School of Computer Science and Statistics
Module Code ST8002 Module Name Implementing statistical methods in R ECTS Weighting[1] 5 ECTS Semester taught Semester 1 Module Coordinator/s Prof. Mimi Zhang Module Learning Outcomes On successful completion … Read more
Module Code ST8001 Module Name Introduction to statistical concepts and methods ECTS Weighting[1] 10 ECTS Semester taught Semester 1 Module Coordinator Prof. Mimi Zhang Module Learning Outcomes On successful completion … Read more
This module introduces students to the framework and methods used in real life problems related to the field of spatial analysis by applying the theoretical knowledge gathered during the module to live project work
(Semester 1; 5 ECTS) This module aims to enable students to gain an understanding of the theory and practice of Environmental Engineering in relation to air pollution.
(Semester 1; 5 ECTS) This module introduces students to a sociotechnical systems model of sustainable organisations, contextualising the organisations role between individual decision making, the services, infrastructure and policies in the society and the physical infrastructure and technological environment.
(Semester 1; 5 ECTS) This module aims to enable students to gain an understanding of what GIS is and how it can be used to gain insights into many different environmental problems.
(Semester 1; 5 ECTS) This module will cover the theory of linear regression and linear mixed models and their estimation methods, modelling repeated measures overtime; various random effect models, including single random effects, multiple random effects, and random coefficient models; and non-parametric modelling approaches, to include splines and bootstrapping.
(Semester 1, 5 ECTS) The module is designed to explore the management, delivery and inter-operability of knowledge, information and data through knowledge and data engineering.
(Semester 1, 5 ECTS) An introduction to computer graphics; problem domain and applications.
(Semester 1, 5 ECTS) Image processing, feature detection and matching, image registration, recognition
and segmentation – Motion flow and object tracking in video – Mathematics for
computer vision.