DATA3750 Applied AI and Data Science project 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.

Two internal examiners. External examiners are used regularly.

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.

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

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

Knowledge

The student:

• has general knowledge of a broad range of software architecture and frameworks
• understands how design patterns can be used in software development
• understands the cost/benefit of using software architecture and frameworks in large-scale software systems

Skills

The student is capable of:

• using techniques and a small number of frameworks that may include standard client-server architecture, web frameworks and object-relational mapping (ORM).
• working on projects and tasks both independently and in groups
• preparing documentation for frameworks and architecture

General competence

The student:

• is familiar with techniques and theories that promote good quality in software systems
• is aware of the importance of frameworks and architecture in large-scale software systems

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

• 3 group assignments
• 3 multiple choice tests

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.

None.

Grade scale A-F.

Grade scale A-F.