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BS in Statistics? If you don't have one you are very likely not a data SCIENTIST. You will be data guy. I know that some meteorologist have used normality in f

BS in Statistics? If you don't have one you are very likely not a data SCIENTIST. You will be data guy. I know that some meteorologist have used normality in forcasting. This is an example of why you cannot become a data SCIENTIST. Another example; applying regression to your data. If you think that regression is as simple as its formula then you need at least 4 years to understand what I mean.

ibab 10y ago
Why should not having a BS in statistics prevent anyone from properly learning and applying statistics in a rigorous scientific fashion? There are a lot of people with a rigorous mathematical background (mathematicians, physicists, biologists, computer scientists, ...) who are perfectly capable of understanding and applying stats concepts at a high level. In addition, these people have a lot of experience with doing scientific research, so shouldn't they be even more qualified to call themselves "data scientists"? Can you give an example of something that clearly distinguishes a "data scientist" from say a physicist who learned regression from a stats textbook?
- markovbling 10y ago
  there are a lot of gotchas that don't seem like errors but completely invalidate analysis when done without a thorough understanding of a technique. For example you can learn regression from a stats textbook but unless you've gone through a thorough (and painful) graduate-level stats course, you probably haven't seen the edge cases that invalidate assumptions and necessitate a more complex regression e.g. your regression may suggest there is no effect but when you look at the residuals, you may find systematic bias that you can model using a subject-specific random effect or some transformation as a generalized linear model... That isn't to say you need a graduate level stats degree but applying statistics without understanding the pitfalls can lead to seriously wrong conclusions.
  - ibab 10y ago
    That's a good point, but I suspect that a lot of serious gotchas that a data scientist might encounter in the wild are not taught as part of a graduate-level statistics course. Being able to think critically and quickly adapting to the problem at hand might end up being more important than previous experience in stats (which is still very valuable, of course).
taeric 10y ago
Does the word scientist mean something different if it is in all caps? I mean, I get it. You would like it for the word to remain some pure version of meaning that it actually never had. Similar to getting upset that people using the word literally in a very figurative sense. The relevant question on this style of article is not about word smithing. That said, I'm in a field where we often through the title engineer on people, but we don't know why.
- WhitneyLand 10y ago
  Get your point, but actually "literally" did actually used to have a different meaning. Only recently have dictionaries accepted the nonliteral use as a form of emphasis. In fact Cambridge still disallows it for formal speech.
  - taeric 10y ago
    Define recently. :) http://www.merriam-webster.com/words-at-play/misuse-of-literally http://www.merriam-webster.com/words-at-play/misuse-of-liter... Now... I deserve to be literally roasted for some of the other mistakes above. I should know throw versus through... Not even pronounced the same...
- Obi_Juan_Kenobi 10y ago
  I don't really want to weigh in on a semantic argument, but I was considering what would be a good definition for a 'scientist' vocation. To me, a scientist is someone who engages in research with generalizable results. That would exclude someone who does experiments and analysis, but only applies established methods to a particular problem. Call them an analyst, perhaps, but science is an ongoing dialog that they are not participating in. I think that's part of why many consider it really presumptuous for 'data scientists' to call themselves such. Some of them are certainly developing new methods and engaging in a kind of dialog that is definitely science. Others are addressing business needs with a new sort of analysis, drawing on the field but not giving back to it. There is absolutely nothing wrong with that, but it does seem like that pursuit is different enough to bother calling it something else.
tomrod 10y ago
I appreciate that you hedged by saying "are very likely not" instead of just saying "not". See, here's the deal. Numerical literacy (numeracy) comes about through many processes, one of which is obtaining a statistics degree. However, not many fresh statisticians could tell you or me the expected runtime of a taking a numerical field's median on a large database. Why? Because the backgrounds are different. Yet, for a solid data science team, you'd like at least someone who can give insight as to whether your project will take minutes or decades to complete. This (contrived but important) example suggests that while a BS in statistics is an important and necessary component of a data science team, it not sufficient.
- moseandre 10y ago
  It was a statistics class where I learned a linear time median algorithm. But that was pretty lucky, I admit. 95% of undergraduate statistics education is focused on formal inference. Data science, in my experience, involves a lot more exploratory data analysis [1] than formal inference (frequentist or Bayesian). The extreme focus on inference and the hypothesis testing step in the scientific method is something people with a formal statistics education have to overcome to be productive data scientists. Or applied statisticians, really! It is more important to understand the data, organize it creatively, and find unexpected structure. [1] https://en.wikipedia.org/wiki/Exploratory_data_analysis https://en.wikipedia.org/wiki/Exploratory_data_analysis
deleted 10y ago
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Fiahil 10y ago
Saying you can't be a data scientist without a Bachelor degree in statistics is like saying you can't be a software engineer without a Master's degree in software engineering. Your educational system is not really a good environment for learning, so I'm pretty sure someone can become a data scientist with a French "License d'Histoire Moderne" and a lot of work.
- darkhorn 10y ago
  Oh yeah, there was a miss calculation of a financial information and I guess because of this stocks have fell. Why? Because some smart guys used Excel. Or, then I can become a doctor right? By studying hard at home? You should not employ a self thought software engineer or statistician or a doctor in critical positions such as health, finance, engineering, and science. Some might be okay but 99.9% of them will be incompetent for the work.
MichaelGG 10y ago
Being a scientist is a mindset and someone that follows a certain approach. Existing institutions do not have a monopoly on who is a scientist.

