Data science depends on a solid grounding in mathematics and programming. In this module, you will learn essential mathematical techniques and programming skills specific to data analysis, including how to apply the mathematical techniques you have learned as part of computational data analysis procedures. Other computational methods with direct relevance to data science and processing of large datasets will also be included, such as data analysis packages for Python. Overall this module will ensure you have the core skills and background knowledge that underpin many central topics in data science, including machine learning, statistical modelling, network analysis and computer vision.

 

Pre-requisite modules: COMM414DA Introduction to Data Science. Corequisite modules: None

 

This module is a part of the dual-qualification MSc Data Science (Professional) / Level 7 Research Scientist Apprenticeship programme. It cannot be taken as an elective by students on other programmes. 

 

The apprenticeship standard and other documentation relating to the Level 7 Research Scientist Apprenticeship can be found here: https://www.instituteforapprenticeships.org/apprenticeship-standards/research-scientist-v1-0.

 

Topics will be chosen depending on the background and experience of the student cohort, but are likely to include:

Aspects of linear algebra (e.g.): Vectors, Matrices, Linear transformations, Eigenvalues and eigenvectors, Positive definite matrices, Singular value decompositions; Principal Component Analysis; Linear Discriminant Analysis

Programming for data science in Python (e.g): Tools for handling data (Python: numpy, scipy, matplotlib, pandas), Linear algebra in code, Notebooks/markdown; Other topics may be included as appropriate to the skills and background of the student cohort.

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a reassessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

 

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to re-take some or all parts of the assessment, as decided by the Module Convener. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 50%.