Programplaner og emneplaner - Student
DATA3750 Applied AI and Data Science project Course description
- Course name in Norwegian
- Anvendt kunstig intelligens og data science prosjekt
- Study programme
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Bachelor in Applied Computer TechnologyBachelor's Degree Programme in Software EngineeringBachelor's Degree Programme in Information Technology
- Weight
- 10.0 ECTS
- Year of study
- 2022/2023
- Curriculum
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FALL 2022
SPRING 2023
- Schedule
- Programme description
- Course history
-
Introduction
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.
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:
Knowledge
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)
Skills
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
Grade scale A-F.
Course requirements
Trine Krigsvoll Haagensen
Assessment
No formal requirements over and above the admission requirements.
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.
Examiners
Two internal examiners. External examiners are used regularly.