This article appears in my HN feed with an article about throwing away React. The React article explains that having code that prevents engineers from doing whatever they want to is essential. If we can't trust engineers coding, why do we trust police arresting? Restraints are needed.
The life concept here is so different from the recent post about the MIT media lab.
"I watched two brilliant students organize two massive hackathons to improve the breast pump, challenging assumptions about who gets to invent the future and what problems are worth solving. Another student launched a remarkably successful movement against facial recognition technologies by demonstrating that they often embed significant racial biases. ...And late one night, I saw a young woman walk past my door wearing a massive pair of delicate, filigreed copper angel wings"
http://www.ethanzuckerman.com/blog/2020/08/15/to-the-future-...
I think maybe the issue here is that the press did not cover the unidentified federal agents arresting the BLM protesters. The result is that it looks like the treatment of the Portland protesters is different.
It implies that your friend lives in Hong Kong or Taiwan, and that China employs people to read Facebook posts from those places. Unless you mean that she sent you a messenger chat (which now has end-to-end encryption). In that case, she lives in Hong Kong or Taiwan and the Facebook encryption should not be relied on to protect yourself from Chinese monitoring.
And incredibly boring. The usual estimate is that data science is 80% data wrangling: finding, collecting, and cleaning up data. The term "data scientist" replaced "data miner", because miners are looking for gold. Scientists are obsessed with finding out the nature of reality, gold or mud. They will do seriously boring stuff to set things up so that reality is revealed.
If the data cleaning is follows standard patterns, you should already have scripts to offload that kind of work to. If not, then there some incredibly interesting decisions hidden underneath. Like in text: Should character casing be preserved ? What should be the unit of representation (word/character) ? How should data be filtered: Quality vs quantity trade-off ?
All of those are non-trivial questions which involve a lot of thought to reason through. You are correct that the modelling is only a small part of DS's day to day job.
But, the rest of it is boring in the same way that coding is boring. It is doesn't involve some grand epiphanies or discoveries, but there is joy similar to the daily grind of "code -> get bug/ violate constraints -> follow trace/problem -> figure a sensible solution" that a lot of software engineers love.
At one point, the word "scientist" in "data scientist" was used to distinguish between people who took the time to develop domain expertise from statistical consultants who applied standard methodologies without reference to what the data was or where it came from.
