Module SOCI44115: Computational Social Science

Department: Sociology

SOCI44115: Computational Social Science

Prerequisites

• None

Corequisites

• SGIA49915 Quantitative Methods and Analysis or MATH42715 Introduction to Statistics for Data Science or other R experience approved by the module convenor.

Excluded Combination of Modules

• None

Aims

• Across the social sciences the methodological landscape has significantly changed to include a new repertoire of methodologies and methods grouped under the general heading of computational social science. These methodologies are part of the ‘complexity turn’ in the social sciences. What is key is that these methods extend and blur the boundaries of conventional statistics and qualitative inquiry, primarily through a focus on cases and context. The purpose of this module is to introduce students to computational social science, including a working knowledge of several of the most widely used methods.

Content

• Historical overview of the development of the complexity sciences - More specifically a review of the ‘complexity turn’ in the social sciences
• Examination of the core links between computational social science and conventional statistics and qualitative inquiry
• Case-based complexity
• Classification and clustering
• Dynamical modelling and simulation
• Complex network analysis

Learning Outcomes

• By the end of this module students will be able to:
• understand the history of the complexity sciences and the complexity turn in the social sciences
• understand key concepts in computational social science
• apply these ideas to social inquiry
• link computational social science to other areas of inquiry, including the qualitative, historical, and statistical.

• By the end of this module students will be able to:
• have a general facility with the field’s key concepts and methodologies and methods
• demonstrate a working knowledge of how to use some of the key software packages for running the methods learned in this class.

• deal with highly complex methodological issues and communicate conclusions to specialist and non-specialist audiences
• demonstrate a high degree of self-direction in using computational social science to engage in social inquiry, particularly related to the student’s interest area
• work autonomously in planning and implementing a computational social science method.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• Lectures: allow staff to introduce designated topic areas in a systematic manner.
• Workshops: enable students to explore and evaluate some of the key methods discussed in the module working through a case study under direction.
• Directed Reading: module study guides provide students with information about core and further reading. Students are expected to read to facilitate class discussion.
• Independent Reading: provides students with the opportunities to read widely, particularly in preparation for formative and summative.
• Summative work - summative essay tests students' understanding of the major issues discussed in the module and to apply selected methods to their topic of interest.
• Formative work - The optional formative essay provides an opportunity for students to receive feedback on their proposed approach prior to completing their summative work.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

An optional formative essay. Students will provide an overview of their strategy for completing their summative. Students will receive feedback in writing or in person with the module convener.

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University