8251 modules
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COMP3212 2029-30
Computational Biology
Modern biology poses many challenging problems for the computer scientists. Rapid growth in instrumentation, and our ability to archive and distribute vast amounts of data, has significantly changed the way we attempt to understand cellular function, and the way we seek to treat complex diseases. Data from biology comes in various forms: nucleotide and amino-acid sequences, macromolecular structures, measurements from high-throughput experiments and curated literature in the form of publications and functional annotations. It is nowadays widely acknowledged that computational modelling will play a key role in extracting useful information from vast amounts of such diverse types of data. The computational challenges faced by the human genome project and Alan Turing’s contribution to morphogenesis are classic examples of such roles. -
COMP3212 2030-31
Computational Biology
Modern biology poses many challenging problems for the computer scientists. Rapid growth in instrumentation, and our ability to archive and distribute vast amounts of data, has significantly changed the way we attempt to understand cellular function, and the way we seek to treat complex diseases. Data from biology comes in various forms: nucleotide and amino-acid sequences, macromolecular structures, measurements from high-throughput experiments and curated literature in the form of publications and functional annotations. It is nowadays widely acknowledged that computational modelling will play a key role in extracting useful information from vast amounts of such diverse types of data. The computational challenges faced by the human genome project and Alan Turing’s contribution to morphogenesis are classic examples of such roles. -
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. -
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. -
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 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.