Lecture Pod 2

Data Types-



-Latin word nomen– pertaining to names

it consists of names categories

inherently un-ordered, e.g dairy & fruit do not have higher greater mathematical value than each other.

It can be counted, but you can’t take the average of nominal data.
When there is only 2 categories available, it is referred to as dichotomas , so either yes or no answer.




Numbers are assigned to make the data analysis easier.

Assignments of numbers are orbitry.

You can count it & use it calculate percentages.

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It is numeric

0 doesn’t = the absence of anything, e.g 00am doesn’t mean there is not time.

Difference between 11:00 – 11:30



It is numeric, it has meaning full data

0 = absence of product



Using the correct data type is very important for designers, to collect & communicate data in the most effective manner. Ordinal – consecutive numbers are used to represent different categories.  Qualitative data is descriptive information, whereas quantitative data is numerical information.

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Screen Shot 2018-10-23 at 5.11.13 pmREFERENCES:

Week 2 Lecture Pod. (2018). Retrieved from https://vimeo.com/176274669


Lecture Pod 1


Introduction to Data Visualisation

  • It is a mass medium
  • Essential part of communication process
  • “23 Exabytes* of information was recorded & replicated in 2002. We now record & transfer that much information every 7 days.” – How much information? 2003
  • everyday people generate so much data, just from facebook , social media etc
  • we live in a complex data economy
  • What is Data? – data values quantity, can visualised using graphs & Infographs.
  • “Data on its own carries no meaning, it must be interpreted & take on a meaning to become information.”
  • Data is measurement
  • It involves creation & study of data which has been schematically formed.
  • Primary goal of Data vis – is to communicate information clearly, using statistical graphics & Infographs.
  • Difference between Infographs & Data Vis? – “not all information visualisations are based on data, but all data visualisations are information visualisations”
  • ‘it makes complex data more accessible, understandable & usable.’
  • ‘Users may have particular analytical tasks such as making comparisons.’

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  • Designers engage with forms of data to make them for digestible, the scope of data is rapidly increasing, we live in a “tsunami of data”, everyday people create a massive trail of personal data. Infographs can appear as data visualisations, however, they can be inaccurate/ non-meaning-full representations. ‘Effective visualization helps users analyse & reason about data & evidence’
  • It is both an art & a science
  • Presents ethical & analytical challenged to designers.

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The 4X4 Model for Winning Knowledge Content.

Bill Shander of Beehive Media at KA Connect 2014


Summary graphic of the model:

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Shedroff, N. (2014). Information interaction design a unified field theory of design.Retrieved from http://nathan.com/information-interaction-design-a-unified-field-theory-of-design/

Waterson, S. (2016). DataVis POD01 – What is Data Vis? [Lecture Pod]. Retrieved from https://vimeo.com/175177926