Twelve Simple Tips to Improve your Sleep
http://healthysleep.med.harvard.edu/healthy/getting/overcoming/tips
The Atlantic: Cari Romm
Psychology Today: Michelle Carr
https://www.psychologytoday.com/blog/dream-factory/201706/thoughts-in-the-brain-awake-and-asleep
Harvard Health:Harvard Health Publishing
https://www.health.harvard.edu/staying-healthy/learning-while-you-sleep-dream-or-reality
CLASS EXERCISE
CLASS EXERCISES
Today we distilled data in order to extract a story. The story we would like to share compares fitness to screen usage and the time spent doing so.
Lectures 3+ 4: The History
Data visualisation has been used throughout history to give an audience a tool to analyse and make data comparisons themselves. One of the strengths of data visualisation is that it can reduce the time necessary for understanding.
The following image of Napoleon’s Invasion of Russia 1812 by Charles Joseph Minard is an early example of data visualisation
CLASS EXERCISE
Lecture 2: Data Types
There are four data types nominal, ordinal, interval, and ratio.
Nominal: Nominal data can be counted and used to calculate the percentage but you can’t take the average of nominal data.
e.g. you walk through each of the sections of a Grocery store, grabbing canned goods, fresh fruit and veg, dairy, frozen foods. If you were to make a list of what section of the store each item came from, the data would come into the nominal data type. Nominal data can also be referred to as category.
Ordinal: is a categorical, statistical data type where the variables have natural, ordered categories and the distances between the categories is not known
Like nominal data, you can count ordinal data and use them to calculate percents, but there is some disagreement about whether you can average ordinal data. On the one hand, you can’t average named categories like “strongly agree” and even if you assign numeric values, they don’t have a true mathematical meaning. Each numeric value represents a particular category, rather than a count of something. E.g.
Castello, M. (2014). Ordinal data example. Retrieved from https://infoactive.co/data-design/ch01.html
Interval data: Interval data is numeric and you can do mathematical operations on it, but it doesn’t have a “meaningful” zero point – that is, the value of zero doesn’t indicate the absence of the thing you’re measuring.
Interval data examples that you encounter in everyday life are time, calendar years and temperature. A value of zero for years doesn’t mean that time didn’t exist before that, and a temperature of zero (when measured in C or F) doesn’t mean there’s no heat.
Ratio Data: Ratio data is numeric and a lot like interval data, except it does have a meaningful zero point. In ratio data, a value of zero indicates an absence of whatever you’re measuring—zero minutes, zero people in line, zero dairy products in your basket.
Data can also be described as Qualitative or Quantitative.
Qualitative: refers to non-numeric data
Quantitative: data is numerical
CLASS EXERCISE: Quality of Quantity
Lecture 1: Introduction
Data Visualisation is an all encompassing aspect of the communication process. Data visualisation: a pictorial or graphical presentation of information. Enabling viewers to see information legibly, so that convoluted concepts can be untangled and understood.
“There is a tsunami of data that is crashing onto the beaches of the civilized world. This is a tidal wave of unrelated, growing data formed in bits and bytes, coming in an unorganised, uncontrolled, incoherent cacophony of foam. None of it is easily related, none of it comes with any organisation methodology…”
-Richard Saul Wurman in Information Architects
What is Data?
Data are values of qualitative or quantitative variables belonging to a set of items. Data is usually the results of measurements and can be visualized using graphs or images. Data Information and knowledge are frequently used for overlapping concepts. Data in itself carries no meaning, for it to become information it needs to be interpreted. The following diagram explains this further. Some Key Points:
DATA VISUALISATION 