UC3DVS10 Data Visualisation
UC3DVS10 Data Visualisation
- Course description
- NQF LevelBachelor's degree (Level 6 1. Cycle)
- Area of StudyComputing
- Program of StudyApplied Data Science
- ECTS10
- CampusKristiansand, OnlinePLUS - Oslo, Online
- Course LeaderRayne Reid
Language of Instruction and assessment: English
May be offered on Campus and Online.
May be offered as a separate course.
Included in the following bachelor's degrees:
- Applied Data Science
The course aims to provide students with the theoretical and practical knowledge and skills to interpret and present the results of the analysis of large datasets using suitable formats and mediums, in appropriate contexts, and to appropriate levels of granularity. A range of example data sets, from a selection of subject domains, will be used for both illustration and practical activities.
The student has knowledge of
| K1 | historical and emerging research and developments in the field of data visualisation, specifically with regard to big data. |
|---|---|
| K2 | the principles and challenges of creating and evaluating effective data visualisations. |
| K3 | how to select, apply and critically evaluate tools and techniques for generating effective and situation-appropriate data visualisations. |
The student gain skills in
| S1 | ability to select and apply a selection of appropriate data visualisation tools, technologies and techniques to specific datasets. |
|---|---|
| S2 | ability to critically evaluate the efficacy of data visualisation tools, technologies and techniques. |
| S3 | critically evaluate and compare data visualisation scholarly matter and case studies. |
| S4 | recognise and interpret the strengths and limitations of various visualisation techniques, in order to critically select and apply them to a given problem. |
The student can demonstrate
| G1 | identifying and understanding relevant legal and ethical issues relating to visualisation of data. |
|---|---|
| G2 | the challenge of developing and communicating meaningful data visualisations. |
| G3 | critical reflection on the application of data visualisation tools and techniques. |
- Introduction to Data Visualisation
- Aspects of Data Visualisation
- Geospacial Data Visualisation
- The Art of Data Visualisation
- Teaching will be based on a hybrid-flexible approach. Instructor-led face-to-face learning is combined with online learning in a flexible course structure that gives students the option of attending sessions in the classroom, participating online, or doing both.
- All activities require active student participation in their own learning.
- Learning delivery methods and available resources will be selected to ensure constructive alignment with course content, learning outcomes and assessment criteria.
- Students will be taught using a mixture of guidance, self-study, and lecture material. Topics will be introduced in a series of weekly lectures. The guidance sessions will be directed practical exercises and reading in which students can explore topics with support from a teacher. This material will also require students to self-manage their time to ensure tasks are completed and the theory is fully understood. This will allow the students to fully engage with lectures and with their peers.
- Learning resources are available in the LMS and include, but is not limited to:
- literature and online reading material (essential and recommended)
- streams, recordings and other digital resources, where applicable
- video conferencing and communication platforms, if applicable
- tools, software and libraries, where applicable
- Students must have access to an internet connection, and suitable hardware.
- Accessing live streams and virtual laboratories requires a minimum broadband connection of 2Mbps (4Mbps recommended).
- Students working on their own laptop/computer are required to acquire appropriate communications software, a webcam, and headphones (or a similar alternative).
UC2SAT10 Statistical Analysis: Tools and Techniques, and UC3MAL10 Machine Learning, or equivalent course(s).
The reading list for this course and any additional electronic resources will be provided in the LMS.
| Activity | Duration |
|---|---|
Teacher-led activity | 24 Hour(s) |
Teacher-supported work | 48 Hour(s) |
Self-study | 178 Hour(s) |
This course has three (3) exams contributing towards the overall and final grade of the course.
All exams must be assessed as passed to receive the final Course Grade.
| Form of assessment | Grading scale | Grouping | Duration of assessment |
|---|---|---|---|
Portfolio of Work | A-F | Individual | |
Portfolio of Work | A-F | Individual | |
Presentation | A-F | Individual |