The New York Times Releases Its Dataset of U.S. Confirmed Coronavirus Cases

Pick a lat/long, and you can map between. Or find the centroid of the zip code and map accordingly. Or you might consider unions between the geometry objects. Qgis3 has a lot of great packages for this.

You can do better than geocoding: use one of the county-level shapefiles from the TopoJSON project: https://github.com/topojson/us-atlas

At the very least, if you do just need point-level info, you can get the centroids of the county shapes.

It is primarily dangerous to the elderly. That is what the actual data shows.

Some people are pushing a counter-narrative that youngs aren't invincible. Which is also true. 0.1% death rates doesn't mean zero deaths. A higher number will be hospitalized, and as hospitals become overwhelmed that means (a) quality of care will be degraded and death rates will increase somewhat, and (b) any youngs in a bed comes at the direct expense of an elderly in a bed.

But if you are claiming that this disease is not mainly dangerous to the elderly, no, that is false, the deaths and hospitalizations are hugely weighted to the elderly and are quite low for young cohorts.

Yes, it is important that everyone minimize contacts as much as possible. For some reason people love to really harp on this point with the youngs. I didn't go to florida for spring break, it was my boomer parents who did (or rather, were planning on going this summer and complained nonstop that Florida state parks closed and cut them off). Churches are still open in many states spreading disease. Boomers are still going to Home Depot and buying their home improvement goods. A lot of boomers aren't taking this seriously either but nobody chooses to bring that up.

There are lots of changes that need to be made by everyone, but younger people are one of the primary targets of casual/unthinking ageism in this country. Avocado toast and all that.

Focusing the blame on this onto the young is no more correct, helpful, or productive than focusing the blame on the Chinese, and I suspect is coming from most of the same media sources.

Oh, I think it's completely fine to play with the data and talk about it with friends.

But SEOd medium posts have different reach potential than a facebook comment/phone call with friends/family. That's kind of the whole point of them.

One thing about experts is that they’ve usually made it through the Dunning-Kruger “confidence pit” and come out the other side. They’re often cautious about making very strident remarks, and when they are really confident in a conclusion, it’s usually because it has a very high probability of being correct. When someone new to a field comes to a contrarian conclusion, they’re often willing to show more confidence in their conclusion because of the Dunning-Kruger effect. This makes it hard to take non-experts at their word, since they’re often unaware of how likely they are to be wrong.

Now, if a total non-expert had come out of nowhere with an analysis that contradicted the public health establishment, definitively showed that the danger was massively overblown, and was right, that would be one thing. But this medium post was incorrect in its conclusion, quite confident in its conclusion, and advised courses of action that would put many people in danger (such as reopening schools).

Self promotion with ??? analysis about public health crisis should make EVERYONE cringe.

Not all self promotion is good self promotion. I've nuked candidates because their online portfolios demonstrate bad technical judgement, questionable professional ethics, a fundamental misunderstanding of core ideas, etc.

Put more simply: a portfolio is a demonstration of your work. Anything you put out in public with your name on it is part of your portfolio. Obviously, don't put bad work in your public portfolio.

There's a reason you see very few serious data scientists publishing hot takes on their medium blogs -- it's a serious threat to your professional integrity and brand if you get it wrong. The only people I know & respect who are writing publicly about this topic have SME collaborators. (Of course, we're all playing with the data and talking about it in private with friends/coworkers over coffee.)

Self promotion specifically by spreading your amateur take on a health crisis seems a bit... immoral? Or at least the sort of thing that makes me question the candidate's judgement.

If you want to write publicly and in a formal way on this topic, get a subject matter expert to serve as a co-author. Anything less is more risky than it's worth, even from a purely selfish perspective.

> I studied CS at a great cs uni

Do you feel that your time there was wasted, and that you would have been better off attending a more focused bootcamp?

Well, the problem isn't that the bootcamp people lack a computer science education, it's that they lack any formal education. I've hired epistemologists and physicians to program computers and it turned out fine. The bootcamp process might in fact work well as training for otherwise educated people.