ibab 10y ago

Why should not having a BS in statistics prevent anyone from properly learning and applying statistics in a rigorous scientific fashion? There are a lot of people with a rigorous mathematical background (mathematicians, physicists, biologists, computer scientists, ...) who are perfectly capable of understanding and applying stats concepts at a high level. In addition, these people have a lot of experience with doing scientific research, so shouldn't they be even more qualified to call themselves "data scientists"? Can you give an example of something that clearly distinguishes a "data scientist" from say a physicist who learned regression from a stats textbook?

markovbling 10y ago
there are a lot of gotchas that don't seem like errors but completely invalidate analysis when done without a thorough understanding of a technique. For example you can learn regression from a stats textbook but unless you've gone through a thorough (and painful) graduate-level stats course, you probably haven't seen the edge cases that invalidate assumptions and necessitate a more complex regression e.g. your regression may suggest there is no effect but when you look at the residuals, you may find systematic bias that you can model using a subject-specific random effect or some transformation as a generalized linear model... That isn't to say you need a graduate level stats degree but applying statistics without understanding the pitfalls can lead to seriously wrong conclusions.
- ibab 10y ago
  That's a good point, but I suspect that a lot of serious gotchas that a data scientist might encounter in the wild are not taught as part of a graduate-level statistics course. Being able to think critically and quickly adapting to the problem at hand might end up being more important than previous experience in stats (which is still very valuable, of course).

taeric 10y ago

Does the word scientist mean something different if it is in all caps? I mean, I get it. You would like it for the word to remain some pure version of meaning that it actually never had. Similar to getting upset that people using the word literally in a very figurative sense. The relevant question on this style of article is not about word smithing. That said, I'm in a field where we often through the title engineer on people, but we don't know why.

WhitneyLand 10y ago
Get your point, but actually "literally" did actually used to have a different meaning. Only recently have dictionaries accepted the nonliteral use as a form of emphasis. In fact Cambridge still disallows it for formal speech.
- taeric 10y ago
  Define recently. :) http://www.merriam-webster.com/words-at-play/misuse-of-literally http://www.merriam-webster.com/words-at-play/misuse-of-liter... Now... I deserve to be literally roasted for some of the other mistakes above. I should know throw versus through... Not even pronounced the same...
Obi_Juan_Kenobi 10y ago
I don't really want to weigh in on a semantic argument, but I was considering what would be a good definition for a 'scientist' vocation. To me, a scientist is someone who engages in research with generalizable results. That would exclude someone who does experiments and analysis, but only applies established methods to a particular problem. Call them an analyst, perhaps, but science is an ongoing dialog that they are not participating in. I think that's part of why many consider it really presumptuous for 'data scientists' to call themselves such. Some of them are certainly developing new methods and engaging in a kind of dialog that is definitely science. Others are addressing business needs with a new sort of analysis, drawing on the field but not giving back to it. There is absolutely nothing wrong with that, but it does seem like that pursuit is different enough to bother calling it something else.

tomrod 10y ago

I appreciate that you hedged by saying "are very likely not" instead of just saying "not". See, here's the deal. Numerical literacy (numeracy) comes about through many processes, one of which is obtaining a statistics degree. However, not many fresh statisticians could tell you or me the expected runtime of a taking a numerical field's median on a large database. Why? Because the backgrounds are different. Yet, for a solid data science team, you'd like at least someone who can give insight as to whether your project will take minutes or decades to complete. This (contrived but important) example suggests that while a BS in statistics is an important and necessary component of a data science team, it not sufficient.

moseandre 10y ago
It was a statistics class where I learned a linear time median algorithm. But that was pretty lucky, I admit. 95% of undergraduate statistics education is focused on formal inference. Data science, in my experience, involves a lot more exploratory data analysis [1] than formal inference (frequentist or Bayesian). The extreme focus on inference and the hypothesis testing step in the scientific method is something people with a formal statistics education have to overcome to be productive data scientists. Or applied statisticians, really! It is more important to understand the data, organize it creatively, and find unexpected structure. [1] https://en.wikipedia.org/wiki/Exploratory_data_analysis https://en.wikipedia.org/wiki/Exploratory_data_analysis

deleted 10y ago

[deleted]

Fiahil 10y ago

Saying you can't be a data scientist without a Bachelor degree in statistics is like saying you can't be a software engineer without a Master's degree in software engineering. Your educational system is not really a good environment for learning, so I'm pretty sure someone can become a data scientist with a French "License d'Histoire Moderne" and a lot of work.

darkhorn 10y ago
Oh yeah, there was a miss calculation of a financial information and I guess because of this stocks have fell. Why? Because some smart guys used Excel. Or, then I can become a doctor right? By studying hard at home? You should not employ a self thought software engineer or statistician or a doctor in critical positions such as health, finance, engineering, and science. Some might be okay but 99.9% of them will be incompetent for the work.

MichaelGG 10y ago

Being a scientist is a mindset and someone that follows a certain approach. Existing institutions do not have a monopoly on who is a scientist.