FM1AZDV05 Data Visualisation
FM1AZDV05 Data Visualisation
- Course description
- Course codeFM1AZDV05
- Level of study5.1
- Program of studyApplied Machine Learning
- Credits5
- Course coordinatorLeon Grobbelaar
The course provides knowledge of the strengths and weaknesses of concepts and processes within big data visualisation. Candidates are provided with skills with tools and techniques for different data types. The course also provides practical skills in reviewing, selecting and the use of data visualisations techniques for datasets. Candidates are provided with an understanding of aspects of design principles and aesthetics.
This course will provide students with the practical knowledge and skills to interpret and present the results of the machine learning analysis of large datasets using suitable formats and mediums, in appropriate contexts, and to appropriate levels of granularity.
The candidate:
- has knowledge of concepts, processes and tools that are used for generating effective and situation-appropriate data visualisations
- can update his/her knowledge about data visualisations
- understands the importance of the design principles for creating and evaluating effective data visualisations
The candidate:
- can apply knowledge of data sets and data visualisation to select appropriate data for visualisation
- masters relevant tools and techniques to visualise data from datasets
- can study data sets and identify strengths and limitations and what measures need to be implemented
The candidate:
- can carry out a data visualisation of large data sets
- can develop and communicate data visualisations
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.
| Form of assessment | Grading scale | Grouping | Duration of assessment |
|---|---|---|---|
Course Assignment | Pass / Fail | Group/Individual | 3 Week(s) |