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Department of Mathematics

Data Science

(BSc) Data Science

Entry for 2025

FHEQ level

This course is set at Level 6 in the national Framework for Higher Education Qualifications.

Course learning outcomes

Cognitive Skills: demonstrate systematic knowledge and understanding of a core of probability theory and statistics, basic mathematics and mathematical modelling; demonstrate knowledge and understanding of some choice of advanced topics.

Cognitive Skills: demonstrate systematic knowledge of a core of data science and machine learning.

Cognitive Skills: demonstrate systematic awareness of ethical and professional responsibilities underpinning decisions and practice.

Practical Skills: demonstrate competence in the use of mathematical methods and techniques in problem solving and modelling, explore, and where feasible solve, mathematical problems, by selecting appropriate techniques and demonstrate knowledge and understanding of the process of mathematical or statistical modelling.

Practical Skills: demonstrate ability to collect, process and analyse large data sets as well as competence in interpreting the results, showing awareness of the importance of statistical assumptions.

Practical Skills: be proficient in computer programming and the use of one or more mathematical and statistical computer packages.

Transferable Skills: take decisions in complex and unpredictable contexts.

Transferable Skills: communicate scientific information orally and in writing.

Transferable Skills: take responsibility for their own learning and manage time appropriately.

Full-time course composition

YearTermStatusModuleCreditsFHEQ level
1Autumn SemesterCoreDiscrete Mathematics (G5136)154
  CoreEngineering Maths 1A (H1033)154
  CoreLinear Algebra 1 (G5134)154
  CoreProgramming Concepts (G6007)154
 Spring SemesterCoreData Structures & Algorithms (G5117)154
  CoreEngineering Maths 1B (H1034)154
  CoreIntroduction to Computer Systems (G6008)154
  CoreLinear Algebra 2 (G5138)154
YearTermStatusModuleCreditsFHEQ level
2Autumn SemesterCoreDatabases (G6031)155
  CoreIntroduction to Probability (G5143)155
  CoreProgram Analysis (G6017)155
  CoreScientific Computing (F3212)155
 Spring SemesterCoreApplied Machine Learning (G6061)155
  CoreProbability and Statistics (G5146)155
  CoreSoftware Engineering (G6046)155
  OptionNumerical Analysis (G5147)155
  Professional and Managerial Skills (H1041)155
YearTermStatusModuleCreditsFHEQ level
3Autumn SemesterCoreData Science Research Methods Autumn (L6) (G5222)156
  CoreLinear Statistical Models (L6) (G1107)156
  OptionApplied Numerical Analysis (L.6) (G1110)156
  Comparative Programming (G6021)156
  Computational Imaging Methods (G6087)156
  E-Business and E-Commerce Systems (G5075)156
  Introduction to Computer Security (G6077)156
  Probability Models (L6) (G1100)156
 Autumn & Spring TeachingCoreDissertation (BSc Data Science &/w IPY) (G5260)306
 Spring SemesterCoreNeural Networks (G5015)156
  CoreWider Topics in Data Science (L6) (G5223)156
  OptionLimits of Computation (G5029)156
  Machine Learning and Statistics for Health (L6) (G5221)156
  Monte Carlo Simulations (L6) (G5220)156
  Statistical Inference (L.6) (G5216)156

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.