Module SSI3003 for 2019/0
- Overview
- Aims and Learning Outcomes
- Module Content
- Indicative Reading List
- Assessment
Undergraduate Module Descriptor
SSI3003: Data Analysis in Social Science 3
This module descriptor refers to the 2019/0 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.
| 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 advanced 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 advanced level |
How this Module is Assessed
In the tables below, you will see reference to 'ILO's. An ILO is an Intended Learning Outcome - see Aims and Learning Outcomes for details of the ILOs for this module.
Formative Assessment
A formative assessment is designed to give you feedback on your understanding of the module content but it will not count towards your mark for the module.
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Formative statistical exercises in class | 6 exercises (about 15 minutes each) | 1-6 | Peer and tutor verbal feedback |
Summative Assessment
A summative assessment counts towards your mark for the module. The table below tells you what percentage of your mark will come from which type of assessment.
| Coursework | Written exams | Practical exams |
|---|---|---|
| 100 | 0 | 0 |
...and this table provides further details on the summative assessments for this module.
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| 5 short data analysis assignments to be submitted on Github Classroom | 50 | Data analysis exercises, about 500 words each assignment | 1-6 | Written feedback provided on Github |
| Final statistical report | 50 | 2000 words | 1-6 | Written feedback |
Re-assessment
Re-assessment takes place when the summative assessment has not been completed by the original deadline, and the student has been allowed to refer or defer it to a later date (this only happens following certain criteria and is always subject to exam board approval). For obvious reasons, re-assessments cannot be the same as the original assessment and so these alternatives are set. In cases where the form of assessment is the same, the content will nevertheless be different.
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Final statistical report | Statistical report (2000 words) | 1-6 | August/September reassessment period |
| 5 short data analysis assignments | 5 short data analysis assignments | 1-6 | August/September reassessment period |
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).
P.Spector, Data Manipulation with R, Springer (2008).
A.Unwin, Graphical Data Analysis with R, CRC Press (2015).
N.Matloff, The Art of R Programming, No Starch Press (2011).
W.Chang, R Graphics Cookbook, O’Reilly (2013).