I've worked as a programmer for 15 years. I did a Philosophy degree.

I can run circles around some programmers with a CS degree.

CS degrees have always been an odd qualification, mainly because so much of it is completely unrelated to professional programming. You can get a high mark and still be unable to write a non-trivial program on your own.

When I originally started doing my Chemistry degree, we did huge amounts of lab work compared to the odd class or two CS students did.

You don't come out of uni a good progammer if you study CS, while you will come out of uni a good chemist, biologist or engineer.

And that really sums up the problem with this line of argument.

I am not looking down on folks different from myself. Also, I am not a data scientist. Regardless, what you are claiming is not possible.

As I am sure you know, data science is a specialized field, one which draws upon a combination of statistics and computer science practices. You may be able to get some of the computer science practices down in a boot camp, but there's no way you're getting a boot camp to fill in the Bachelor's in statistics you need to be a good data scientist. Years of hard work and sincere dedication, however, I am sure can get you there.

The issue with is the word "scientist".

In academia, science is the pursuit of postulating a falsifiable theory, then gathering facts to verify that theory, and then going through a process of intensive peer review that either confirms, dispels or amends those findings.

The formality of the academic process is a necessity. The value of scientific findings entirely depends on the trustworthiness of the research. That is, how were the results obtained, which line of thinking was followed, did the research exclude crucial biases, etc.?

The importance here is that academic research is used as a pillar to produce products and services we all use in daily life. For instance, if you need a hip replacement, you want to be sure that prosthetic was designed based on rigorous scientific findings and studies that can vouch for safety and comfort.

The difference then with "data scientist" is that they often they don't apply the same rigorous research practices. It's easy to pick a dataset and bang off visualizations; it's a different story to actually come up with relevant questions, assert the quality of the data at hand and publish your findings towards a community of domain experts who are actually able to review your findings.

One needs in-depth domain knowledge to do that. Good data scientists will understand this limitation. They often work in a specific domain in a supporting capacity: bringing technical skills and capabilities to domain experts that don't have those skills.

Then there are those who purport to practice data science while grokking datasets, creating visualisations and cobbling a blogpost together at the end of the day. That's when you need to be really wary of what they publish, even if the bigger picture contains truthiness.

Hence why I have extremely mixed feelings about what https://medium.com/@tomaspueyo is doing.

To be sure, the core points of what he's telling are in line with what domain experts are telling us. But the extreme number juggling is quite mind bending. Moreover, the man is not a domain expert. He's an entrepreneur who happens to know how to write viral blogposts such as "how to deliver your funny speech" and "How to become the best in the world at something". What he does is anything but scientific. And so, even though he's making a heartfelt plea heard by many, one should be careful to not take the precise details in his pieces at face value.

At the moment, we all are victims of our own confirmation bias. Each day yields another data point, and given our desperate state, we want to see trends that confirm improvement, a probability that one will survive this, low mortality and so on. The reality is that we only have so few datapoints and it's still far too soon to make conclusive assertions about how this will pan out for the world at large and you in particular.

I'm happy to hear that. Ethics is oft-neglected, but I believe it's imperative that we guide our efforts using it.

Now, if we could only get SE and DS professionals to stop working on mass surveillance and population manipulation systems.

The issue is that making a mistake here isn't like making a mistake about, say, movie review sentiment analysis. Already there have been a few "I know stats!" posts that didn't do the right controls but still got sent around the internet as proof of someone's argument (see the "Viral advertising exec now knows epidemiology" post that medium took down). These data are confusing and difficult. It turns out there's a bunch of people who have studied this field for years and can help us avoid those issues (epidemiologists). We should listen to them. That's not to say we shouldn't open the data, but handle with extreme care, which isn't the modality of most early data scientists. A 4 week bootcamp doesn't lead to the most rigorous science, usually that's not a big issue (Pareto principle and all that), but when it's lives and active problems, it can become a big issue.

I say this as a graduate of Insight which is a kind of DS bootcamp.

Florida?