4 ms·

It's possible that all the data scientists you know are not a representative sample. I've met smart data scientists and engineers who've gone to a bootcamp. Pe

by ahuth 7y ago

It's possible that all the data scientists you know are not a representative sample. 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.

AndrewUnmuted 7y ago
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.
CaptArmchair 7y ago
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 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.
- freepor 7y ago
  What Tomas Pueyo did is create a massively viral piece of content that, regardless of accuracy, encouraged the right actions. In doing so he saved at least thousands of lives. So forgive me for not caring how robust his analyses are.
  - CaptArmchair 7y ago
    Saving lives doesn't absolve anyone from critical consideration about what they are publishing exactly. 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.
- bart_spoon 7y ago
  Well as a statistician, I hate to break it to you, but there is a massive reproduction crisis in science because heretofore the "rigourous research practices" has often times been more a veneer of rigor rather than actual science. The amount of published research (often in esteemed journals) that has been found to be unreproducible and based on faulty methods is aburdly high. The modern scientific method has arguably been as or more effective at building a tower that shields scientists and academics from criticism from work of questionable validity. 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.
  - CaptArmchair 7y ago

by ahuth 7y ago

AndrewUnmuted 7y ago

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.

CaptArmchair 7y ago

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 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.

freepor 7y ago
What Tomas Pueyo did is create a massively viral piece of content that, regardless of accuracy, encouraged the right actions. In doing so he saved at least thousands of lives. So forgive me for not caring how robust his analyses are.
- CaptArmchair 7y ago
  Saving lives doesn't absolve anyone from critical consideration about what they are publishing exactly. 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.
bart_spoon 7y ago
Well as a statistician, I hate to break it to you, but there is a massive reproduction crisis in science because heretofore the "rigourous research practices" has often times been more a veneer of rigor rather than actual science. The amount of published research (often in esteemed journals) that has been found to be unreproducible and based on faulty methods is aburdly high. The modern scientific method has arguably been as or more effective at building a tower that shields scientists and academics from criticism from work of questionable validity. 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.
- CaptArmchair 7y ago