People that claim to be data scientists after attending a coding boot camp must be clowns. I’ve never come across such a person. All the data scientists I know are smart self taught people, not people who went to a boot camp.
I've met smart data scientists and engineers who've gone to a bootcamp. People who are good and bad at their jobs come from a huge variety of backgrounds, and it's not helpful to look down on folks whose background is different than your own.
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.
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.
For all intents and purposes, there are authors who write similar massive viral pieces that may - and likely will - end up killing thousands of people inadvertently.
As I said, the core message is absolutely right, but his method - the way he packs his message with solid looking graphs and number juggling - is questionable. Sure, it gets the job done; whereas many others fail to push the very same message. But it's still a questionable tactic of convincing people.
Does it matter? Perhaps not. At the end of the day, he saved lives. Morality is a luxury presently. And yet, dismissing critical considerations outright equally opens the door to unintended consequences if we turn it into a habit each time someone is purported to have saved lives.
I'm not saying random data science blogs aren't often wrong. But you've probably been burned just as often by publish science and simply haven't realized it. And at least the data science blogs aren't behind expensive paywalls, aren't couched in meaningless vernacular, and present the code/data for reproducing their results, none of which can be said for a lot of science these days.
Open science and open access are important movements as to the relationship between publishers and academic research.
However, that debate doesn't negate my point as to citizen or data science.
For all the discussion about paywalls and the use of vernacular, the same timeless critical considerations need applying: who wrote the article? what is their background? are they a domain expert? where they did get their sources? how did they come to conclusions? is their method sound? are they asking relevant questions? etc. etc.
And I don't always see that happening online. On the contrary. The fluidity and the speed at which information flows online seems to be a justification to give in and accept what's being said at face value. The past few years should have made it clear that such indulgence can lead to dire consequences.
With powerful tools comes responsibility. Sure, it's great to see people apply free and open source tools to come to a new understanding of observations. But that's only half of the story: you still have to apply critical thinking to those conclusions. That's not something throwing more code or technical skills can achieve.
Having a proper, critical debate takes time and experience.
This seems to be an improvement over academia imo. The person writing the article should be immaterial. The same paper written by an unheard of researcher should be treated no differently than the same paper written by a well-known, tenured researcher at a prestigious institution. Unfortunately in academia, who you are and who you know is often as big a deal as what you do and what you know.
> What is their background? are they a domain expert?
Again, the methodology should stand alone. Being a "domain expert" or having a particular background is only relevant if the readers of the article are incapable of judging the results on their own merits, and have to instead rely appeals to authority. But appeals to authority introduce their own problems, including a moat that protects the status quo and the ingroup from potentially important new ideas and newcomers. And its hardly scientific.
> where they did get their sources? how did they come to conclusions? is their method sound? are they asking relevant questions? etc. etc.
These need not be restricted to academic work. If a blog fails to properly cite sources, you can move on. Plenty of academic papers are built on flimsy premises, poor methodology, asking incorrect questions, etc. etc. There is nothing differentiating the ability to assess the quality of something written in a blog vs something written in an academic journal here.
> And I don't always see that happening online. On the contrary. The fluidity and the speed at which information flows online seems to be a justification to give in and accept what's being said at face value. The past few years should have made it clear that such indulgence can lead to dire consequences.
And yet, as noted, the more conservative approach of academia has resulted in a huge amount of unreproducible "science". And now, in a time of crisis, the stringent model is being set aside in favor of openly available preprints shared online, without peer review, via social media. How can the traditional model be considered useful when, in times without urgency, the validity of its results are highly questionable, and in times of urgency, its thrown aside for the sake of actual progress? It seems to me the modern academic method is largely a facade, something closer to an ornate religious ritual that has been divorced of its actual intentions.
> But that's only half of the story: you still have to apply critical thinking to those conclusions. That's not something throwing more code or technical skills can achieve.
This assumes that those coding or utilizing technical skills are doing so without critical thinking. Perhaps this is true some of the time, but I don't see the situation being any different in academia.
> Having a proper, critical debate takes time and experience.
What constitutes a "proper, critical debate" is subjective. And from my position, it would appear that the advent of the internet and freedom of information is making the current academic model unsustainable and obsolete. And so academia has an entrenched interest in defining "proper, critical debate" in a way that protects their livelihood.
I'm not saying there isn't plenty of poorly written science and analysis being done on blogs out there. I'm simply saying I don't think traditional academia and science is much different.
Self motivation is much harder than you think, and a little nudge from a bootcamp may be what most people need before they become 'self motivated'.