polyglot-workshop-syllabus-2015.txt

[Subject to minor updates]

Science-Driven Decision Making: Syllabus

1. Setup & intro
2. What the news sound like
	- Find and assess the keywords
	- Find and assess the metrics
3. Association
	- What is "observational" data?
	- What does association really mean: average differences
	- What does it usually look like behind the scenes: oh, the variance!
	- Is it "real"?
		- The traps of small sample sizes
		- What does "accurate to plus or minus three percentage points, 19 times out of 20" really mean?
		- What it doesn't mean
		- The importance of prior knowledge
	- How does association come about?
	- What's the problem? Simpson's paradox
	- When is association useful for decision making?
4. Causality
	- Why run experiments
	- How to run experiments - What is A/B testing
		- The importance of sampling and randomization
		- What do "blind" and "double blind" mean?
		- What is the "Placebo effect"?
	- What can experiments tell us?
		- Deja vu? More averages, variance, tests and prior knowledge
[Short Break]
5. More buzzwords explained
	- Prediction: association, causality, both?
	- What is "Machine Learning"?
	- Does "Big Data" make all our problems go away? (spoiler: no)
		- Patterns, patterns everywhere
6. Media and society
	- The "positive results" bias
	- The "news" bias
	- The "sales" bias
7. Comprehensive article analysis exercise
8. Roadmap ahead