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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
ECTS Credits: 10.0


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.

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture221:0022:00Traditional lectures
Guided Independent StudyAssessment preparation and completion221:0022:00Lecture follow-up
Scheduled Learning And Teaching ActivitiesPractical222:0044:00Computer classroom
Guided Independent StudyProject work51:005:00Reflective report preparation
Guided Independent StudyProject work401:0040:00Coursework portfolio preparation
Guided Independent StudyIndependent study671:0067:00Background 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.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Case study1M90For each topic/problem students will provide a report that contributes to their portfolio of evidence (4000 words)
Report1M10A 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.

Reading Lists