Data in Culture and Society

DIGI65522 Data in Culture and Society 30 Semester 2 Postgraduate Taught Level 6 Luca Scholz Unit coordinator Giulia Grisot Unit coordinator School of Arts, Languages and Cultures 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 15.0 Data in Culture and Society examines how data shapes contemporary cultural life and how cultural researchers can critically and creatively work with data. In a world where data infrastructures underpin everything from political communication to entertainment platforms, it is essential to understand not only what data represents, but also how it is produced, processed, and mobilised in social and cultural contexts. This course combines critical inquiry with hands-on methodological training. Students explore the cultural, ethical, and political dimensions of datafication, studying how data is collected, structured, classified, and circulated, and how these processes reflect wider social values, power dynamics, and forms of knowledge. Alongside this conceptual foundation, students engage in weekly coding labs where they learn practical techniques for working with cultural datasets, including data cleaning, text analysis, classification, visualisation, and basic computational modelling. By moving between theory and practice, the course enables students to understand how methodological decisions - about formats, categories, tools, and models - fundamentally shape how data can be interpreted. Students learn to design small-scale analytical projects, critically evaluate computational outputs, and situate their findings within broader cultural debates. No prior technical experience is required. All technical skills will be introduced from first principles and developed incrementally throughout the course.  Data in Culture and Society examines how data shapes contemporary cultural life and how cultural researchers can critically and creatively work with data. In a world where data infrastructures underpin everything from political communication to entertainment platforms, it is essential to understand not only what data represents, but also how it is produced, processed, and mobilised in social and cultural contexts. This course combines critical inquiry with hands-on methodological training. Students explore the cultural, ethical, and political dimensions of datafication, studying how data is collected, structured, classified, and circulated, and how these processes reflect wider social values, power dynamics, and forms of knowledge. Alongside this conceptual foundation, students engage in weekly coding labs where they learn practical techniques for working with cultural datasets, including data cleaning, text analysis, classification, visualisation, and basic computational modelling. By moving between theory and practice, the course enables students to understand how methodological decisions - about formats, categories, tools, and models - fundamentally shape how data can be interpreted. Students learn to design small-scale analytical projects, critically evaluate computational outputs, and situate their findings within broader cultural debates. No prior technical experience is required. All technical skills will be introduced from first principles and developed incrementally throughout the course.  <ul><li>Develop a critical understanding of how data is produced, structured, and circulated, and how social, cultural, and political forces shape what becomes visible or invisible through data.</li><li>Enable students to work with data as both an academic and civic practice, fostering the confidence to question data-driven claims, challenge algorithmic assumptions, and participate actively in contemporary data cultures.</li><li>Build practical competence in data analysis within the humanities, introducing students to core techniques for collecting, cleaning, transforming, analysing, and visualising cultural datasets.</li><li>Cultivate reflective and responsible data practitioners, able to evaluate the methodological choices behind data creation and analysis, and to understand the ethical implications of computational approaches.</li><li>Support students in designing and conducting their own small-scale data projects, integrating conceptual insights with hands-on coding skills and reproducible workflows.</li><li>Enhance student employability by developing a combined technical, analytical, and interpretive skillset, preparing them for roles in cultural institutions, media, research, policy, creative industries, and any domain requiring critical engagement with data.</li></ul> On successful completion of the unit, it is expected that you will be able to: · <ul><li>Understand and explain the role that data plays in a range of societal and cultural contexts</li><li>Evaluate humanities and social science research undertaken with different types of data analysis, including visualisation, text mining, and computer vision </li><li>Explain how data is produced, cleaned, structured, and transformed </li><li>Understand how data can be used to reframe individual problems as broader structural patterns, and critically reflect on the cultural narratives such reframing creates</li></ul> On successful completion of the unit, it is expected that you will be able to: <ul><li>Read, critically evaluate, and apply literature on data-driven scholarship in the humanities, cultural analytics and data studies </li><li>Be able to read, understand, assess, and discuss arguments made with data </li><li>Interpret, assess, and question arguments made with data, identifying assumptions, biases, gaps, and methodological limitations. </li><li>Critically reflect on how choices made during data creation, cleaning, processing, modelling, and visualisation affect the meanings drawn from data. </li><li>Integrate conceptual and methodological perspectives, using theory to guide practical analysis and using practical work to interrogate theory.