Module manager: Shira Dvir-Gvirsman
Email: S.Dvir-Gvirsman@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2026/27
This module is not approved as an Elective
How do social media platforms shape public opinion? What patterns emerge in audience engagement with news content? How can data reveal the hidden dynamics of communication in the digital age? These are the kinds of questions that drive research in media and communication—and answering them requires more than intuition. It demands the ability to collect, analyse, and interpret quantitative data. This module takes students through the full data lifecycle, from gathering and cleaning data to analysing and visualizing it, using a hands-on approach to develop practical technical skills that you can apply to real-world communication and media problems. By the end of the module, students won’t just understand the numbers, they will know how to turn them into insights that matter. These skills will not only strengthen future research projects and dissertations but also give students a competitive edge in careers where data-driven decision-making is essential. Please note this is an optional module and runs subject to enrolments. If a low number of students choose this module, then the module may not run and you may be asked to choose another module.
This module provides students with the methodological knowledge and understanding to carry out an independent data-driven research project in the field of media and communication studies. It covers both methods of data collection and visualisation.
The module aims to make students familiar with select research techniques from the field of data science and approaches that can be applied both in academic research and in professional careers in communications-related fields. Teaching will combine lectures and practical workshops to balance conceptual understanding with hands-on experience.
Early lectures introduce the foundations of quantitative research, including epistemology, research design, and the logic behind data science. Subsequent sessions focus on data collection methods, such as surveys, content analysis, and digital data scraping, with workshops providing opportunities to apply these techniques using real-world examples. Later in the module, students will learn basic statistical tools and data visualization techniques, culminating in sessions on constructing narratives through data storytelling. Throughout, seminars and workshops emphasize collaborative learning, problem-solving, and the use of digital tools to prepare students for both academic research and professional practice.
On successful completion of the module students will be able to:
Critically assess how core quantitative research methods are used in media and communication contexts, including their strengths, assumptions, and limitations.
Select and justify appropriate quantitative approaches for specific research questions within communication and media studies.
Design structured, data-driven research projects, including formulating research questions, hypotheses, and selecting relevant data collection tools.
Implement key quantitative methods and demonstrate competency in their practical application for media and communication analysis.
On successful completion of the module students will be able to:
Apply quantitative research techniques and digital tools to collect, process, analyse, and visualise data effectively.
Communicate research findings clearly and collaboratively, while managing and executing data-driven projects from proposal to presentation.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Supervision | 5 | 0.2 | 1 |
| Lecture | 8 | 1 | 8 |
| Practical | 9 | 1 | 9 |
| Private study hours | 282 | ||
| Total Contact hours | 18 | ||
| Total hours (100hr per 10 credits) | 300 | ||
Students will receive formative feedback through discussion and tasks in practical sessions and through the group supervisory sessions provided on the module’s teaching and learning schedule.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Group Project | Data collection and analysis | 50 |
| Group Project | Data visualisation | 50 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Check the module area in Minerva for your reading list
Last updated: 18/05/2026
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