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.
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?
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.
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.
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...
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.
More recently, a lot of scientists worked to figure out atomic energy. Most, in likelihood, we do not know the names of anymore.
I think of it as musicians. If you define musician to be rockstar, there are not nearly as many as if you include school teachers, symphony players, conductors, etc. Yet, for most people, this later set would definitely be considered a musician.
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.
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.
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.
Existing institutions do not have a monopoly on who is a scientist.