MSc Data Science (Social Analytics) / Course details

Year of entry: 2021

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Course unit details:
Demographic Forecasting

Unit code SOST70102
Credit rating 15
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
Statistical Foundations SOST70151 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.


Learning outcomes



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

Intellectual skills

  • Understanding and critically appraise the methods and data used to measure and forecast population change

Practical skills

  • 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

Assessment methods

[Group work] (formative)

Computer code and outputs


[Group work] Critical appraisal of one or two selected journal articles

Slides & Talk

1000 words 


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



Recommended reading

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.

Additional Materials

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.

Study hours

Scheduled activity hours
Lectures 27
Independent study hours
Independent study 123

Teaching staff

Staff member Role
Arkadiusz Wisniowski Unit coordinator

Additional notes




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