Data Fair 2019

What is the Data Fair?

Our data fair brings together our Master students in Design Informatics at the University of Edinburgh (http://www.designinformatics.org) and external partners (you!) to collaborate on data analysis and visualization. The goal is for the students to chose a real-world dataset and an associated 'challenge' over in our course 'Data Science for Design', running from October to December 2019. Within that course, students will learn the basics of data analysis and visualization. Their assignment requires them to analyze a data set (basic analysis and plotting) and work on a visualization project that can focus on exploratory or explanatory issues for data visualization. Students will work in groups of 3 students.

For references to the datafair, please cite this website and our research.

Why should I participate?

You will have the chance to work with motivated and creative students from a variety of backgrounds: graphic design, media, computer science, product design, etc. You will define a 'data challenge', an urgent problem or project you require help with around data analysis and visualization. You are invited to work with the students as close as you wish and attend our lectures and lab sessions (Thursdays 9am-1pm).

We hope that novel collaborations between you and the students and us will emerge. In semester 2, students have to chose open project as well as a master theses project in the summer; if the data fair project works well, there you are welcome to propose further projects and collaborate on supervision.

How can I participate?

The commitment from your side will be:

1. Submit a data brief and data challenge before Wednesday, 2nd October.
2. From all submissions, we will seek those ones we believe will best fit the course and students skills and let you know by October 4th (Friday).
3. Present your challenge on Thursday, 10th October 2018, 10:00am at Inspace (Edinburgh): pitch your data brief for 3min (including slides) and stay for 1-2h to meet and discuss your challenge with students. We organize speed-dating, then we’re having open discussions between you and the students. Ideally, you or a colleague will come to discuss details with students. In case this turns out to be tricky, please leave a note in your data brief and we will contact you.
4. Students have 1 week to decide on a challenge.
5. Then, provide your data to students in an accessible format and eventually help them get going.
6. During the semester, you consult with the students as much or little as you want and you're welcome to attend our lectures and lab sessions.
7. Students will first submit a report on the data analysis (~November 6). Then will work on a final visualization project.
8. Final presentations will happen November 28th, same location. Students are required to deliver their python code and an extensive data report with measures and visualizations. You are welcome to attend this event and give public feedback.

How do I write a Data Brief?

What happens after the day of the data fair:

We can’t promise everyone that their data will be used – it may be too complex or otherwise not suited to student analysis.

For selected datasets, students have 3 weeks to come up with an an individual analysis report (each student, hence 3 per group). Each report (ipython notebook including code and results) should investigate something different and will contain

1. 4-5 exploratory data visualisations, presented in a readable way and provided with explanation about what you have found.
2. 1-2 relationships between variables analysed: Trends, outliers, clusters, etc…some high-level statistics,
3. 2-3 hypotheses for further investigation

After these two weeks, students have 3 weeks to come up with some engaging form of presenting the data (group work). This end-piece should help communicating insights from or around the data to a specific audience. This will be shaped by what you think makes most sense for you and the data, but the students will have the final say over their brief. Some outputs might be:

1. An interactive web site
2. Interactive visualizations on a website
3. Data comics (http://datacomics.net)
4. Infographic
5. Physical data visualization (http://dataphys.org/list)
6. Data visualization in mixed reality
7. Data video
8. A standard scientific presentation / report.
9. anything else that might address your challenge.

For questions, contact us.

Course Organizers:

• Benjamin Bach, Lecturer in Design Informatics and Visualization, http://benjbach.me
• Dave Murray-Rust, Lecturer in Design Informatics, https://www.eca.ed.ac.uk/profile/dave-murray-rust

Submit a Data Challenge