FI2BCP175 Semester Project 2
FI2BCP175 Semester Project 2
- Course description
- Course codeFI2BCP175
- Level of study5.2
- Program of studyData Analyst 2
- Credits7.5
- Course coordinatorBertram Haskins, Alec Du Plessis
The semester concludes with a graded project where the candidate must demonstrate practical skills and competence from courses in the first semester. Candidates work independently or in a group on a project, which must be planned, documented and executed according to project criteria. The aim is to carry out a practical project of elements from the previous courses. The candidate also prepares a project plan and an individual reflection report documenting the process and choices made along the way.
The project challenges candidates to use and combine accumulated competence from the first semester and showcase how they can complete larger projects, either individually or across disciplinary boundaries. Candidates are challenged to think and work holistically, providing a solid platform for further learning and understanding the data analysis field.
The candidate:
- can assess own knowledge of the data analysis paradigm through the subjects and topics in the first semester
- has insight into own opportunities for development in programming with Python, programmatic data analysis, databases and cloud-based services
The candidate:
- can explain vocational choices to solve practical and real-world problems and tasks with data analysis
- can find and refer to information and vocational material that is relevant to the given data analysis project
The candidate:
- can plan and carry out data design processes, data models, and applicable techniques based on the given project brief alone or as part of a group and in accordance with ethical principles that apply in appropriately sourced, stored, and used data
- can exchange points of view with their peers and other data specialists such as system architects, data scientists, data engineers, and business intelligence agents and participate in discussions about the development of good data analysis practice
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 |
|---|---|---|---|
Semester Project | Grade A-F | Group/Individual | 4 Week(s) |