MSc Data Science (Social Analytics) / Course details
Year of entry: 2021
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Course unit details:
|Unit level||FHEQ level 7 – master's degree or fourth year of an integrated master's degree|
|Teaching period(s)||Semester 2|
|Offered by||Social Statistics|
|Available as a free choice unit?||Yes|
|Unit title||Unit code||Requirement type||Description|
|Introduction to Statistical Modelling||SOST70011||Pre-Requisite||Compulsory|
To provide the knowledge and skills required to analyse the population structure and forecast the population change, in the context of the CoViD-19 pandemic. Population change is driven by changes in mortality, fertility, and both international and internal migration. The pandemic is set to make an important contribution to the population change due to the highly selective fatality affecting persons at older ages, of male gender, and with existing comorbidities. Therefore, we derive, interpret and apply a range of demographic measures to the past and current populations at various levels of geography with a critical appraisal of their accuracy in the light of the available data sources and their quality. We focus on measures of mortality, such as life tables, multiple-decrement and cause-deleted life tables, bilinear and hierarchical models for estimating and forecasting mortality and other components of population change. We also learn and apply a cohort-component model for population forecasting that integrates all components of change. Methods are then applied to the real world data focusing on outcomes of the CoViD-19 pandemic in the UK and other countries, and critically interpreted.
Teaching and learning methods
8 x Lecture (2 hours) + 8 x Tutorial – lab session (1 hour)
Knowledge and understanding
Students should be able to
- Understand the key concepts and theories related to population change and population components
- Understand the key measures used to analyse population change
- Understanding and critically appraise the methods and data used to measure and forecast population change
- Produce a range of demographic measures using statistical techniques in R software
- Evaluate the quality of the claims by the media and statistical authorities about the population change
Transferable skills and personal qualities
- Apply the learnt methods to the real world data and other settings such as at local authorities, governments and companies that utilise population estimation and forecasting as part of their activities
[Group work] (formative)
Computer code and outputs
[Group work] Critical appraisal of one or two selected journal articles
Slides & Talk
Analysis of a given aspect of population change/producing population forecasts/assessing impact of population change of a selected country/region accompanied by software output and code (in R)
1500 of written essay text; no limit on computer code and output
Bryant, J., & Zhang, J. L. (2018). Bayesian demographic estimation and forecasting. CRC Press.
Preston, S., Heuveline, P., & Guillot, M. (2000). Demography: measuring and modeling population processes.
Castles, S., De Haas, H., & Miller, M. J. (2013). The age of migration: International population movements in the modern world. Palgrave Macmillan.
Rowland, D. T. (2003). Demographic methods and concepts. OUP Catalogue.
Bijak, J. (2010). Forecasting international migration in Europe: A Bayesian view (Vol. 24). Springer Science & Business Media.
Gerland, P., Raftery, A. E., Šev¿íková, H., Li, N., Gu, D., Spoorenberg, T., ... & Bay, G. (2014). World population stabilization unlikely this century. Science, 346(6206), 234-237.
Wi¿niowski, A., Smith, P. W., Bijak, J., Raymer, J., & Forster, J. J. (2015). Bayesian population forecasting: extending the Lee-Carter method. Demography, 52(3), 1035-1059.
|Scheduled activity hours|
|Independent study hours|
|Arkadiusz Wisniowski||Unit coordinator|