8212 modules
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SOES6025 2030-31
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. -
SOES3042 2028-29
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. -
SOES6025 2028-29
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. -
SOES6025 2027-28
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. -
SOES6025 2025-26
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. -
SOES6025 2026-27
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. -
SOES6025 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. -
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.