CEG2722 : Data Visualisation and Analysis (Inactive)
- Inactive for Year: 2025/26
- Module Leader(s): Professor Philip James
- Owning School: Engineering
- Teaching Location: Newcastle City Campus
- Capacity limit: 100 student places
Semesters
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 | |
Aims
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 Activities | Lecture | 12 | 1:00 | 12:00 | Follow on exercise from lectures (1 per lecture) |
| Guided Independent Study | Assessment preparation and completion | 2 | 15:00 | 30:00 | Assessment completion and report wrtiing |
| Scheduled Learning And Teaching Activities | Practical | 6 | 2:00 | 12:00 | Lecture/practical PiP |
| Guided Independent Study | Directed research and reading | 1 | 45:00 | 45:00 | Directed readings from lectures, practice with software tools, directed online tutorials |
| Scheduled Learning And Teaching Activities | Module talk | 1 | 1:00 | 1:00 | N/A |
| Total | 100:00 |
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 |
|---|---|---|---|---|
| Report | 2 | M | 40 | Report 1 Python (spatial data processing) |
| Report | 2 | M | 60 | Report 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
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- CEG2722's Timetable