Notes from Data Visualisation and D3.js (Communicating with Data) MOOC by udacity (Highly recommend*****)

Traditional Journalism – spectrum (quantitive (numbers) vs. qualitative (context), Rigorous & Empirical vs. Anecdotal & Ad-hoc. 538 (quant and rigous) and washington post (qual and rigorous)

Correlation vs. Causation

A and B vs A –>B

Traditional Journalism used data around the narrative (data is supportive in a secondary role), Sequence of event flow, physical and/or static

Data Journalism narrative around the data (data is in front and centre), interactive allowing complex and find own story structure, interactive and open web

Common mistakes – not mostly about viz, 90% work is finding, exploring, cleaning, preparing the data before the vizualisation

Don’t lie with data – bar chart = axis show start a 0 , pie chart = no 3D,

Author bias – knowingly or unknowingly misrepresenting data through visual encoding. Your design choices should establish trust between the reader and the graphic.

Data bias – Data bias arises from the process of collecting data. Issues with data collection, sampling methods and other issues. subjective data – Feltron annual personal reports

Reader Bias – Any preconceived notions or assumptions that a reader brings to interpreting a visualization.

5min field guide data & data from web 

Types of narrative structure

• intro
• rising action
• climax
• final resolution

Author driven narrative – start to end, strong order, heavy msg, need for clarity and speed, linear and pre-determined

Viewer driven narrative start multiple endings – viewer choose their story, allows ask q’s, explore, tell your own data story.

Martini glass ——< starting point, single path to then explore own story example drone strikes