8443 modules
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MATH6027 2025-26
Design of Experiments
A well-designed experiment is an efficient way of learning about the world. Typically, an experiment may involve varying several factors and observing the value of a response at settings of combinations of values of these factors. The mathematical challenge is then to choose which settings to use in order to gain the maximum information from the resulting data.
Experiments are performed in all branches of science, engineering and industry. In recent years, traditional application areas such as agriculture, manufacturing, medicine and pharmaceutical science
have been joined by bioinformatics, genetics, drug discovery, finance and economics. Problems of increasing size and complexity from these new areas have led to the development of many new
methods for designing and analysing experiments. The aim of this module is to provide a grounding in the statistical and mathematical methods that underpin the design and analysis of experiments, before exploring a number of areas where recent and ongoing developments are taking place. Mathematical criteria for quantifying the information available from a given design will be defined and explored, and will underpin much of the material in the module. -
MATH6027 2029-30
Design of Experiments
A well-designed experiment is an efficient way of learning about the world. Typically, an experiment may involve varying several factors and observing the value of a response at settings of combinations of values of these factors. The mathematical challenge is then to choose which settings to use in order to gain the maximum information from the resulting data.
Experiments are performed in all branches of science, engineering and industry. In recent years, traditional application areas such as agriculture, manufacturing, medicine and pharmaceutical science
have been joined by bioinformatics, genetics, drug discovery, finance and economics. Problems of increasing size and complexity from these new areas have led to the development of many new
methods for designing and analysing experiments. The aim of this module is to provide a grounding in the statistical and mathematical methods that underpin the design and analysis of experiments, before exploring a number of areas where recent and ongoing developments are taking place. Mathematical criteria for quantifying the information available from a given design will be defined and explored, and will underpin much of the material in the module. -
MATH6027 2026-27
Design of Experiments
A well-designed experiment is an efficient way of learning about the world. Typically, an experiment may involve varying several factors and observing the value of a response at settings of combinations of values of these factors. The mathematical challenge is then to choose which settings to use in order to gain the maximum information from the resulting data.
Experiments are performed in all branches of science, engineering and industry. In recent years, traditional application areas such as agriculture, manufacturing, medicine and pharmaceutical science
have been joined by bioinformatics, genetics, drug discovery, finance and economics. Problems of increasing size and complexity from these new areas have led to the development of many new
methods for designing and analysing experiments. The aim of this module is to provide a grounding in the statistical and mathematical methods that underpin the design and analysis of experiments, before exploring a number of areas where recent and ongoing developments are taking place. Mathematical criteria for quantifying the information available from a given design will be defined and explored, and will underpin much of the material in the module. -
MATH6027 2028-29
Design of Experiments
A well-designed experiment is an efficient way of learning about the world. Typically, an experiment may involve varying several factors and observing the value of a response at settings of combinations of values of these factors. The mathematical challenge is then to choose which settings to use in order to gain the maximum information from the resulting data.
Experiments are performed in all branches of science, engineering and industry. In recent years, traditional application areas such as agriculture, manufacturing, medicine and pharmaceutical science
have been joined by bioinformatics, genetics, drug discovery, finance and economics. Problems of increasing size and complexity from these new areas have led to the development of many new
methods for designing and analysing experiments. The aim of this module is to provide a grounding in the statistical and mathematical methods that underpin the design and analysis of experiments, before exploring a number of areas where recent and ongoing developments are taking place. Mathematical criteria for quantifying the information available from a given design will be defined and explored, and will underpin much of the material in the module. -
MATH6027 2027-28
Design of Experiments
A well-designed experiment is an efficient way of learning about the world. Typically, an experiment may involve varying several factors and observing the value of a response at settings of combinations of values of these factors. The mathematical challenge is then to choose which settings to use in order to gain the maximum information from the resulting data.
Experiments are performed in all branches of science, engineering and industry. In recent years, traditional application areas such as agriculture, manufacturing, medicine and pharmaceutical science
have been joined by bioinformatics, genetics, drug discovery, finance and economics. Problems of increasing size and complexity from these new areas have led to the development of many new
methods for designing and analysing experiments. The aim of this module is to provide a grounding in the statistical and mathematical methods that underpin the design and analysis of experiments, before exploring a number of areas where recent and ongoing developments are taking place. Mathematical criteria for quantifying the information available from a given design will be defined and explored, and will underpin much of the material in the module. -
ARTD6320 2027-28
Design Projects: Investigate, Imagine & Intervene
This studio module introduces Environmental and Spatial Design as a critical, responsive, and creative practice. Working across physical, interior, and spatial contexts, students engage with urgent real-world challenges through a structured sequence of design projects.
Emphasis is placed on research-informed investigation, creative problem-solving, and the development of a sustainable design mindset. Students work both collaboratively and independently, building fluency in visual communication, critical analysis, and reflective practice.
The module establishes the intellectual and creative foundations for postgraduate study in Environmental and Spatial Design, positioning design as a discipline capable of meaningful response to complex social, environmental, and spatial challenges. -
ARTD6324 2027-28
Design Research and Impact Methods
This module equips you with core design research and evaluations tools. You will explore a range of design-research methods—visual, spatial, participatory, and digital—and learn how to frame research questions, apply research ethics, justify your approach, and assess the potential impacts of your work. You will develop a research question and plan to implement in the final project. -
FEEG6009 2030-31
Design Search and Optimisation (DSO) - Principles, Methods, Parameterizations and Case Studies
This module introduces students to formal design search and optimization (DSO) approaches using a mixture of lectures covering theory and practice and a series of worked case studies with student participation. -
FEEG6009 2028-29
Design Search and Optimisation (DSO) - Principles, Methods, Parameterizations and Case Studies
This module introduces students to formal design search and optimization (DSO) approaches using a mixture of lectures covering theory and practice and a series of worked case studies with student participation. -
FEEG6009 2031-32
Design Search and Optimisation (DSO) - Principles, Methods, Parameterizations and Case Studies
This module introduces students to formal design search and optimization (DSO) approaches using a mixture of lectures covering theory and practice and a series of worked case studies with student participation.