CSC3831 : Data Exploration (Inactive)
- Inactive for Year: 2019/20
- Module Leader(s): Dr Paolo Missier
- Lecturer: Professor Nick Holliman, Dr Sara Fernstad
- Owning School: Computing
- Teaching Location: Newcastle City Campus
|Semester 1 Credit Value:||20|
Students will learn and acquire skills in data exploration and visualization. By the end of the module they will be able to take raw data sets, clean them, structure them and choose suitable methods for visualizing them. They will also acquire theoretical knowledge of the underpinning descriptive statistics and the basics of human perception for cognition.
Outline Of Syllabus
• Fundamental data representations.
• Data structures and schemas that enable data analytics.
• Methods for data preparation including cleaning aggregation.
• Descriptive statistics for data sets.
• The visualization pipeline.
• Human perception and cognition.
• Visualization of numerical data and categorical data.
• Visualization of geographical data.
• Visualization of hierarchical data.
• Interactive techniques for visualization.
• What make a good visualization.
|Scheduled Learning And Teaching Activities||Lecture||22||1:00||22:00||Traditional lectures|
|Guided Independent Study||Assessment preparation and completion||22||1:00||22:00||Lecture follow-up|
|Scheduled Learning And Teaching Activities||Practical||22||2:00||44:00||Computer classroom|
|Guided Independent Study||Project work||5||1:00||5:00||Reflective report preparation|
|Guided Independent Study||Project work||40||1:00||40:00||Coursework portfolio preparation|
|Guided Independent Study||Independent study||67||1:00||67:00||Background reading|
Teaching Rationale And Relationship
The teaching methods combine traditional lectures with practical sessions so that students can explore the topics covered in both a theoretical and practical context. Lectures outline the underlying principles, algorithms and theory, while practical lab work encourages students to implement the algorithms using rea-world data, in terms of both analysing the data, and visualising the evaluation.
The format of resits will be determined by the Board of Examiners
|Case study||1||M||90||For each topic/problem students will provide a report that contributes to their portfolio of evidence (4000 words)|
|Report||1||M||10||A reflective report on the skills gained summarising the portfolio of evidence produced by the problem-based activities. (400 words)|
Assessment Rationale And Relationship
The assessment is based on case studies, using real world data, allowing students to explore practical application of the techniques and algorithms that have been learned. The reflective report offers students the opportunity to draw together the overall learning experience from the entire data analytical and visualisation pipeline.