- UCAS course code
- 6G49
- UCAS institution code
- M20
Bachelor of Science (BSc)
BSc Fashion Buying and Merchandising
Become a successful fashion buyer by combining creativity and trend-spotting with data analysis, business theory and textile science.
- Typical A-level offer: AAB including specific subjects
- Typical contextual A-level offer: ABB including specific subjects
- Refugee/care-experienced offer: BBB including specific subjects
- Typical International Baccalaureate offer: 35 points overall with 6,6,5 at HL
Fees and funding
Fees
Tuition fees for home students commencing their studies in September 2025 will be £9,535 per annum (subject to Parliamentary approval). Tuition fees for international students will be £38,000 per annum. For general information please see the undergraduate finance pages.
Policy on additional costs
All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).
Scholarships/sponsorships
The University of Manchester is committed to attracting and supporting the very best students. We have a focus on nurturing talent and ability and we want to make sure that you have the opportunity to study here, regardless of your financial circumstances. For information about scholarships and bursaries please visit our undergraduate student finance pages and our the Department funding pages .
Course unit details:
Visualising Information: Uses and Abuses of Data
Unit code | UCIL20421 |
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Credit rating | 20 |
Unit level | Level 2 |
Teaching period(s) | Semester 1 |
Available as a free choice unit? | Yes |
Overview
"A picture is worth a thousand words," but only if you know how to read it. Digital technology has made charts, maps, and data visualisations easier to create and share than ever. Whether we consider climate change graphs, invasion maps, or visualisations of artificial intelligence, well-designed charts and maps can be enlightening, but they can also be ambiguous or misleading. Indeed, we are often ill-equipped to approach visualised information critically.
In this course, you will learn to engage with information visually. You will learn to recognise and critique oversimplifying, biased, or misleading forms of visual representation and to create your own visualisations to explore and communicate data that matters to you. Through examples from a wide range of academic disciplines - including such fields as economics, literature, meteorology, history, urban design, and computer science - you will discover key principles of exploratory and public-facing data visualisation and learn how to create your own charts and maps.
Aims
- Explore uses (and abuses) of visualised information in different domains of knowledge, from infection charts to invasion maps.
- Allow you to master to some of the most important data visualisation tools used across disciplines to create interactive visualisations, maps, and network graphs
- Help you, regardless of your discipline or background, to become more vigilant and reflective users of visual information
- Enhance your employability by allowing you to develop technical, critical, and creative skills needed to thrive in sectors that work with data and visual information
Learning outcomes
On successful completion of the unit you will be able to:
- Identify the opportunities and limitations of data visualisation
- Decide when a visualisation tool can be useful for specific questions in your subject area.
- Critically reflect on how data modelling and visualisation choices influence the interpretation of the data
- Evaluate different types of projects undertaken in text mining, network analysis, and digital mapping.
- Use digital tools to collect, analyse, and explore different types of data, and create high quality maps and charts
- Present information and arguments orally, verbally and visually with due regard to the target audience
- Apply skills and concepts learned in class to plan, develop and present a research project (for 20 credit students)
Syllabus
- Why We Visualise
- Visual Variables
- Thinking with Charts
- Visualising Space: Maps
- Deceptive Visualisations
- Envisioning Connection: Networks
- Beyond Numbers: Qualitative Visualisation
- Visualising Uncertainty
- Data Visualisation and Social Justice
- Vision and Knowledge
Teaching and learning methods
The unit includes contributions from leading researchers from a broad range of disciplines, including history, computer science, sociology, and meteorology and others.
The unit is made up of 5/10 online modules (released at intervals) and 5/10 face-to-face seminars that include practical tutorials and discussions in the Digital Humanities Lab, which is equipped with computers and large screens.
The unit is interactive and uses a variety of learning materials, including historical and contemporary visualisations from a broad range of disciplines.
Knowledge and understanding
On successful completion of the unit you will be able to:
- Develop an awareness of the opportunities and limitations of digital tools and visual information.
- Evaluate different types of projects undertaken in text mining, network analysis, and digital mapping.
- Recognise how charts and maps can both enhance and limit our understanding of a wide range of phenomena
Intellectual skills
- Read, critically evaluate, and apply literature on distant reading, network analysis, mapping, and data visualisation.
- Decide when a digital tool can be useful for specific questions in your subject area.
- Critically reflect on how data modelling and visualisation choices influence the interpretation of the data
- Remain vigilant for bullshit contaminating your information diet
- Develop a critical perspective on maps and charts and learn to identify misuse, oversimplification, or misleading use of colour, symbology, or scale and act on your criticism by creating visualisations of your own
- Apply skills and concepts learned in class to plan, develop and present a research project
Practical skills
- Learn to use some of the most important tools currently employed in the humanities and social sciences and develop a deeper proficiency in at least one technology of your choosing
- Identify a question amenable to digital analysis and explore the use of geospatial, network, distant reading techniques and concepts to answer it
- Use digital tools to collect, analyse, and explore different types of data
- Create high quality maps and charts
- See a digital research project from inception to completion
- Present an argument through visualization and narrative, combining short texts, supporting graphics, figures, and tables
Transferable skills and personal qualities
- Acquire practical skills using a range of different digital applications
- Present information and arguments orally, verbally and visually with due regard to the target audience
- Think creatively how to develop and communicate your work.
- Gather, clean, and synthesise data from a diverse range of sources
Assessment methods
Method | Weight |
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Other | 10% |
Written assignment (inc essay) | 55% |
Oral assessment/presentation | 35% |
- Ongoing assessments (10%)
- Presentation of a visualisation: use one of the technologies learned to develop a basic visualisation and present it in oral and written form (5 minute presentation & 500 word report) (35%)
- Written Task (choice of Essay or StoryMap) with self-designed visualisation (3000 words) (55%)
Feedback methods
Feedback on assignments
Office hours
Study hours
Scheduled activity hours | |
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eAssessment | 100 |
Independent study hours | |
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Independent study | 100 |
Teaching staff
Staff member | Role |
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Luca Scholz | Unit coordinator |