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HS52001: Advanced computational social science methods and applications

[Page last updated: 15 August 2024]

Academic Year: 2024/25
Owning Department/School: Faculty of Humanities & Social Sciences (units for MRes programmes)
Credits: 10 [equivalent to 20 CATS credits]
Notional Study Hours: 200
Level: Masters UG & PG (FHEQ level 7)
Period:
Semester 2
Assessment Summary: CWPI 100%
Assessment Detail:
  • Research project (CWPI 100%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Requisites: Before taking this module you must take HS52008
Learning Outcomes: By the end of the unit students are expected to: - Competently use the R statistical programming language - Have the ability collect a range of digital data, using appropriate computational methods - Understand the functioning, assumptions and limitations of a range of advanced computational methods used in social science research - Competently choose the most appropriate methods for answering different research questions - Implement a range of advanced computational methods to a variety of data sources - Be able to visualize, present and interpret research results - Develop the ability to conduct an independent research project, going through all required steps, and following the latest academic quality standards and ethical consideration standards


Synopsis: Gain the knowledge and programmatic skills needed to conduct and present a computational social science research project. This unit complements the 'Digital Methods and Data Skills' unit, to help you develop your ability to use technological approaches. You'll cover topics like: - collecting and analysing digital data using the R statistical programming language - web-scraping and API use - text analysis, network analysis and machine learning - results visualisation and interpretation

Content:
  • Overview of the computational social science field.
  • Introduction to R, RStudio and R Markdown.
  • Research design and current standards in computational social science research planning ¿ pre-registration, ethical and legal considerations.
  • Collecting digital data ¿ introduction to web-scraping.
  • Collecting digital data using APIs.
  • Data structures. Data management and storage ¿ types of databases and data storage environments.
  • Computational data processing, cleaning and linkage across multiple sources.
  • Advanced computational text analysis: NLP, vector spaces, topic models and structural topic models, word embeddings, large language models.
  • Advanced network analysis: exponential random graph models.
  • Machine learning applied to different types of data. Supervised and unsupervised learning. Deep neural network models.
  • Visualizing, presenting and discussing results. Creating interactive displays and applications.
  • Ethical considerations.


Course availability:

HS52001 is a Must Pass Unit on the following courses:

Department of Politics, Languages and International Studies

Notes:

  • This unit catalogue is applicable for the 2024/25 academic year only. Students continuing their studies into 2025/26 and beyond should not assume that this unit will be available in future years in the format displayed here for 2024/25.
  • Courses and units are subject to change in accordance with normal University procedures.
  • Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.
  • Find out more about these and other important University terms and conditions here.