Applications of Data Science and Statistics - 2019 entry

This module will enable you to learn new Data Science and Statistical methods, and to use the techniques learnt in other modules, by working on analyses of real data examples. There will be a strong emphasis throughout on understanding the practical application of statistical and machine learning methods including clustering, data reduction, methods for handling missing data, study design and introductory methods for time series data. Theory and ideas will be developed to allow the implementation of methods in examples drawn from industry, medicine, finance, public health and environmental challenges, including climate change and air pollution.

Pre-requisites: None

The aim of this module is to practice the use of Data Science and Statistical modelling by working through a series of case studies. The case studies will be based on real-life problems and will start with a description of the setting of the problem and the intended outcomes. One of the important things in any statistical analysis is to understand the background to the problem and, for each case study, there will be a review the field in which it is set. Analyses will start with raw data that will have to be sense-checked and manipulated into a form that is suitable for the intended analyses. Deciding on the exact form of the analyses in each case will be a central focus of this module and an important aim of this module will be developing the skills to make decisions in this regard, drawing on information from the setting, the exact nature of the problem being assessed and knowledge of the techniques and methods that are available. In each case study, the results of the chosen form of analyses will be interpreted, with particular attention given to the best way of communicating the results to a variety of technical and non-technical audiences.

Activities will include problem formulation, knowledge discovery, regression modelling, machine learning and report writing and presentation. Assessment will be based on examination and practical examples using real-world data examples.

Data Science and Statistical modelling topics will be introduced through their application in a series of case studies. Case studies may change each year, but the initial selection will include:

·         Case study: modelling environmental hazards

·         Case study: clustering and segmentation of customers

·         Case study: forecasting electricity demands

·         Case study: modelling the effects of air pollution on health

·         Case study: mapping rates of disease

·         Case study: exploring physical activity data for health

Case study: using local sources of data to address local challenges

RE-ASSESSMENT NOTES –

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 re-assessment 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 Convenor. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 50%.

Reading list for this module: