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From Data to Argument

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rahulbot

guest lecture for the spring 2017 Systemic Justice course at Harvard

Education
1 of 20
From Data to Argument Rahul Bhargava rahulb@mit.edu • @rahulbot Systemic Justice Project 2017.02.22
Agenda [20] Intro & Collective Crit [10] A Process [15] Asking Questions [10] Finding Stories [15] Telling Your Story [15] Evaluating Stories
Intro
Group Crit The Global State of Agriculture USAID, 2011
Process
Models The Inverted Pyramid of Data Journalism Paul Bradshaw
Models Infographic Process Wheel Visualizing Impact
Asking questions brainstorming, hypothesis forming, audience and goals Gathering data Scraping, collecting, cleaning Finding a story Cleaning and analyzing Telling your story Picking a technique Trying it out Assessment My Model
Question Asking & Getting Data
Practice Asking Questions Together the Somerville Tree Census
WTFcsv - learning to asking questions https://databasic.io/wtfcsv Come back with: 1 question to ask list of other datasets you'd need how you'd try to get those datasets
Finding a Story
Sketching a Story https://databasic.io/wordcounter Pick a sample or two Find a quick story Sketch how you'd tell that story
Telling Your Story
Choose the Right Chart Andrew Abela
tools for making charts does lots of things does one thing easy to learn hard to learn raw processing Excel Tableau gephi D3.js Illustrator infogr.am Comic Life Super Lame skrollr Google Charts
Evaluating Stories
Abelson's MAGIC Magnitude - size of the claim Articulation - how precise is your claim Generality - is it valid in multiple contexts Interestingness - can this change beliefs in a way that matters? Credibility - do you believe it? Robert Abelson. 1995. Making Claims with Statistics. In Statistics as Principled Argument. Hillsdale, NJ: Lawrence Erlbaum Associates, 1–16. (PDF)
Convincing People Let's practice making arguments with data to convince people.
From Data to Argument Rahul Bhargava rahulb@mit.edu • @rahulbot Systemic Justice Project 2017.02.22

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Editor's Notes

  1. types: open data with APIs, semi-open data - PDF munging, scraping, crowdsourcing, citizen science - manual data collection sources: social media, government, non-profits / interest groups, DIY