Week 13: Research

Twelve Simple Tips to Improve your Sleep


The Atlantic: Cari Romm



Psychology Today: Michelle Carr



Harvard Health:Harvard Health Publishing



Week Nine: Art made of Data

Nathalie Miebach

Nathalie Miebach extracts data from her surroundings using low tech data collection devices. She then contrasts them with online data both historical and current. Intertwining the data into a playful display. Miebach also toys with musical scores that she collaborates with musicians who play the scores and receives them into a data display not too dissimilar .




Aaron Koblin

Koblin is known for exploring modern technology and its ability to make us more human. He translates beautiful visualisations of immense data into his work. Koblin traces airline flights to landscapes off cell phones. .





Week Eight: The Beauty of Data

Week Eight: David Mccandleuss – The Beauty of Data Visualisation

David McCandless describes Information overload in our modern society and the simple solution of visualisation of information to enable us to make more sense of, or find stories within this information. He suggests that good design is the best way to navigate this information overload, and it may change the way we see the world.

Week Six: Data Journalism

Lecture 6: What is Data Journalism?

The Guardian describes Data Journalism as; the use of key data sets to inform a story. It’s about processing what the data tells you. Data Visualisation is about the recognition of the power of measurement in helping public conversation and public discourse. Its not about opinion its about facts and numbers.

The Guardian Datablog was the first blog of its kind and has become a key component of The Guardians Journalistic style.

History of Data Journalism at The Guardian

While many believe that data journalism is brand new and relies entirely on new technologies that didn’t exist before 2009; however, journalists at the Guardian have been using data since their first issue in 1821.

The first example of Data Journalism is a long table of data, a list of every school in Manchester, with how many children in each school, and how much each girl and boy costs. It was captioned with the following:

“At all times such information as it contains is valuable, because without knowing the extent to which education and particularly the education of the labouring classes prevails, the best opinions which can be formed of the condition of future progress of society must be necessarily incorrect.”

Meaning unless we know and understand what is going on in the world, through data, how can things improve and how can things get any better, which is still.

Data journalism in action: the London Olympics

People were obsessed with Medal tables and the performance of one country over another. The guardian wanted to mash up the data tables with population, team size etc. They wanted to weight the medals to show the worth of a small poor country winning 5 medals compared to a large rich country winning 5 medals. They used statistics to work out a medals count. This then became a visualisation created by Garry Blight. It allowed you to explore the data and engage in the community aspect of data journalism. It was also used throughout many countries coverage of the olympics.

Week Five: Data Presentation Styles & Graphs

Lecture 5: Data Presentation Styles & Graphs


The main reason we use graphs is to make comparisons easier.

Our brains are good at comparing a single dimension, e.g. Length. We are not good at calculating more complex measurements e.g. surface area, height x width. So it is important to use the correct style of graph for the data.

The following diagram ranks the different graphic approaches to comparing data, based on human visual perception.

Types of graphs:

Bar chart: Incredibly useful, easy to use, most people have a familiarity with them. Easy and quick to compare information. Especially effective with numerical data. Easy to see trends.

Line Chart: Primary use is to display trends in data over time.

Pie chart: Used to show relative proportions or percentages of information (Only) e.g. percentage of a budget that’s been spent in different departments. Limit the number of wedges to 6, if more are needed consider a bar chart.

Week Three + Four: The History

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



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Week Two: Data Types

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 

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