Introduction to Social Data Science

The details
Sociology and Criminology
Colchester Campus
Undergraduate: Level 5
Thursday 03 October 2024
Friday 13 December 2024
10 May 2024


Requisites for this module



Key module for

BSC L310 Sociology with Data Science,
BSC L311 Sociology with Data Science (including Year Abroad),
BSC L312 Sociology with Data Science (including Placement Year),
BSC L313 Sociology with Data Science (Including foundation Year)

Module description

With research methods rapidly changing in response to the large-scale generation of data within society, social science needs to ensure it is engaged with new digital methods to both benefit from them and to shape them. This module is designed for first-time programmers, from social science and humanities backgrounds, and focuses on teaching foundational skills in data collection, analysis, and visualisation using the Python programming language.

Students will learn key skills such as how to gather large-scale datasets from application programming interfaces (APIs), data cleaning and exploratory data analysis, and visualisation for finding patterns and themes, in varied datasets. Students will develop an understanding of how, through these techniques, new, creative non-traditional data sources can be utilised for social science research.

Module aims

The aims of this module are:

  • To provide students with a fundamental knowledge of the Python programming language

  • To develop the ability of students to acquire data through application programming interfaces (APIs)

  • To provide students with knowledge and understanding of cleaning, managing and reporting on large datasets

  • To provide knowledge of how these techniques are utilised in research as well as in non-academic contexts

Module learning outcomes

By the end of this module, students will be expected to be able to:

  1. Have a fundamental proficiency in the Python programming language.

  2. Generate new datasets using an API.

  3. Store, query and clean large datasets of varied data.

  4. Understand different approaches to data visualisation and how they may impact interpretation.

  5. Clearly communicate method and findings both through visualisations and written reports.

  6. Understand the ethical and legal dimensions of computational social science.

  7. Situate computational techniques within broader principles of research design in the social sciences.

Module information

In this module, students will learn to combine their growing knowledge about society, social processes and research design, with powerful tools to both draw on and analyse the vast amounts and forms of new data in a way that is critical, ethical and valuable.

This module provides a practical introduction to a range of methods that utilise intensive computational processing. Students will be taught in Python, a general-purpose accessible programming language popular in data science and used across a vast range of sectors. Students are not expected to have any prior programming experience, making this a valuable opportunity to learn new research techniques, as well as a skill that is in great demand.

Indicative Topic list:

  • Topic 1 - What is computational social science? Let’s get started.

  • Topic 2 - Python Fundamentals 1: Loops, Lists and Strings, oh my! 

  • Topic 3 - Python Fundamentals 2: Functions, Errors and how to fix them.

  • Topic 4 - Pandas 1: Data Wrangling with Pandas.

  • Topic 5 - Pandas 2: Cleaning, transforming and storing your data.

  • Topic 6 - Data Visualisation 1: Plot fundamentals.

  • Topic 7 - Data Visualisation 2: How to explore data through visualisation.

  • Topic 8 - APIs 1: Pulling Data from the Guardian API.

  • Topic 9 - APIs 2: Wrangling and visualising your own data.

  • Topic 10 - Catch up and code surgery.

Learning and teaching methods

This module will be delivered via one x two-hour seminar per week which will be delivered face-to-face and attendance is expected.



Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Coding Task    30% 
Coursework   Directed Report    70% 

Exam format definitions

  • Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
  • In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
  • In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
  • In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary, for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.

Your department will provide further guidance before your exams.

Overall assessment

Coursework Exam
100% 0%


Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr James Allen-Robertson, email:
Dr James Allen-Robertson



External examiner

Dr Eongyung Kim
University of Manchester
Available via Moodle
No lecture recording information available for this module.


Further information
Sociology and Criminology

* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.

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