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POL30660

Academic Year 2024/2025

Data Analytics for Social Sciences (POL30660)

Subject:
Politics
College:
Social Sciences & Law
School:
Politics & Int Relations
Level:
3 (Degree)
Credits:
10
Module Coordinator:
Dr Thomas Daubler
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

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 principles 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.

About this Module

Learning Outcomes:

- 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

Indicative Module Content:

Data inspection and visualisation
Descriptive statistics

Linear regression
Logistic regression

Machine Learning: Regression trees

Cluster analysis
Principal component analysis

Quantitative text analysis
Topic models

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

200
Lectures

12
Computer Aided Lab

12
Total

224

Approaches to Teaching and Learning:
This course is entirely lab based, with relatively short introductory lectures to each topic, avoiding technical detail and focusing on the basic idea and interpretation of the statistical tool of that week. Preparation of the mandatory readings before each class is crucial.

Students are provided with detailed instructions on how to perform analyses during the lab, and homework assignments are based on the interpretation of the lab analyses. This allows for students to learn about a wide range of techniques, with little or no prior training in statistics or programming, and to think about how this can be relevant to provide insights into social science questions.

Requirements, Exclusions and Recommendations
Learning Recommendations:

Some familiarity with descriptive statistics and statistical modeling (e.g., regression analysis) will be helpful.


Module Requisites and Incompatibles
Equivalents:
Data Analytics for Soc Sci (POL30430)


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Group Work Assignment: Short presentation of data visualizations prepared as part of group work Week 4 Graded No
20
 No
Assignment(Including Essay): Memo describing data analyses Week 9 Graded No
40
 No
Assignment(Including Essay): Memo describing data analyses Week 14 Graded No
40
 No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Summer No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Feedback will be provided within 20 days from submission, as per university guidelines.

James, Gareth, Daniela Witten, Trevor Hastie and Robert Tibshirani. 2013. An Introduction to Statistical Learning: With applications in R. Springer.

Name Role
Dr Yoo Sun Jung Lecturer / Co-Lecturer

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 26, 29, 30, 31, 32, 33 Thurs 11:00 - 12:50
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