8251 modules
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SOES3042 2029-30
Computational Data Analysis for Ocean and Earth Scientists
The module will present a variety of different types of oceanographic, meteorological, geophysical, and remote sensing data and will explore methods for processing, analysing and modelling using Python.
This module introduces you to the essential skills in computational data analysis, specifically designed for ocean and earth scientists. As we explore a variety of methods for processing, analysing, and modelling data, you'll actively engage with Python, the leading programming language in scientific computing. Topics covered in the module include statistical distributions, correlation, hypothesis testing, regression, model selection, principal component analysis, spectrum analysis, filtering, and advanced signal processing methods. For each topic, we'll provide practical exercises designed to apply these skills to real-world scenarios, including oceanography, meteorology, climate science, geophysics, and remote sensing data, allowing for a deeper understanding how scientists leverage these methods to extract meaningful insights from data. -
ECON2040 2025-26
Computational Economics
This module will familiarise students with various computational methods and software tools used in economics and econometrics. Topics include programming, numerical simulation and optimisation, data processing and estimation. The module will provide students with a firm foundation in state-of-the-art techniques and software for each topic. The module will go through applications in economics and econometrics. -
ECON2040 2028-29
Computational Economics
This module will familiarise students with various computational methods and software tools used in economics and econometrics. Topics include programming, numerical simulation and optimisation, data processing and estimation. The module will provide students with a firm foundation in state-of-the-art techniques and software for each topic. The module will go through applications in economics and econometrics. -
ECON2040 2026-27
Computational Economics
This module will familiarise students with various computational methods and software tools used in economics and econometrics. Topics include programming, numerical simulation and optimisation, data processing and estimation. The module will provide students with a firm foundation in state-of-the-art techniques and software for each topic. The module will go through applications in economics and econometrics. -
ECON2040 2027-28
Computational Economics
This module will familiarise students with various computational methods and software tools used in economics and econometrics. Topics include programming, numerical simulation and optimisation, data processing and estimation. The module will provide students with a firm foundation in state-of-the-art techniques and software for each topic. The module will go through applications in economics and econometrics. -
MANG6576 2025-26
Computational Finance
This module provides a hands-on introduction to modern computational finance, with an emphasis on practical skills useful in the industry. It is in two parts, roughly corresponding to the “buy” and “sell” sides of the industry. Part One introduces tools of portfolio management and algorithmic trading, such as yield curve bootstrapping, mean-risk optimization, and the use of Machine Learning in trading strategies. Part Two is focused on the use of Monte Carlo simulation in risk management and valuation, including topics such as simulation, risk limits, stress testing, capital management and regulatory compliance. Although no knowledge of programming will be assumed, Python code will be used in examples and demonstrations from the outset, and students will be required to submit programs as part of the assessment. -
MANG6576 2026-27
Computational Finance with Python
This module provides a hands-on introduction to modern computational finance, with an emphasis on practical skills useful in the industry. It is in two parts, roughly corresponding to the “buy” and “sell” sides of the industry. Part One introduces tools of portfolio management and algorithmic trading, such as yield curve bootstrapping, mean-risk optimization, and the use of Machine Learning in trading strategies. Part Two is focused on the use of Monte Carlo simulation in risk management and valuation, including topics such as simulation, risk limits, stress testing, capital management and regulatory compliance. Although no knowledge of programming will be assumed, Python code will be used in examples and demonstrations from the outset, and students will be required to submit programs as part of the assessment. -
MATH6184 2025-26
Computational Machine Learning and Optimisation
This module will introduce you to some of the main approaches used for data analysis and machine learning. Students will gain knowledge and understanding of different computational machine learning methods, and gain skills in applying them to analyse data, make predictions, and evaluate performance.
The main tools to train and tune machine learning models stem from the area of nonlinear programming. Nonlinear programming is also used in a variety of applications, ranging from machine learning and data science to finance and engineering. This course provides an introduction to nonlinear programming and covers modelling techniques, solution algorithms, and their application in machine learning. -
MATH6184 2026-27
Computational Machine Learning and Optimisation
This module will introduce you to some of the main approaches used for data analysis and machine learning. Students will gain knowledge and understanding of different computational machine learning methods, and gain skills in applying them to analyse data, make predictions, and evaluate performance.
The main tools to train and tune machine learning models stem from the area of nonlinear programming. Nonlinear programming is also used in a variety of applications, ranging from machine learning and data science to finance and engineering. This course provides an introduction to nonlinear programming and covers modelling techniques, solution algorithms, and their application in machine learning. -
MATH6184 2027-28
Computational Machine Learning and Optimisation
This module will introduce you to some of the main approaches used for data analysis and machine learning. Students will gain knowledge and understanding of different computational machine learning methods, and gain skills in applying them to analyse data, make predictions, and evaluate performance.
The main tools to train and tune machine learning models stem from the area of nonlinear programming. Nonlinear programming is also used in a variety of applications, ranging from machine learning and data science to finance and engineering. This course provides an introduction to nonlinear programming and covers modelling techniques, solution algorithms, and their application in machine learning.