Data Science (January start) : 2025 entry : ... : School of Mathematical and Physical Sciences : Schools and services : �鶹ӳ��

Data Science (January start)

(MSc) Data Science (January start)

Entry for 2025

Outline
Module information

FHEQ level

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

Course learning outcomes

apply your knowledge and understanding of: (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 real-world professional contexts

transfer real-world phenomena into mathematical models

develop numerical models for the analysis of large data sets

analyse and interprete current research literature in applied data science and in relevant disciplines

design programming projects and analyse them

interpret numerical and graphical model results

use computer programming to solve real-world problems

use and develop of data analysis tools

present your results effectively on a series of platforms

communicate technical and scientific results on large data sets to experts and non-specialist audience

Full-time course composition

Year	Term	Status	Module	Credits	FHEQ level
1	Spring Semester	Core	Data Science Masters Research Proposal Jan entry (806G1JN)	15	7
		Core	Data Science Research Methods Spring (L7) (970G1S)	15	7
		Core	Machine Learning (934G5)	15	7
		Core	Wider Topics in Data Science (L7) (981G1)	15	7
		Option	Advanced Natural Language Processing (968G5)	15	7
			Image Processing (521H3)	15	7
			Machine Learning and Statistics for Health (L7) (974G1)	15	7
			Monte Carlo Simulations (L7) (865G1)	15	7
			Network Science (981G5)	15	7
			Statistical Inference (L.7) (867G1)	15	7
			Wearable Technologies (867H1)	15	7
			Web Applications and Services (944G5)	15	7
	Spring & Summer Teaching	Core	Dissertation (Data Science Jan entry) (844G1JN)	45	7
Year	Term	Status	Module	Credits	FHEQ level
2	Autumn Semester	Core	Algorithmic Data Science (969G5)	15	7
		Core	Linear Statistical Models (L7) (971G1)	15	7
		Option	Advanced Methods in Molecular Research (806C7)	30	7
			Applied Natural Language Processing (955G5)	15	7
			Applied Numerical Analysis (L.7) (852G1)	15	7
			Data Analysis Techniques (890F3)	15	7
			Programming through Python (823G5)	15	7

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.

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Department of Mathematics