Skip to main content


CEG2722 : Data Visualisation and Analysis

  • Offered for Year: 2023/24
  • Module Leader(s): Professor Philip James
  • Owning School: Engineering
  • Teaching Location: Newcastle City Campus
  • Capacity limit: 100 student places

Your programme is made up of credits, the total differs on programme to programme.

Semester 2 Credit Value: 10
ECTS Credits: 5.0
European Credit Transfer System


This module introduces data science in the context of spatial data manipulation and processing. It develops skills in the processing, analysis and manipulation of spatial data including vector, raster, image and time series data. It builds on existing scripting skills and develops them in the spatial context. It provides an introduction to software and systems that will underpin further studies and facilitate individual research projects.

Outline Of Syllabus

This module covers:
•       Data in the spatial context
•       Vector and Raster models
•       Image manipulation
•       Using Data APIs and Time-Series processing
•       Time Series data
•       Machine Learning and AI introduction

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture121:0012:00Follow on exercise from lectures (1 per lecture)
Guided Independent StudyAssessment preparation and completion215:0030:00Assessment completion and report wrtiing
Guided Independent StudyDirected research and reading145:0045:00Directed readings from lectures, practice with software tools, directed online tutorials
Scheduled Learning And Teaching ActivitiesPractical62:0012:00Lecture/practical PiP
Scheduled Learning And Teaching ActivitiesModule talk11:001:00N/A
Teaching Rationale And Relationship

•       Students will be presented with new information and concepts through lectures.
•       Practicals will address key areas of the curriculum and develop specific skills in data handling and processing within the context of current software and tools
•       Lecture and practicals will demonstrate and utilise a range of software tools and libraries
•       Assessment preparation will allow students to apply and practise their knowledge and skills to new problems

Assessment Methods

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

Other Assessment
Description Semester When Set Percentage Comment
Report2M40Report 1 Python (spatial data processing)
Report2M60Report 2 Time Series Analysis
Assessment Rationale And Relationship

The assessment will:
•       demonstrate understanding of key spatial data structures through the access and manipulation of these data types and formats
•       assess the application of scripting within current software and tools
•       will require the mastery of a number of tools, libraries and environments for data manipulation and processing

Reading Lists