Module Details for the Academic Year 2018/2019

POL30430 Data Analytics for Soc Sci

This module provides an overview of common statistical methods applied to the social sciences, with particular focus on political science, sociology, public policy and development. It starts with a brief recap of the basic principals of statistical analysis, then discusses how to access, manipulate, and summarize data, and then moves on to a range of different methods - regression analysis, logistic regression, dimension reduction techniques, quantitative text analysis, etc. - that are commonly used in social science empirical research or in contemporary data science applications. It reviews both long established and cutting-edge techniques.

All material is discussed using real world examples of data analysis, with both micro- and macro-level data, and the lab exercises form the basis for the continuous assessments. Rather than delving deeply into the mathematical properties of various techniques, this module focuses on the application and the types of problems where particular techniques can be applied.

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- Basic understanding of statistical analysis in the social science
- Ability to manipulate data sets to prepare for statistical analysis
- Ability to select the appropriate statistical technique for a range of different types of empirical questions
- Ability to execute a range of standard techniques
- Ability to describe, interpret, and present statistical analysis to a wider audience
- Ability to translate statistical results to substantive relevance
- Introductory level skills in data analysis in R
- Ability to organise data analysis and results 
Item Workload


Computer Aided Lab


Autonomous Student Learning




Description % of Final Grade Timing
Continuous Assessment: Assignment 1


Continuous Assessment: Assignment 2


Continuous Assessment: Assignment 3




This module is not passable by compensation

Resit Opportunities

In-semester assessment


If you fail this module, there will be a resit available in form of an 'in semester assessment'. You should register for this 'in semester assessment' at the start of the following semester. Note that it is YOUR responsibility to contact the Module Coordinator to find out what the 'in semester assessment' will be and when it will take place.

Module Requisites and Incompatibles

Equivalent Modules

Prior Learning

An introductory statistics course prior to this course is recommended but not required.
Curricular information is subject to change