Undergraduate Module Descriptor

SSI2007: Data Analysis in Social Science 3

This module descriptor refers to the 2021/2 academic year.

Module Aims

The aim of this module is to introduce you to more advanced quantitative techniques for the analysis of social data. More
specifically, you will learn how to clean, transform, reshape and visualise data in R, a statistical programming language, and
tidyverse, a collection of tidyverse packages. You will also learn the fundamentals of programming in R, such as conditional
statements, loops and functions. After completing this module, you will be able to independently conduct data analysis in R.
Employers in many industries value this skill.

Intended Learning Outcomes (ILOs)
This module's assessment will evaluate your achievement of the ILOs listed here – you will see reference to these ILO numbers in the details of the assessment for this module.
On successfully completing the programme you will be able to:
Module-Specific Skills	1. clean and prepare your data for statistical analysis in R; 2. conduct statistical analysis using selected methods at the intermediate level in R;
Discipline-Specific Skills	3. apply statistical data analysis techniques to social science problems; 4. clearly explain the results of statistical analysis in substantive terms and relate them to substantive social science problems;
Personal and Key Skills	5. report the results of statistical analysis in writing in a way that would be understood by non-specialists; and 6. use general-purpose statistical software (such as R) for the analysis of social data at the intermediate level.

Module Content

Syllabus Plan

Whilst the module’s precise content will vary from year to year, it is envisaged that the syllabus will cover some of the following themes:

Data types and structures in R
Data import with readr and data.table
Data manipulation with dplyr
Data visualisation with ggplot2
Iteration
Functions
Reproducible research and effective presentation of statistical results

Learning and Teaching

This table provides an overview of how your hours of study for this module are allocated:

Scheduled Learning and Teaching Activities	Guided independent study	Placement / study abroad
22	128	0

...and this table provides a more detailed breakdown of the hours allocated to various study activities:

Category	Hours of study time	Description
Scheduled learning and teaching activity	22	11 x 2 hour lectures / computer lab sessions
Guided independent study	78	Reading and preparation for lectures and lab sessions
Guided independent study	50	Data analysis and writing of the statistical report

Online Resources

This module has online resources available via ELE (the Exeter Learning Environment).

Indicative Reading List

This reading list is indicative - i.e. it provides an idea of texts that may be useful to you on this module, but it is not considered to be a confirmed or compulsory reading list for this module.

G.Grolemund, H.Wickham. R for Data Science. O’Reilly (2017). Available at https://r4ds.had.co.nz/

W.Chang, R Graphics Cookbook, 2^nd ed., O’Reilly (2019). Available at https://r-graphics.org/