[laGrenouille]

> The unfortunate truth about data is that nothing much can be done with it

This is a fairly strong statement that goes against a lot of other work in data science and information visualization (John Tukey, Edward Tufte, Jacques Bertin, Hadley Wickham, ...). For example, see [0] and [1].

[0] https://en.wikipedia.org/wiki/Exploratory_data_analysis [1] https://courses.csail.mit.edu/18.337/2015/docs/50YearsDataSc...

[nightski]

You are leaving out a very important part of the sentence - "until we say what caused it". If you listen to the first few lectures you'll understand exactly what he intends with this sentence.

[hinkley]

This cleaves very close to an aphorism I stole mercilessly many years ago: charts are for asking questions, not answering them.

“What caused it” is the answer, and a graph can reveal just as easily as it can conceal the cause. Lies, damn lies, and statistics.

[chiefalchemist]

To your point...Data has context. It has a source. It likely has flaws and/or (so to speak) bias. To get anything of it It's essential to understand what went into it. Else you'll deceive yourself or your stakeholders and bad decisions will be made.

[laGrenouille]

Thanks, though I actually meant to copy the entire thing (my fault).

My point was that a lot of people working in data analysis would (strongly) disagree with the idea that we need to model the data in order to do anything with it. Visualisations and tabulations can tell a lot without any mathematical formalism.

[agucova]

This is taking the quote completely out of context, it's not the data itself that conveys useful information, it's the data combined with a causal model!

[bigbillheck]

> The unfortunate truth about data is that nothing much can be done with it until we say what caused it

Nonparametric methods say 'hi'.