Postgraduate Course: Data Science for Design (DESI11100)
Course Outline
| School | Edinburgh College of Art |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course provides an introduction to programming, used in support of the development of data science techniques, to give a practical facility in manipulating, analysing, visualising and contextualising data within design and creative practice. |
| Course description |
You will be introduced to the importance of data and how to work with it creatively and critically in a robust, communicative and defensible manner.
The ability to program brings a qualitatively different view of the world, one which designers are increasingly required to be familiar with. Similarly, the ability to understand the structure and meanings of data, and communicate this while maintaining awareness of the social context from which the data came is a key part of working in this field.
Through demonstration and practical application, you will be supported in developing a competence in writing code to work with data. Building on this, you will develop an understanding of data through statistics, machine learning, and exploratory visualisation, sonification and physicalisation. Lectures will provide you with theoretical context, examples of data-driven creative practice, case studies, and historical analysis to build up an understanding of how political and economic structures, cognitive and social biases, and ethical frameworks and governance impact on how data is collected and used to affect lives.
Bringing this together around a particular problem will help you develop your ability to work creatively with code and data, collaborate professionally, and work with a challenge holder to collect, analyse and present data sets of social relevance.
You will attend in person weekly lectures and demonstrations (1.5hrs) and weekly workshops and practical labs (2hrs). You will be assessed through individual assignments and a group project.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
| Additional Costs | Students must have a laptop. |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2025/26, Available to all students (SV1)
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Quota: 0 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 11,
Seminar/Tutorial Hours 5,
Supervised Practical/Workshop/Studio Hours 16.5,
Formative Assessment Hours 2,
Summative Assessment Hours 1,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
160 )
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| Additional Information (Learning and Teaching) |
Tutorials and Supervised Practical hours will be taught in groups.
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
This course has 3 assessment components.
1) Individual Report 1: 150-200 lines of code appropriately documented with 300-500 words of analysis and reflection, 30%, weeks 5-6, assessed against Learning Outcomes 1 and 2.
You will design and implement in Python a scripted exploratory analysis of assigned datasets.
2) Group Project: A creative artefact, 200-300 lines of code, and 1500-2000 words of analysis and reflection, 50%, weeks 11-12, assessed against Learning Outcomes 1, 2, 3 and 4.
Working in a small team, you will produce a creative artefact such as a visualisation, sonification, physicalisation or interactive experience in response to a challenge that requires programmatic exploration of real-world datasets. You will write an accompanying report communicating your analysis of the challenge and the datasets, documenting your design response, and critically reflecting on your process.
3) Individual Reflection: 1000 words, 20%, week 12, assessed against Learning Outcomes 3 and 4.
You will write a report evidencing your contributions and reflecting on your professionalism in working on the group project
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| Feedback |
Formative Feedback
Formative feedback will be provided verbally by the course organiser, tutors and peers during weekly labs and tutorials. This feedback will guide students in developing the technical and analytical skills required in summative component 1 and additional creative, critical and reflective capacities for completing summative components 2 and 3
Summative Feedback
Summative feedback will be provided in writing by the course organiser and tutors following the submission of all 3 components. Summative feedback for component 1 will help students identify the technical and analytical skills needing further development to complete components 2 and 3
Summative feedback will be provided according to University regulations. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Identify, understand and deploy programming strategies for working with data in multiple formats using Python
- Demonstrate the ability to transform, combine, analyse, learn from and creatively explore a range of data in ways that are relevant to particular contexts of enquiry
- Communicate and critically reflect around socially relevant issues, supported by the creative use of data sources and appropriate analysis
- Collaborate within an interdisciplinary team to a high professional standard and reflect critically on practice
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Reading List
D¿Ignazio, Catherine, and Lauren F. Klein. Data Feminism. Strong Ideas Series. Cambridge, Massachusetts: The MIT Press, 2020.
Maeda, John, ed. Creative Code. 1. publ. London: Thames & Hudson, 2004.
McCandless, David. Information Is Beautiful. New ed. London: Collins, 2012.
O¿Neil, Cathy. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. London: Penguin Books, 2016.
Schutt, Rachel, and Cathy O¿Neil. Doing Data Science. Beijing: O¿Reilly Media, 2014.
Tufte, Edward R. Visual Explanations: Images and Quantities, Evidence and Narrative. Tenth printing, July 2012. Cheshire, Conn: Graphics Press, 2012. |
Additional Information
| Graduate Attributes and Skills |
Research and enquiry: evaluating, transforming and managing data while drawing inferences through statistics, exploratory data analysis and machine learning will help you develop fundamental skills for undertaking academic and practice-based research
Personal and intellectual autonomy: considering data within real-world socio-political contexts will aid in honing your ability to think critically and reflect on your own perspective, experiences and practices as well as those of others.
Communication: through creatively presenting your findings from exploring data programmatically you will improve the effectiveness of your communication. |
| Keywords | data science,data analysis,computer programming,visualisation,software carpentry |
Contacts
| Course organiser | Theodore Koterwas
Tel:
Email: tkoterwa@ed.ac.uk |
Course secretary | Ms Ruth Lee
Tel:
Email: clee5@ed.ac.uk |
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