8588 modules
Page 637
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LING6088 2026-27
Pragmatics in global contexts
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BIOM3004 2028-29
Precision Health: Machine Learning Under Uncertainty
This third-year options module introduces students to the principles and analytical methods that underpin precision health, with a focus on modelling clinical risk and uncertainty using statistical and machine learning techniques. Building on foundational biomedical and computational knowledge, students will explore key health datasets, calibrated risk estimation models, and approaches for quantifying and communicating uncertainty in real-world healthcare settings. Through practical examples and state-of-the-art applications, they will learn how bias, missingness, and evidence quality affect model reliability and how uncertainty-aware methods support safe, informed decision-making in precision health. -
ARTD6910 2026-27
Precision Marketing for Smarter Campaigns
In this module, you will explore the principles and practices of marketing optimisation, focusing on how to refine strategies to maximise both effectiveness and efficiency. You will be introduced to key marketing metrics that help evaluate performance and provide insights into campaign success. Through this, you will gain knowledge and understanding of how organisations demonstrate return on marketing investment and ensure accountability in decision-making. You will explore how to use data to identify opportunities, make informed adjustments, and enhance the impact of marketing activities. By engaging with real-world tools and techniques, you will develop the skills to measure, evaluate, and optimise campaigns with precision, ensuring that every marketing effort is strategically aligned and results-driven. -
MANG1043 2025-26
Predictive Analytics I: Regression and combinatorial techniques
Predictive modelling offers a lot of benefits to organisations: it can help them to improve their business decisions and which in turn will have a huge impact on their business and its profits. As a result, there is a huge demand for persons with predictive modelling skills in various organisations across the globe. In this module, you will learn techniques to determine patterns and to make predictions about future trends from the data, including regression modelling, clustering analysis and Principal Component Analysis (PCA). -
MANG1043 2026-27
Predictive Analytics I: Regression and combinatorial techniques
Predictive modelling offers a lot of benefits to organisations: it can help them to improve their business decisions and which in turn will have a huge impact on their business and its profits. As a result, there is a huge demand for persons with predictive modelling skills in various organisations across the globe. In this module, you will learn techniques to determine patterns and to make predictions about future trends from the data, including regression modelling, clustering analysis and Principal Component Analysis (PCA). -
MANG2091 2027-28
Predictive Analytics II: Business Forecasting
This course provides part of the essential knowledge and skills required for conducting the Final Project module in the final year.
Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed. A commonplace example might be estimation of some variable of interest at some specified future date. This module gives you a thorough understanding of various statistical methods for forecasting, in particular time-series methods that have wide applications in business.
Risk and uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty attaching to forecasts, and sometimes it is necessary to provide distributional rather than point forecasts. As such, an introduction to methods for distributional forecasting will also be provided.
As forecasting often requires huge amount of data, both for training and testing the models, and the required formulae and equations are often complicated, it is essential to implement forecasting methods using a proper statistical package. As such training will be provided on using statistical software package for implementing forecasting methods. -
MANG2091 2026-27
Predictive Analytics II: Business Forecasting
This course provides part of the essential knowledge and skills required for conducting the Final Project module in the final year.
Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed. A commonplace example might be estimation of some variable of interest at some specified future date. This module gives you a thorough understanding of various statistical methods for forecasting, in particular time-series methods that have wide applications in business.
Risk and uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty attaching to forecasts, and sometimes it is necessary to provide distributional rather than point forecasts. As such, an introduction to methods for distributional forecasting will also be provided.
As forecasting often requires huge amount of data, both for training and testing the models, and the required formulae and equations are often complicated, it is essential to implement forecasting methods using a proper statistical package. As such training will be provided on using statistical software package for implementing forecasting methods. -
ECON6067 2025-26
Preliminary Mathematics and Statistics
This two week intensive course is designed to ensure that students who start postgraduate programmes in Economics will have the skills in mathematics and statistics needed for their subsequent modules. The course has two components, statistics and mathematics, and these are taught in parallel. The module concludes with a test anabling students to reflect on their preparedness for their respective programme. However, this module does not carry any credits, so that the test mark will not affect the final course mark. -
ECON6067 2026-27
Preliminary Mathematics and Statistics
This two week intensive course is designed to ensure that students who start postgraduate programmes in Economics will have the skills in mathematics and statistics needed for their subsequent modules. The course has two components, statistics and mathematics, and these are taught in parallel. The module concludes with a test anabling students to reflect on their preparedness for their respective programme. However, this module does not carry any credits, so that the test mark will not affect the final course mark. -
ECON6067 2029-30
Preliminary Mathematics and Statistics
This two week intensive course is designed to ensure that students who start postgraduate programmes in Economics will have the skills in mathematics and statistics needed for their subsequent modules. The course has two components, statistics and mathematics, and these are taught in parallel. The module concludes with a test anabling students to reflect on their preparedness for their respective programme. However, this module does not carry any credits, so that the test mark will not affect the final course mark.