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Department of Mathematics

Data Science

(MSc) Data Science

Entry for 2023

FHEQ level

This course is set at Level 7 (Masters) in the national Framework for Higher Education Qualifications.

Course learning outcomes

Acquisition of the following knowledge and understanding: (1) basic probability theory and statistics, (2) computer programming, (3) data infrastructure, (4) machine learning, (5) mathematical modelling, (6) software development also for High Performance Computing and Grid

To command intellectual skills in interpretation of phenomena into mathematical model

To command intellectual skills in development of numerical models for the analysis of large data sets

To command intellectual skills in analysis and interpretation of the current research literature in applied data science and in relevant disciplines

To develop a range of practical skills in programming project design and analysis

To develop a range of practical skills in real-life interpretation of numerical and graphical model results

To develop a range of practical skills in computer programming

To develop a range of practical skills in use and development of data analysis tools

To develop a range of transferable skills in effective oral presentation on a series of platforms

To develop a range of transferable skills scientific and technical writing skills

To develop a range of transferable skills in communication of scientific results on large data sets to experts and wider audiences

Full-time course composition

YearTermStatusModuleCreditsFHEQ level
1Autumn SemesterCoreAlgorithmic Data Science (969G5)157
  CoreData Science Research Methods (970G1)157
  OptionAdvanced Methods in Molecular Research (806C7)307
  Advanced Numerical Analysis (L.7) (852G1)157
  Applied Natural Language Processing (955G5)157
  Data Analysis Techniques (890F3)157
  Mathematics and Computational Methods for Complex Systems (817G5)157
  Measure Theory with Applications (L.7) (850G1)157
  Probability Models (L7) (973G1)157
  Programming through Python (823G5)157
 Spring SemesterCoreData Science Masters Research Proposal (806G1)157
  CoreMachine Learning (934G5)157
  CoreWider Topics in Data Science (905F3)157
  OptionAdvanced Natural Language Processing (968G5)157
  Frontiers in Particle Physics (894F3)157
  Genomics and Bioinformatics (C7120)156
  Image Processing (521H3)157
  Monte Carlo Simulations (L7) (865G1)157
  Numerical Solution of Partial Differential Equations (L.7) (845G1)157
  Particle Physics Detector Technology (880F3)157
  Statistical Inference (L.7) (867G1)157
  Wearable Technologies (867H1)157
  Web Applications and Services (944G5)157

Part-time course composition

YearTermStatusModuleCreditsFHEQ level
1Autumn SemesterCoreData Science Research Methods (970G1)157
  OptionData Analysis Techniques (890F3)157
  Mathematics and Computational Methods for Complex Systems (817G5)157
 Spring SemesterCoreWider Topics in Data Science (905F3)157
  OptionFrontiers in Particle Physics (894F3)157
  Genomics and Bioinformatics (C7120)156
  Image Processing (521H3)157
  Monte Carlo Simulations (L7) (865G1)157
  Numerical Solution of Partial Differential Equations (L.7) (845G1)157
  Particle Physics Detector Technology (880F3)157
  Statistical Inference (L.7) (867G1)157
  Wearable Technologies (867H1)157
YearTermStatusModuleCreditsFHEQ level
2Autumn SemesterCoreAlgorithmic Data Science (969G5)157
  OptionAdvanced Methods in Molecular Research (806C7)307
  Advanced Numerical Analysis (L.7) (852G1)157
  Applied Natural Language Processing (955G5)157
  Measure Theory with Applications (L.7) (850G1)157
  Probability Models (L7) (973G1)157
  Programming through Python (823G5)157
 Spring SemesterCoreData Science Masters Research Proposal (806G1)157
  CoreMachine Learning (934G5)157
  OptionAdvanced Natural Language Processing (968G5)157
  Frontiers in Particle Physics (894F3)157
  Genomics and Bioinformatics (C7120)156
  Image Processing (521H3)157
  Monte Carlo Simulations (L7) (865G1)157
  Numerical Solution of Partial Differential Equations (L.7) (845G1)157
  Particle Physics Detector Technology (880F3)157
  Statistical Inference (L.7) (867G1)157
  Wearable Technologies (867H1)157
  Web Applications and Services (944G5)157

Please note that the University will use all reasonable endeavours to deliver courses and modules in accordance with the descriptions set out here. However, the University keeps its courses and modules under review with the aim of enhancing quality. Some changes may therefore be made to the form or content of courses or modules shown as part of the normal process of curriculum management.

The University reserves the right to make changes to the contents or methods of delivery of, or to discontinue, merge or combine modules, if such action is reasonably considered necessary by the University. If there are not sufficient student numbers to make a module viable, the University reserves the right to cancel such a module. If the University withdraws or discontinues a module, it will use its reasonable endeavours to provide a suitable alternative module.