FI2BCP275 Exam Project 2
FI2BCP275 Exam Project 2
- Course description
- Course codeFI2BCP275
- Level of study5.2
- Program of studyData Analyst 2
- Credits7.5
- Course coordinatorBertram Haskins, Alec Du Plessis
This major project reflects the competence the candidates have acquired during the academic year. The candidate must solve the assignment independently or as a group from a practical problem. The candidate is responsible for all aspects of the project. Alternative case projects will be presented by the academic staff. The course builds on competence from all courses during the academic year.
The course aims to provide candidates with the ability to make independent choices and deliver a comprehensive product from start to finish with professional standards, deadline compliance and focus on efficient workflows. The candidate must demonstrate their ability to make reasonable and efficient choices through software tools and detailed documentation and communicate with professional terminology and expressions during a project. The project work is independent, and the project scope is set with guidance from the academic staff and industry.
The candidate:
- can assess own work in relation to industry-relevant data analysis standards and quality requirements for project preparation, presentation and delivery
- is familiar with the distinctive nature and place in the society of the data analysis discipline
- has insight into own opportunities for development in planning and carry out data analysis projects in accordance with industry-relevant standards
The candidate:
- can explain vocational choices of concepts, theories and tools that are used in a data analysis project
- can reflect on own data analysis project and identify possible data errors or issues and adjust it to meet the quality requirements
- can find and refer to industry-relevant information and vocational material and assess its relevance to a data analysis project
The candidate:
- can plan and carry out data analysis projects on a given project brief alone or as part of a group and in accordance with the current ethical requirements and principles
- can exchange points of view and build relations with their peers also across disciplines, project owners, participants and other interaction designers and participate in discussions about the development of best practices within data analysis
In this course, the following teaching and learning methods can be applied, but are not limited to:
- Lecture: Educator-led presentations or activities providing knowledge, skills, or general competencies in the subject area.
- Group work: Collaborative activities where students work together to solve problems or complete tasks.
- Tutoring: One-on-one or small group sessions with an instructor for personalized guidance and support.
- Student presentations: Opportunities for students to demonstrate their understanding of course material by presenting to peers.
- Online lessons: Digital content delivered via an online learning platform.
- Guidance: Individualized advice and direction from instructors to support students in their learning journey.
- Workshops: Practical sessions focused on hands-on application of theoretical concepts or skills.
- Self-study: Independent study where students engage with course material on their own without any teacher support.
Teaching materials, reading lists, and essential resources will be shared in the learning platform and software user manuals where applicable.
| Form of assessment | Grading scale | Grouping | Duration of assessment |
|---|---|---|---|
Exam Project | Grade A-F | Group/Individual | 6 Week(s) |