</li><li>Develop a critical perspective on data-intensive scholarship and learn to identify misinterpretation and oversimplification and act on your criticism by learning to work with data yourself</li></ul> On successful completion of the unit, it is expected that you will be able to: <ul><li>Use core tools and programming environments employed in the digital humanities, including Python, Jupyter notebooks, and specialised libraries for data manipulation, text processing, visualisation, and basic modelling. </li><li>Collect, clean, transform, and analyse cultural data from a variety of sources, including texts, metadata, images, and digital platforms. </li><li>Build reproducible workflows, documenting methodological decisions and using structured coding practices suitable for humanities research. </li><li>Generate and critique visualisations and analytical outputs, understanding how representational choices support or distort cultural interpretation. </li><li>Develop deeper proficiency in at least one analytical technique of your choosing, such as text mining, classification, network analysis, or metadata exploration.</li></ul> On successful completion of the unit, it is expected that you will be able to: <ul><li style="margin-bottom:0px;margin-top:0px;">Acquire and apply practical skills using a range of digital tools and coding techniques </li><li style="margin-bottom:0px;margin-top:0px;">Present information and arguments orally, verbally, and visually with due regard to the target audience </li><li style="margin-bottom:0px;margin-top:0px;">Reflect on your own analytical choices, demonstrating openness to feedback and the ability to refine your approach. </li><li style="margin-bottom:0px;margin-top:0px;">Think creatively how to develop and communicate your work </li></ul> Analytical skills This course enables you to critically read and evaluate data-driven arguments. You will learn to recognise biased, misleading, or oversimplifying forms of data analysis. Group/team working The course allows you to learn to collaborate in a team with diverse skills and potentially conflicting visions. Innovation/creativity The course allows you to formulate your own research questions, condense them into a manageable agenda, and answer them using new tools that allow you to develop and present your argument through visualization and narrative. Other By the end of this course students will be able to extract and process different types of data, generate their own visualisations, and understand key principles of data work, an increasingly essential skillset in a range of occupations. You will be able to present information and arguments orally, verbally and visually with due regard to the target audience This syllabus is indicativeData, Datafication and CitizenshipCounterdataMaking DataRepresenting DataCultural DataThe Natures of DataData JournalismOpen Data The course is taught through a weekly seminar, which combines introductory lectures, full class discussions, practical labs, and small group work. The course will take place in the Digital Humanities Lab and includes hands-on work with data. Students have access to two scheduled weekly consultation hours to meet individually with the course unit director to discuss their ideas and progress. All course material will be made available on Blackboard. All feedback will incorporate advice on improving future performance. 6 Project output (not diss/n) 80% 7 Oral assessment/presentation 20% <table class="Table" style="width:646px; margin-left:7px; border-collapse:collapse; border:none" width="646"> <tbody> <tr> <td>Feedback method</td> <td>Formative and/or Summative</td> </tr> <tr> <td>Detailed written feedback on written assignments, designed to include advice on improving future performance</td> <td>Summative</td> </tr> <tr> <td>15 minutes of the weekly seminars will be dedicated to discussing project progress and address issues as soon as they arise. Week 9 is entirely dedicated to project troubleshooting ahead of the final submission. Moreover, students are encouraged to seek formative feedback during seminars and in consultation hours.</td> <td>Formative</td> </tr> </tbody></table> N Indicative reading list: Bowker, Geoffrey C., and Susan Leigh Star. Sorting Things Out: Classification and Its Consequences. MIT Press, 2000.D’Ignazio, Catherine. Counting Feminicide: Data Feminism in Action. MIT Press, 2022.  Goldstein, Jenny, and Eric Nost. The Nature of Data: Infrastructures, Environments, Politics. Lincoln: University of Nebraska Press, 2022.Garrido, Gonzalo José López. “Radical Geography and Advocacy Mapping: The Case of the Detroit Geographical Expedition and Institute (1968–1972).” Journal of Planning History 20/4 (2021): 291–307.Guldi, Jo. “What Kind of Information Does the Era of Climate Change Require?” Climatic Change 169/1 (2021): 3 (https://doi.org/10.1007/s10584-021-03243-5).  Iliadis, Andrew, and Federica Russo. “Critical Data Studies: An Introduction.” Big Data & Society 3, no. 2 (2016)Loukissas, Yanni Alexander. All Data Are Local: Thinking Critically in a Data-Driven Society. Cambridge, MA: The MIT Press, 2019.McGrath, Laura, Richard Jean So, Ted Underwood, and Chad Wellmon. “Culture, Theory, Data: An Introduction.” New Literary History 53/4 (2023): 519– 30  Thorp, Jer. Living in Data: A Citizen’s Guide to a Better Information Future. Farrar, Straus and Giroux, 2021.Williams, Sarah. Data Action: Using Data for Public Good (MIT Press, 2020). Lectures 5 Seminars 10 0 285