It seemed to have been rather taxing before the presentation, due to the fact that our group was far from being fully prepared. Luckily, we managed to pull through. Initially, I wanted to create a more creative form of infographics, but it just seem like I don't have enough resources and time. I guess we will have to settle with the current layout. This is the rush hour.
Our current progress: finished the infographic and finishing the report
Hope to finish it by tonight and submit tomorrow!
Friday 18 April 2014
Thursday 27 March 2014
Class Exercise (Yahoo)
Cool session with the Yahoo guy, inspired a few new ways of representing data. Data set was also interesting. Class exercise was well received and constructive feedback were given by him. I think for him, choosing the interesting and relevant question is very important, he never failed to re-emphasize each groups' focus and what information they could have obtained if they were to conduct a visualization project. Pretty slick. Each group came up with different ideas, like activities on weekends, colours, food etc. The good thing about the class exercise was that we were given an imaginative data set to work with. With no boundaries, students can ask anything and have everything to work with. Perhaps, my takeaway is that if there isn't sufficient data and openness for data visualization, creative works and discoveries won't be made. Since a visualization should tell a story, it is only necessary for the story to have content.
Class exercise
I think this is long overdue but here are my definitions of data visualization and infographics:
Data visualization: huge data, multiple data, complex graphs, open for interpretation, simple, allowance for multiple understanding, freedom of users to interpret, derived information.
Infographics: appealing, interpreted data, numbers, simple, comprehensible graphs, single interpretation but allows users to use the interpreted data in their own ways, fed information.
There is a thin line of difference between data viz and infographics, but a good informative design should incorporate both to make data more comprehensible and yet interesting.
Data visualization: huge data, multiple data, complex graphs, open for interpretation, simple, allowance for multiple understanding, freedom of users to interpret, derived information.
Infographics: appealing, interpreted data, numbers, simple, comprehensible graphs, single interpretation but allows users to use the interpreted data in their own ways, fed information.
There is a thin line of difference between data viz and infographics, but a good informative design should incorporate both to make data more comprehensible and yet interesting.
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