spring MVC, Spring Boot, Hibernate / JPA (for database linking), Spring ROO (for rapid prototype development), XML and JSON (for data exchange), and Amazon EC2 (for cloud installation and software testing).

The "Software Architecture" section includes various architectural desing patterns (client-server, distributed, web architecture, etc.). It also covers how to take an idea and divide it into business requirements and produce it through architectural diagrams. This section of the topic shows how a solid architecture forms the backbone of an application.

Regular follow-up of the project work by a project 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 supervisor(s) can suggest suitable online courses in AI and data science that the students can 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.

The course can be carried out individually by agreement with the course coordinator.

Projects are selected/distributed at the start of the semester. 

The following work requirements are mandatory and must be approved in order to prepare for the exam:

• 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 course coordinator before the project can start.
• Three meeting minutes from supervisory meetings during the project period.
• An oral mid-term presentation, individual or in groups (max 5 students), 10 minutes + 5 minutes Q&A.

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

Written project report (100% of the final grade).

A written project report delivered at the of the semester, individual or in groups (max 5 students), 4000 words +/-10 %.

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.

The exam result can be appealed.

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

• 3 assignments

Grade scale A-F.

All.

Two internal examiners. External examiners are used regularly.

Emnet er ekvivalent (overlapper 10 studiepoeng) med: DATS2500, ITPE2500

Ved praktisering av 3-gangers regelen for oppmelding til eksamen teller forsøk brukt i ekvivalente emner.