CEG8730 : Data Analysis and Geographic Information Systems
- Offered for Year: 2026/27
- Module Leader(s): Dr Craig Robson
- Lecturer: Dr Caspar Hewett, Dr Achraf Koulali Idrissi
- Owning School: Engineering
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 1 Credit Value: | 20 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Aims
This module aims to equip students from diverse backgrounds with the fundamental mathematical, statistical, computational, and spatial analysis skills required to apply data science methods for advanced study and problem-solving in civil and geospatial engineering. Covering core mathematical concepts, numerical methods, programming in Python, and GIS-based spatial analysis, the module ensures that all students acquire the prerequisites to effectively manipulate equations, and undertake data science fundamentals including data analysis, developing mathematical models, and data visualisation.
Outline Of Syllabus
The module will cover a key range of fundamental concepts which are essential for learners and will be accessible to those from a range of diverse backgrounds and prior knowledge. Core mathematical foundations will be covered such as solving equations. The key data science principles will be introduced and then followed by an overview of key numerical and statistical methods and how they are applied in data science. Technical skills including computer programming will be introduced as a fundamental skill for data science and other tasks such as statistics, data processing and for solving large complex problems. The concept of Machine Learning (ML) as well as Artificial Intelligence (AI) will also be introduced. Knowledge and skills for the handling, processing, analysis and visualisation of spatial data will also be developed as key tool for the use of data in the built and natural environments.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 13 | 3:00 | 39:00 | N/A |
| Guided Independent Study | Assessment preparation and completion | 1 | 22:00 | 22:00 | report approx/equivalent length 3000 words |
| Scheduled Learning And Teaching Activities | Lecture | 3 | 2:00 | 6:00 | N/A |
| Structured Guided Learning | Lecture materials | 1 | 24:00 | 24:00 | N/A |
| Guided Independent Study | Assessment preparation and completion | 1 | 4:00 | 4:00 | Formative outline report |
| Guided Independent Study | Assessment preparation and completion | 1 | 2:00 | 2:00 | Formative quiz |
| Guided Independent Study | Assessment preparation and completion | 1 | 10:00 | 10:00 | Portfolio - canvas/NUMBAS assessments |
| Scheduled Learning And Teaching Activities | Practical | 15 | 3:00 | 45:00 | Computer tutorials |
| Guided Independent Study | Skills practice | 10 | 2:00 | 20:00 | Skills practice |
| Guided Independent Study | Independent study | 1 | 28:00 | 28:00 | N/A |
| Total | 200:00 |
Teaching Rationale And Relationship
The teaching methods in this module are designed to accommodate students from diverse backgrounds, ensuring they acquire the necessary foundational and advanced skills in mathematical, statistical, computational, and spatial analysis. The approach integrates lectures, hands-on practicals, problem-solving sessions, and project-based learning to align with the intended learning outcomes.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Report | 1 | M | 70 | Focused on the application of maths and stats for data analysis and data visualisation. Individual submission of approximate/equivalent length of 3000 words. |
| Portfolio | 1 | M | 30 | Assessing key foundational knowledge and skills comprising of 3 CANVAS/NUMBAS Assessments |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Computer assessment | 1 | M | Formative quiz to test mathematical skills |
Assessment Rationale And Relationship
This module is assessed through coursework and quizzes, supported by a formative assessment. The assessment strategy is designed to reflect the applied nature of the module, focusing on real-world problem-solving using data analysis, programming, and geospatial methods. These components act as developmental milestones and support student confidence and competence in key technical areas. The portfolio element ensures foundational competencies are achieved in underpinning mathematical skills, while the larger report submission uses authentic assessment practice to assess skills in the application of data analysis and visualisation.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- CEG8730's Timetable